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@@ -59,62 +59,80 @@ df_data = []
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# %%
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-# 主要的类
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class CMSAnalyst:
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def __init__(self, fmin, fmax, table_name, ids):
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- self.table_name =table_name
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+ """
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+ table_name: 当前代码实际传入的是 windcode（例如 SKF001），内部会拼 _wave
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+ ids: [id1, id2, ...]
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+ """
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+ self.table_name = table_name
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self.ids = ids
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- # envelope_spectrum_analysis
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- # datas是[df1,df2,.....]
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- #从数据库获取原始数据
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- self.datas = self._get_by_id(table_name,ids)
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+
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+ # 1) 从数据库获取原始数据（按 id 分组）
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+ self.datas = self._get_by_id(table_name, ids)
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+ if not self.datas:
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+ raise ValueError(f"[ERROR] 未查到任何数据 ids={ids}")
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+
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+ # 2) 只保留需要字段
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self.datas = [
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- df[['id', 'mesure_data', 'time_stamp', 'sampling_frequency',
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+ df[['id', 'mesure_data', 'time_stamp', 'sampling_frequency',
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'wind_turbine_number', 'rotational_speed', 'mesure_point_name']]
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- for df in self.datas
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- ]
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+ for df in self.datas if df is not None and not df.empty
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+ ]
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+ if not self.datas:
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+ raise ValueError("[ERROR] 分组后 DataFrame 全为空")
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- # 只输入一个id，返回一个[df],所以拿到self.data[0]
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+ # 3) 单 id 情况：取第一个 df
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self.data_filter = self.datas[0]
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- # print("mesure_data sample:", self.data_filter['mesure_data'].iloc[0]) # 打印第一条数据
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- self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'].iloc[0]))
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- # print(self.data_filter)
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- # 取数据列
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- self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'][0]))
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+ if self.data_filter.empty:
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+ raise ValueError("[ERROR] data_filter 为空，无法读取数据")
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+
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+ # 4) 解析 mesure_data
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+ raw_md = self.data_filter['mesure_data'].iloc[0]
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+ self.data = self._parse_mesure_data(raw_md)
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+ if self.data is None or len(self.data) == 0:
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+ raise ValueError("[ERROR] mesure_data 解析失败或为空")
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+
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+ self.data = np.asarray(self.data, dtype=float)
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+
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+ # 5) 采样频率
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+ self.fs = int(self.data_filter['sampling_frequency'].iloc[0])
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+
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+ # 6) 分析参数
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self.envelope_spectrum_m = self.data.shape[0]
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self.envelope_spectrum_n = 1
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- #设置分析参数
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- self.fs = int(self.data_filter['sampling_frequency'].iloc[0])
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self.envelope_spectrum_t = np.arange(self.envelope_spectrum_m) / self.fs
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- self.fmin = fmin if fmin is not None else 0
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- self.fmax = fmax if fmax is not None else float('inf')
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- self.envelope_spectrum_y = self._bandpass_filter(self.data)
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- self.f, self.HP = self._calculate_envelope_spectrum(self.envelope_spectrum_y)
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- #设备信息
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+ self.fmin = fmin if fmin is not None else 0
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+ self.fmax = fmax if fmax is not None else float('inf')
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+
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+ # 7) 设备信息
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self.wind_code = self.data_filter['wind_turbine_number'].iloc[0]
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self.rpm_Gen = self.data_filter['rotational_speed'].iloc[0]
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self.mesure_point_name = self.data_filter['mesure_point_name'].iloc[0]
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- self.fn_Gen = round(self.rpm_Gen/60,2)
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-
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- self.CF = self.Characteristic_Frequency()
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- print(self.CF)
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- self.CF = pd.DataFrame(self.CF,index=[0])
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- print(self.CF)
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- print(self.rpm_Gen)
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- #if self.CF['type'].iloc[0] == 'bearing':
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+ self.fn_Gen = round(self.rpm_Gen / 60, 2)
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+
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+ # 8) 包络谱预计算
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+ self.envelope_spectrum_y = self._bandpass_filter(self.data)
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+ self.f, self.HP = self._calculate_envelope_spectrum(self.envelope_spectrum_y)
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+
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+ # 9) 特征频率 & 轴承频率
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+ cf_dict = self.Characteristic_Frequency()
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+ self.CF = pd.DataFrame([cf_dict])
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+
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n_rolls_m = self.CF['n_rolls'].iloc[0]
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d_rolls_m = self.CF['d_rolls'].iloc[0]
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D_diameter_m = self.CF['D_diameter'].iloc[0]
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theta_deg_m = self.CF['theta_deg'].iloc[0]
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- print(n_rolls_m)
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- print(d_rolls_m)
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- print(D_diameter_m)
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- print(theta_deg_m)
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- self.bearing_frequencies = self.calculate_bearing_frequencies(n_rolls_m, d_rolls_m, D_diameter_m, theta_deg_m, self.rpm_Gen)
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- print(self.bearing_frequencies)
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- self.bearing_frequencies = pd.DataFrame(self.bearing_frequencies,index=[0])
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- print(self.bearing_frequencies)
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- # frequency_domain_analysis
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+
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+ if any(v is None for v in [n_rolls_m, d_rolls_m, D_diameter_m, theta_deg_m]):
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+ self.bearing_frequencies = None
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+ else:
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+ self.bearing_frequencies = self.calculate_bearing_frequencies(
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+ n_rolls_m, d_rolls_m, D_diameter_m, theta_deg_m, self.rpm_Gen
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+ )
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+ self.bearing_frequencies = pd.DataFrame([self.bearing_frequencies])
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+
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+ # 10) 频谱预计算
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(
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self.frequency_domain_analysis_t,
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self.frequency_domain_analysis_f,
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@@ -123,301 +141,288 @@ class CMSAnalyst:
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self.frequency_domain_analysis_Xrms,
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) = self._calculate_spectrum(self.data)
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- # time_domain_analysis
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+ # 11) 时域时间轴
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self.time_domain_analysis_t = np.arange(self.data.shape[0]) / self.fs
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-
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- # def _get_by_id(self, windcode, ids):
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- # df_res = []
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- # #engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
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- # engine = create_engine('mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod')
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- # for id in ids:
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- # table_name=windcode+'_wave'
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- # lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
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- # # print(lastday_df_sql)
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- # df = pd.read_sql(lastday_df_sql, engine)
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- # df_res.append(df)
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- # return df_res
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-
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- # def _get_by_id(self, windcode, ids):
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- # #engine = create_engine('mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod')
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- # engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
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- # table_name = windcode + '_wave'
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- # ids_str = ','.join(map(str, ids))
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- # sql = f"SELECT * FROM {table_name} WHERE id IN ({ids_str}) ORDER BY time_stamp"
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- # df = pd.read_sql(sql, engine)
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-
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- # # 按ID分组返回
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- # grouped = [group for _, group in df.groupby('id')]
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- # return grouped
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+
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+ # ==========================================================
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+ # 工具：解析 mesure_data（兼容 json 字符串 / python list 字符串 / list）
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+ # ==========================================================
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+ def _parse_mesure_data(self, raw):
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+ if raw is None:
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+ return None
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+
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+ # 已经是 list/np.array
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+ if isinstance(raw, (list, tuple, np.ndarray)):
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+ return list(raw)
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+
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+ # 字符串：可能是 JSON 或 python list 字符串
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+ if isinstance(raw, str):
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+ s = raw.strip()
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+ # 优先 json.loads（如果是标准 JSON）
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+ try:
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+ v = json.loads(s)
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+ if isinstance(v, list):
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+ return v
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+ except Exception:
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+ pass
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+
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+ # 再尝试 ast.literal_eval（如果是 python 格式 list）
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+ try:
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+ v = ast.literal_eval(s)
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+ if isinstance(v, (list, tuple, np.ndarray)):
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+ return list(v)
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+ except Exception:
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+ return None
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+
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+ return None
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+
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+ # ==========================================================
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+ # DB：按 id 拉取波形
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+ # ==========================================================
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def _get_by_id(self, windcode, ids):
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engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data_prod')
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- table_name = windcode + '_wave'
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+ table_name = f"{windcode}_wave"
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ids_str = ','.join(map(str, ids))
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sql = f"SELECT * FROM {table_name} WHERE id IN ({ids_str}) ORDER BY time_stamp"
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- print("Executing SQL:", sql) # 打印 SQL
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df = pd.read_sql(sql, engine)
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- print("Returned DataFrame shape:", df.shape) # 检查返回的数据量
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+ if df.empty:
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+ return []
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grouped = [group for _, group in df.groupby('id')]
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return grouped
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- # envelope_spectrum_analysis 包络谱分析
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+ # ==========================================================
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+ # 包络谱：带通滤波（FFT 截频）
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+ # ==========================================================
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def _bandpass_filter(self, data):
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- """带通滤波"""
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-
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- m= data.shape[0]
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- ni = round(self.fmin * self.envelope_spectrum_m / self.fs + 1)
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- # na = round(self.fmax * self.envelope_spectrum_m / self.fs + 1)
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+ m = data.shape[0]
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+
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+ # index 保护：fmin/fmax 可能超范围
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+ ni = int(round(self.fmin * m / self.fs + 1))
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+ ni = max(0, min(ni, m))
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+
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if self.fmax == float('inf'):
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na = m
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else:
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- na = round(self.fmax * m / self.fs + 1)
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- col = 1
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- y = np.zeros((self.envelope_spectrum_m, col))
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- # for p in range(col):
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- # print(data.shape,p)
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+ na = int(round(self.fmax * m / self.fs + 1))
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+ na = max(0, min(na, m))
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+
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+ if na <= ni:
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+ # 退化情况：不做滤波
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+ y = np.zeros((m, 1))
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+ y[:, 0] = data
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+ return y
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+
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z = np.fft.fft(data)
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- a = np.zeros(self.envelope_spectrum_m, dtype=complex)
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+ a = np.zeros(m, dtype=complex)
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+
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a[ni:na] = z[ni:na]
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- a[self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1] = z[
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- self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1
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- ]
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- z = np.fft.ifft(a)
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- y[:, 0] = np.real(z)
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+ # 对称频段
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+ a[m - na + 1: m - ni + 1] = z[m - na + 1: m - ni + 1]
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+ x_ifft = np.fft.ifft(a)
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+ y = np.zeros((m, 1))
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+ y[:, 0] = np.real(x_ifft)
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return y
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def _calculate_envelope_spectrum(self, y):
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- """计算包络谱"""
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m, n = y.shape
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HP = np.zeros((m, n))
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- col = 1
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- for p in range(col):
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+ for p in range(n):
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H = np.abs(hilbert(y[:, p] - np.mean(y[:, p])))
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HP[:, p] = np.abs(np.fft.fft(H - np.mean(H))) * 2 / m
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f = np.fft.fftfreq(m, d=1 / self.fs)
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return f, HP
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-
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def envelope_spectrum(self):
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- """绘制包络谱"""
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- # 只取正频率部分
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positive_frequencies = self.f[: self.envelope_spectrum_m // 2]
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positive_HP = self.HP[: self.envelope_spectrum_m // 2, 0]
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x = positive_frequencies
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y = positive_HP
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- title = "包络谱"
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- xaxis = "频率(Hz)"
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- yaxis = "加速度(m/s^2)"
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- Xrms = np.sqrt(np.mean(y**2)) # 加速度均方根值（有效值）
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- rpm_Gen = round(self.rpm_Gen, 2)
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- BPFI_1X = round(self.bearing_frequencies['BPFI'].iloc[0], 2)
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- BPFO_1X = round(self.bearing_frequencies['BPFO'].iloc[0], 2)
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- BSF_1X = round(self.bearing_frequencies['BSF'].iloc[0], 2)
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- FTF_1X = round(self.bearing_frequencies['FTF'].iloc[0], 2)
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- fn_Gen = round(self.fn_Gen, 2)
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- _3P_1X = round(self.fn_Gen, 2) * 3
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-
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- # if self.CF['type'].iloc[0] == 'bearing':
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- result = {
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- "fs":self.fs,
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- "Xrms":round(Xrms, 2),
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- "x":list(x),
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- "y":list(y),
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- "title":title,
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- "xaxis":xaxis,
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- "yaxis":yaxis,
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- "rpm_Gen": round(rpm_Gen, 2), # 转速r/min
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- "BPFI": [{"Xaxis": BPFI_1X ,"val": "1BPFI"},{"Xaxis": BPFI_1X*2 ,"val": "2BPFI"},
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- {"Xaxis": BPFI_1X*3, "val": "3BPFI"}, {"Xaxis": BPFI_1X*4, "val": "4BPFI"},
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- {"Xaxis": BPFI_1X*5, "val": "5BPFI"}, {"Xaxis": BPFI_1X*6, "val": "6BPFI"}],
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- "BPFO": [{"Xaxis": BPFO_1X ,"val": "1BPFO"},{"Xaxis": BPFO_1X*2 ,"val": "2BPFO"},
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- {"Xaxis": BPFO_1X*3, "val": "3BPFO"}, {"Xaxis": BPFO_1X*4, "val": "4BPFO"},
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- {"Xaxis": BPFO_1X*5, "val": "5BPFO"}, {"Xaxis": BPFO_1X*6, "val": "6BPFO"}],
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- "BSF": [{"Xaxis": BSF_1X ,"val": "1BSF"},{"Xaxis": BSF_1X*2 ,"val": "2BSF"},
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- {"Xaxis": BSF_1X*3, "val": "3BSF"}, {"Xaxis": BSF_1X*4, "val": "4BSF"},
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- {"Xaxis": BSF_1X*5, "val": "5BSF"}, {"Xaxis": BSF_1X*6, "val": "6BSF"}],
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- "FTF": [{"Xaxis": FTF_1X ,"val": "1FTF"},{"Xaxis": FTF_1X*2 ,"val": "2FTF"},
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- {"Xaxis": FTF_1X*3, "val": "3FTF"}, {"Xaxis": FTF_1X*4, "val": "4FTF"},
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- {"Xaxis": FTF_1X*5, "val": "5FTF"}, {"Xaxis": FTF_1X*6, "val": "6FTF"}],
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- "fn_Gen":[{"Xaxis": fn_Gen ,"val": "1X"},{"Xaxis": fn_Gen*2 ,"val": "2X"},
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- {"Xaxis": fn_Gen*3, "val": "3X"}, {"Xaxis": fn_Gen*4, "val": "4X"},
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- {"Xaxis": fn_Gen*5, "val": "5X"}, {"Xaxis": fn_Gen*6, "val": "6X"}],
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- "B3P":_3P_1X,
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- }
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- # result = json.dumps(result, ensure_ascii=False)
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- result = self.replace_nan(result)
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- return result
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+ Xrms = float(np.sqrt(np.mean(y ** 2)))
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+ rpm_Gen = round(float(self.rpm_Gen), 2)
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+ if self.bearing_frequencies is None:
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+ BPFI_1X = BPFO_1X = BSF_1X = FTF_1X = None
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+ else:
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+ BPFI_1X = round(float(self.bearing_frequencies['BPFI'].iloc[0]), 2)
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+ BPFO_1X = round(float(self.bearing_frequencies['BPFO'].iloc[0]), 2)
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+ BSF_1X = round(float(self.bearing_frequencies['BSF'].iloc[0]), 2)
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+ FTF_1X = round(float(self.bearing_frequencies['FTF'].iloc[0]), 2)
|
|
|
+
|
|
|
+ fn_Gen = round(float(self.fn_Gen), 2)
|
|
|
+ _3P_1X = fn_Gen * 3
|
|
|
|
|
|
- # frequency_domain_analysis 频谱分析
|
|
|
+ result = {
|
|
|
+ "fs": int(self.fs),
|
|
|
+ "Xrms": round(Xrms, 2),
|
|
|
+ "x": list(x),
|
|
|
+ "y": list(y),
|
|
|
+ "title": "包络谱",
|
|
|
+ "xaxis": "频率(Hz)",
|
|
|
+ "yaxis": "加速度(m/s^2)",
|
|
|
+ "rpm_Gen": rpm_Gen,
|
|
|
+ "BPFI": [{"Xaxis": (None if BPFI_1X is None else BPFI_1X * k), "val": f"{k}BPFI"} for k in range(1, 7)],
|
|
|
+ "BPFO": [{"Xaxis": (None if BPFO_1X is None else BPFO_1X * k), "val": f"{k}BPFO"} for k in range(1, 7)],
|
|
|
+ "BSF": [{"Xaxis": (None if BSF_1X is None else BSF_1X * k), "val": f"{k}BSF"} for k in range(1, 7)],
|
|
|
+ "FTF": [{"Xaxis": (None if FTF_1X is None else FTF_1X * k), "val": f"{k}FTF"} for k in range(1, 7)],
|
|
|
+ "fn_Gen": [{"Xaxis": fn_Gen * k, "val": f"{k}X"} for k in range(1, 7)],
|
|
|
+ "B3P": _3P_1X,
|
|
|
+ }
|
|
|
|
|
|
+ return self.replace_nan(result)
|
|
|
+
|
|
|
+ # ==========================================================
|
|
|
+ # 频谱
|
|
|
+ # ==========================================================
|
|
|
def _calculate_spectrum(self, data):
|
|
|
- """计算频谱"""
|
|
|
m = data.shape[0]
|
|
|
- n = 1
|
|
|
t = np.arange(m) / self.fs
|
|
|
- mag = np.zeros((m, n))
|
|
|
- Xrms = np.sqrt(np.mean(data**2)) # 加速度均方根值（有效值）
|
|
|
- # col=1
|
|
|
- # for p in range(col):
|
|
|
mag = np.abs(np.fft.fft(data - np.mean(data))) * 2 / m
|
|
|
f = np.fft.fftfreq(m, d=1 / self.fs)
|
|
|
+ Xrms = float(np.sqrt(np.mean(data ** 2)))
|
|
|
return t, f, m, mag, Xrms
|
|
|
|
|
|
def frequency_domain(self):
|
|
|
- """绘制频域波形参数"""
|
|
|
- # 只取正频率部分
|
|
|
- positive_frequencies = self.frequency_domain_analysis_f[
|
|
|
- : self.frequency_domain_analysis_m // 2
|
|
|
- ]
|
|
|
- positive_mag = self.frequency_domain_analysis_mag[
|
|
|
- : self.frequency_domain_analysis_m // 2
|
|
|
- ]
|
|
|
+ positive_frequencies = self.frequency_domain_analysis_f[: self.frequency_domain_analysis_m // 2]
|
|
|
+ positive_mag = self.frequency_domain_analysis_mag[: self.frequency_domain_analysis_m // 2]
|
|
|
|
|
|
x = positive_frequencies
|
|
|
y = positive_mag
|
|
|
- title = "频域信号"
|
|
|
- xaxis = "频率(Hz)"
|
|
|
- yaxis = "加速度(m/s^2)"
|
|
|
- Xrms = self.frequency_domain_analysis_Xrms
|
|
|
-
|
|
|
- rpm_Gen = round(self.rpm_Gen, 2)
|
|
|
- BPFI_1X = round(self.bearing_frequencies['BPFI'].iloc[0], 2)
|
|
|
- BPFO_1X = round(self.bearing_frequencies['BPFO'].iloc[0], 2)
|
|
|
- BSF_1X = round(self.bearing_frequencies['BSF'].iloc[0], 2)
|
|
|
- FTF_1X = round(self.bearing_frequencies['FTF'].iloc[0], 2)
|
|
|
- fn_Gen = round(self.fn_Gen, 2)
|
|
|
- _3P_1X = round(self.fn_Gen, 2) * 3
|
|
|
-
|
|
|
- # if self.CF['type'].iloc[0] == 'bearing':
|
|
|
+
|
|
|
+ Xrms = float(self.frequency_domain_analysis_Xrms)
|
|
|
+ rpm_Gen = round(float(self.rpm_Gen), 2)
|
|
|
+
|
|
|
+ if self.bearing_frequencies is None:
|
|
|
+ BPFI_1X = BPFO_1X = BSF_1X = FTF_1X = None
|
|
|
+ else:
|
|
|
+ BPFI_1X = round(float(self.bearing_frequencies['BPFI'].iloc[0]), 2)
|
|
|
+ BPFO_1X = round(float(self.bearing_frequencies['BPFO'].iloc[0]), 2)
|
|
|
+ BSF_1X = round(float(self.bearing_frequencies['BSF'].iloc[0]), 2)
|
|
|
+ FTF_1X = round(float(self.bearing_frequencies['FTF'].iloc[0]), 2)
|
|
|
+
|
|
|
+ fn_Gen = round(float(self.fn_Gen), 2)
|
|
|
+ _3P_1X = fn_Gen * 3
|
|
|
+
|
|
|
result = {
|
|
|
- "fs":self.fs,
|
|
|
- "Xrms":round(Xrms, 2),
|
|
|
- "x":list(x),
|
|
|
- "y":list(y),
|
|
|
- "title":title,
|
|
|
- "xaxis":xaxis,
|
|
|
- "yaxis":yaxis,
|
|
|
- "rpm_Gen": round(rpm_Gen, 2), # 转速r/min
|
|
|
- "BPFI": [{"Xaxis": BPFI_1X ,"val": "1BPFI"},{"Xaxis": BPFI_1X*2 ,"val": "2BPFI"},
|
|
|
- {"Xaxis": BPFI_1X*3, "val": "3BPFI"}, {"Xaxis": BPFI_1X*4, "val": "4BPFI"},
|
|
|
- {"Xaxis": BPFI_1X*5, "val": "5BPFI"}, {"Xaxis": BPFI_1X*6, "val": "6BPFI"}],
|
|
|
- "BPFO": [{"Xaxis": BPFO_1X ,"val": "1BPFO"},{"Xaxis": BPFO_1X*2 ,"val": "2BPFO"},
|
|
|
- {"Xaxis": BPFO_1X*3, "val": "3BPFO"}, {"Xaxis": BPFO_1X*4, "val": "4BPFO"},
|
|
|
- {"Xaxis": BPFO_1X*5, "val": "5BPFO"}, {"Xaxis": BPFO_1X*6, "val": "6BPFO"}],
|
|
|
- "BSF": [{"Xaxis": BSF_1X ,"val": "1BSF"},{"Xaxis": BSF_1X*2 ,"val": "2BSF"},
|
|
|
- {"Xaxis": BSF_1X*3, "val": "3BSF"}, {"Xaxis": BSF_1X*4, "val": "4BSF"},
|
|
|
- {"Xaxis": BSF_1X*5, "val": "5BSF"}, {"Xaxis": BSF_1X*6, "val": "6BSF"}],
|
|
|
- "FTF": [{"Xaxis": FTF_1X ,"val": "1FTF"},{"Xaxis": FTF_1X*2 ,"val": "2FTF"},
|
|
|
- {"Xaxis": FTF_1X*3, "val": "3FTF"}, {"Xaxis": FTF_1X*4, "val": "4FTF"},
|
|
|
- {"Xaxis": FTF_1X*5, "val": "5FTF"}, {"Xaxis": FTF_1X*6, "val": "6FTF"}],
|
|
|
- "fn_Gen":[{"Xaxis": fn_Gen ,"val": "1X"},{"Xaxis": fn_Gen*2 ,"val": "2X"},
|
|
|
- {"Xaxis": fn_Gen*3, "val": "3X"}, {"Xaxis": fn_Gen*4, "val": "4X"},
|
|
|
- {"Xaxis": fn_Gen*5, "val": "5X"}, {"Xaxis": fn_Gen*6, "val": "6X"}],
|
|
|
- "B3P":_3P_1X,
|
|
|
+ "fs": int(self.fs),
|
|
|
+ "Xrms": round(Xrms, 2),
|
|
|
+ "x": list(x),
|
|
|
+ "y": list(y),
|
|
|
+ "title": "频域信号",
|
|
|
+ "xaxis": "频率(Hz)",
|
|
|
+ "yaxis": "加速度(m/s^2)",
|
|
|
+ "rpm_Gen": rpm_Gen,
|
|
|
+ "BPFI": [{"Xaxis": (None if BPFI_1X is None else BPFI_1X * k), "val": f"{k}BPFI"} for k in range(1, 7)],
|
|
|
+ "BPFO": [{"Xaxis": (None if BPFO_1X is None else BPFO_1X * k), "val": f"{k}BPFO"} for k in range(1, 7)],
|
|
|
+ "BSF": [{"Xaxis": (None if BSF_1X is None else BSF_1X * k), "val": f"{k}BSF"} for k in range(1, 7)],
|
|
|
+ "FTF": [{"Xaxis": (None if FTF_1X is None else FTF_1X * k), "val": f"{k}FTF"} for k in range(1, 7)],
|
|
|
+ "fn_Gen": [{"Xaxis": fn_Gen * k, "val": f"{k}X"} for k in range(1, 7)],
|
|
|
+ "B3P": _3P_1X,
|
|
|
}
|
|
|
- result = self.replace_nan(result)
|
|
|
- result = json.dumps(result, ensure_ascii=False)
|
|
|
- return result
|
|
|
|
|
|
- # time_domain_analysis 时域分析
|
|
|
+ result = self.replace_nan(result)
|
|
|
+ return json.dumps(result, ensure_ascii=False)
|
|
|
+
|
|
|
+ # ==========================================================
|
|
|
+ # 时域
|
|
|
+ # ==========================================================
|
|
|
def time_domain(self):
|
|
|
- """绘制时域波形参数"""
|
|
|
x = self.time_domain_analysis_t
|
|
|
y = self.data
|
|
|
- rpm_Gen =self.rpm_Gen
|
|
|
- title = "时间域信号"
|
|
|
- xaxis = "时间(s)"
|
|
|
- yaxis = "加速度(m/s^2)"
|
|
|
- # 图片右侧统计量
|
|
|
- mean_value = np.mean(y)#平均值
|
|
|
- max_value = np.max(y)#最大值
|
|
|
- min_value = np.min(y)#最小值
|
|
|
- Xrms = np.sqrt(np.mean(y**2)) # 加速度均方根值（有效值）
|
|
|
- Xp = (max_value - min_value) / 2 # 峰值（单峰最大值） # 峰值
|
|
|
- Xpp=max_value-min_value#峰峰值
|
|
|
- Cf = Xp / Xrms # 峰值指标
|
|
|
- Sf = Xrms / mean_value # 波形指标
|
|
|
- If = Xp / np.mean(np.abs(y)) # 脉冲指标
|
|
|
- Xr = np.mean(np.sqrt(np.abs(y))) ** 2 # 方根幅值
|
|
|
- Ce = Xp / Xr # 裕度指标
|
|
|
- # 计算每个数据点的绝对值减去均值后的三次方，并求和
|
|
|
- sum_abs_diff_cubed_3 = np.mean((np.abs(y) - mean_value) ** 3)
|
|
|
- # 计算偏度指标
|
|
|
- Cw = sum_abs_diff_cubed_3 / (Xrms**3)
|
|
|
- # 计算每个数据点的绝对值减去均值后的四次方，并求和
|
|
|
- sum_abs_diff_cubed_4 = np.mean((np.abs(y) - mean_value) ** 4)
|
|
|
- # 计算峭度指标
|
|
|
- Cq = sum_abs_diff_cubed_4 / (Xrms**4)
|
|
|
- result = {
|
|
|
-
|
|
|
- "x":list(x),
|
|
|
- "y":list(y),
|
|
|
- "title":title,
|
|
|
- "xaxis":xaxis,
|
|
|
- "yaxis":yaxis,
|
|
|
- "fs":self.fs,
|
|
|
- "Xrms":round(Xrms, 2),#有效值
|
|
|
- "mean_value":round(mean_value, 2),# 均值
|
|
|
- "max_value":round(max_value, 2),# 最大值
|
|
|
- "min_value":round(min_value, 2), # 最小值
|
|
|
- "Xp":round(Xp, 2),# 峰值
|
|
|
- "Xpp":round(Xpp, 2),# 峰峰值
|
|
|
- "Cf":round(Cf, 2),# 峰值指标
|
|
|
- "Sf":round(Sf, 2),# 波形因子
|
|
|
- "If":round(If, 2),# 脉冲指标
|
|
|
- "Ce":round(Ce, 2),# 裕度指标
|
|
|
- "Cw":round(Cw, 2) ,# 偏度指标
|
|
|
- "Cq":round(Cq, 2) ,# 峭度指标
|
|
|
- "rpm_Gen": round(rpm_Gen, 2), # 转速r/min
|
|
|
|
|
|
+ mean_value = float(np.mean(y))
|
|
|
+ max_value = float(np.max(y))
|
|
|
+ min_value = float(np.min(y))
|
|
|
+ Xrms = float(np.sqrt(np.mean(y ** 2)))
|
|
|
+
|
|
|
+ Xp = float((max_value - min_value) / 2)
|
|
|
+ Xpp = float(max_value - min_value)
|
|
|
+
|
|
|
+ Cf = (Xp / Xrms) if Xrms != 0 else 0.0
|
|
|
+ Sf = (Xrms / mean_value) if mean_value != 0 else 0.0
|
|
|
+ If = (Xp / float(np.mean(np.abs(y)))) if np.mean(np.abs(y)) != 0 else 0.0
|
|
|
+
|
|
|
+ Xr = float(np.mean(np.sqrt(np.abs(y))) ** 2)
|
|
|
+ Ce = (Xp / Xr) if Xr != 0 else 0.0
|
|
|
+
|
|
|
+ # 偏度/峭度
|
|
|
+ Cw = float(np.mean((np.abs(y) - mean_value) ** 3) / (Xrms ** 3)) if Xrms != 0 else 0.0
|
|
|
+ Cq = float(np.mean((np.abs(y) - mean_value) ** 4) / (Xrms ** 4)) if Xrms != 0 else 0.0
|
|
|
+
|
|
|
+ result = {
|
|
|
+ "x": list(x),
|
|
|
+ "y": list(y),
|
|
|
+ "title": "时间域信号",
|
|
|
+ "xaxis": "时间(s)",
|
|
|
+ "yaxis": "加速度(m/s^2)",
|
|
|
+ "fs": int(self.fs),
|
|
|
+ "Xrms": round(Xrms, 2),
|
|
|
+ "mean_value": round(mean_value, 2),
|
|
|
+ "max_value": round(max_value, 2),
|
|
|
+ "min_value": round(min_value, 2),
|
|
|
+ "Xp": round(Xp, 2),
|
|
|
+ "Xpp": round(Xpp, 2),
|
|
|
+ "Cf": round(Cf, 2),
|
|
|
+ "Sf": round(Sf, 2),
|
|
|
+ "If": round(If, 2),
|
|
|
+ "Ce": round(Ce, 2),
|
|
|
+ "Cw": round(Cw, 2),
|
|
|
+ "Cq": round(Cq, 2),
|
|
|
+ "rpm_Gen": round(float(self.rpm_Gen), 2),
|
|
|
}
|
|
|
- result = self.replace_nan(result)
|
|
|
- result = json.dumps(result, ensure_ascii=False)
|
|
|
|
|
|
- return result
|
|
|
+ result = self.replace_nan(result)
|
|
|
+ return json.dumps(result, ensure_ascii=False)
|
|
|
|
|
|
+ # ==========================================================
|
|
|
+ # 趋势分析（按id统计一条数据，取每个id的第一条）
|
|
|
+ # ==========================================================
|
|
|
def trend_analysis(self) -> str:
|
|
|
- """
|
|
|
- 优化后的趋势分析方法（向量化计算统计指标）
|
|
|
- 返回 JSON 字符串，包含所有时间点的统计结果。
|
|
|
- """
|
|
|
- for df in self.datas:
|
|
|
- df['parsed_data'] = df['mesure_data'].apply(json.loads)
|
|
|
- # 1. 合并所有数据并解析 mesure_data
|
|
|
- combined_df = pd.concat(self.datas)
|
|
|
-
|
|
|
-
|
|
|
- combined_df['parsed_data'] = combined_df['mesure_data'].apply(json.loads) # 批量解析 JSON
|
|
|
-
|
|
|
- # 2. 向量化计算统计指标（避免逐行循环）
|
|
|
- def calculate_stats(group: pd.DataFrame) -> Dict[str, Any]:
|
|
|
- data = np.array(group['parsed_data'].iloc[0]) # 提取振动数据数组
|
|
|
- fs = int(group['sampling_frequency'].iloc[0]) # 采样频率
|
|
|
- dt = 1 / fs # 时间间隔
|
|
|
-
|
|
|
- # 计算时域指标（向量化操作）
|
|
|
- mean = np.mean(data)
|
|
|
- max_val = np.max(data)
|
|
|
- min_val = np.min(data)
|
|
|
- Xrms = np.sqrt(np.mean(data**2))
|
|
|
- Xp = (max_val - min_val) / 2
|
|
|
- Cf = Xp / Xrms
|
|
|
- Sf = Xrms / mean if mean != 0 else 0
|
|
|
- If = Xp / np.mean(np.abs(data))
|
|
|
- Xr = np.mean(np.sqrt(np.abs(data))) ** 2
|
|
|
- Ce = Xp / Xr
|
|
|
-
|
|
|
- # 计算偏度和峭度
|
|
|
-
|
|
|
-
|
|
|
- # 计算速度有效值
|
|
|
+ combined_df = pd.concat(self.datas, ignore_index=True)
|
|
|
+ if combined_df.empty:
|
|
|
+ return json.dumps([], ensure_ascii=False)
|
|
|
+
|
|
|
+ # 统一解析 mesure_data
|
|
|
+ def parse_cell(x):
|
|
|
+ v = self._parse_mesure_data(x)
|
|
|
+ return v
|
|
|
+
|
|
|
+ combined_df['parsed_data'] = combined_df['mesure_data'].apply(parse_cell)
|
|
|
+
|
|
|
+ def calculate_stats(group: pd.DataFrame) -> Optional[Dict[str, Any]]:
|
|
|
+ if group.empty:
|
|
|
+ return None
|
|
|
+
|
|
|
+ arr = group['parsed_data'].iloc[0]
|
|
|
+ if arr is None or len(arr) == 0:
|
|
|
+ return None
|
|
|
+
|
|
|
+ data = np.asarray(arr, dtype=float)
|
|
|
+ fs = int(group['sampling_frequency'].iloc[0])
|
|
|
+ dt = 1 / fs
|
|
|
+
|
|
|
+ mean = float(np.mean(data))
|
|
|
+ max_val = float(np.max(data))
|
|
|
+ min_val = float(np.min(data))
|
|
|
+ Xrms = float(np.sqrt(np.mean(data ** 2)))
|
|
|
+
|
|
|
+ Xp = float((max_val - min_val) / 2)
|
|
|
+ Cf = (Xp / Xrms) if Xrms != 0 else 0.0
|
|
|
+ Sf = (Xrms / mean) if mean != 0 else 0.0
|
|
|
+ If = (Xp / float(np.mean(np.abs(data)))) if np.mean(np.abs(data)) != 0 else 0.0
|
|
|
+
|
|
|
+ Xr = float(np.mean(np.sqrt(np.abs(data))) ** 2)
|
|
|
+ Ce = (Xp / Xr) if Xr != 0 else 0.0
|
|
|
+
|
|
|
+ # 速度有效值
|
|
|
velocity = np.cumsum(data) * dt
|
|
|
- velocity_rms = np.sqrt(np.mean(velocity**2))
|
|
|
- Cw = np.mean((data - mean) ** 3) / (Xrms ** 3) if Xrms != 0 else 0
|
|
|
- Cq = np.mean((data - mean) ** 4) / (Xrms ** 4) if Xrms != 0 else 0
|
|
|
+ velocity_rms = float(np.sqrt(np.mean(velocity ** 2)))
|
|
|
+
|
|
|
+ Cw = float(np.mean((data - mean) ** 3) / (Xrms ** 3)) if Xrms != 0 else 0.0
|
|
|
+ Cq = float(np.mean((data - mean) ** 4) / (Xrms ** 4)) if Xrms != 0 else 0.0
|
|
|
+
|
|
|
return {
|
|
|
"fs": fs,
|
|
|
"Mean": round(mean, 2),
|
|
|
@@ -432,263 +437,265 @@ class CMSAnalyst:
|
|
|
"Cw": round(Cw, 2),
|
|
|
"Cq": round(Cq, 2),
|
|
|
"velocity_rms": round(velocity_rms, 2),
|
|
|
- "time_stamp": str(group['time_stamp'].iloc[0])
|
|
|
+ "time_stamp": str(group['time_stamp'].iloc[0]),
|
|
|
+ "id": int(group['id'].iloc[0]),
|
|
|
}
|
|
|
-
|
|
|
-
|
|
|
- # 3. 按 ID 分组并应用统计计算
|
|
|
- stats = combined_df.groupby('id').apply(calculate_stats).tolist()
|
|
|
|
|
|
- # 4. 返回 JSON 格式结果
|
|
|
- return json.dumps(stats, ensure_ascii=False)
|
|
|
+ stats = [
|
|
|
+ s for s in combined_df.groupby('id', sort=True).apply(calculate_stats).tolist()
|
|
|
+ if s is not None
|
|
|
+ ]
|
|
|
|
|
|
+ stats = self.replace_nan(stats)
|
|
|
+ return json.dumps(stats, ensure_ascii=False)
|
|
|
|
|
|
+ # ==========================================================
|
|
|
+ # 特征频率
|
|
|
+ # ==========================================================
|
|
|
def Characteristic_Frequency(self):
|
|
|
- """提取轴承、齿轮等参数"""
|
|
|
- str1 = self.mesure_point_name
|
|
|
- print(str1)
|
|
|
- # 2、连接233的数据库'energy_show'，从表'wind_engine_group'查找风机编号'engine_code'对应的机型编号'mill_type_code'
|
|
|
- engine_code = self.wind_code
|
|
|
- print(engine_code)
|
|
|
+ """
|
|
|
+ 目标：拿到 _brand/_model -> unit_dict_brand_model -> rolls_number 等参数
|
|
|
+ 任意失败：返回 None 字段（不会炸）
|
|
|
+ """
|
|
|
+ def empty_result():
|
|
|
+ return {
|
|
|
+ "type": "bearing",
|
|
|
+ "n_rolls": None,
|
|
|
+ "d_rolls": None,
|
|
|
+ "D_diameter": None,
|
|
|
+ "theta_deg": None,
|
|
|
+ }
|
|
|
+
|
|
|
+ str1 = str(self.mesure_point_name or "")
|
|
|
+ engine_code = str(self.wind_code or "")
|
|
|
+
|
|
|
Engine = create_engine('mysql+pymysql://admin:admin123456@192.168.50.233:3306/energy_show')
|
|
|
- #Engine = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')
|
|
|
- # df_sql2 = f"SELECT * FROM {'wind_engine_group'} where engine_code = {'engine_code'} "
|
|
|
- df_sql2 = f"SELECT * FROM wind_engine_group WHERE engine_code = '{engine_code}'"
|
|
|
- df2 = pd.read_sql(df_sql2, Engine)
|
|
|
+
|
|
|
+ df2 = pd.read_sql(
|
|
|
+ f"SELECT * FROM wind_engine_group WHERE engine_code = '{engine_code}'",
|
|
|
+ Engine
|
|
|
+ )
|
|
|
+ if df2.empty or 'mill_type_code' not in df2.columns:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
mill_type_code = df2['mill_type_code'].iloc[0]
|
|
|
- print(mill_type_code)
|
|
|
+ _brand = None
|
|
|
+ _model = None
|
|
|
|
|
|
- # # 3、从表'unit_bearings'中通过机型编号'mill_type_code'查找部件'brand'、'model'的参数信息
|
|
|
- # 3、从相关的表中通过机型编号'mill_type_code'或者齿轮箱编号gearbox_code查找部件'brand'、'model'的参数信息
|
|
|
- #unit_bearings主轴承参数表 关键词"main_bearing"
|
|
|
+ # --------------------------
|
|
|
+ # main_bearing
|
|
|
+ # --------------------------
|
|
|
if 'main_bearing' in str1:
|
|
|
- print("main_bearing")
|
|
|
- df_sql3 = f"SELECT * FROM unit_bearings WHERE mill_type_code = '{mill_type_code}' "
|
|
|
- df3 = pd.read_sql(df_sql3, Engine)
|
|
|
+ df3 = pd.read_sql(
|
|
|
+ f"SELECT * FROM unit_bearings WHERE mill_type_code = '{mill_type_code}'",
|
|
|
+ Engine
|
|
|
+ )
|
|
|
if df3.empty:
|
|
|
- print("警告: 没有找到有效的机型信息")
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ # front/rear/自动选择
|
|
|
if 'front' in str1:
|
|
|
- brand = 'front_bearing' + '_brand'
|
|
|
- model = 'front_bearing' + '_model'
|
|
|
- front_has_value = not pd.isna(df3[brand].iloc[0]) and not pd.isna(df3[model].iloc[0])
|
|
|
- if not front_has_value:
|
|
|
- print("警告: 没有找到有效的品牌信息")
|
|
|
+ brand_col = 'front_bearing_brand'
|
|
|
+ model_col = 'front_bearing_model'
|
|
|
elif 'rear' in str1:
|
|
|
- brand = 'rear_bearing' + '_brand'
|
|
|
- model = 'rear_bearing' + '_model'
|
|
|
- end_has_value = not pd.isna(df3[brand].iloc[0]) and not pd.isna(df3[model].iloc[0])
|
|
|
- if not end_has_value:
|
|
|
- print("警告: 没有找到有效的品牌信息")
|
|
|
+ brand_col = 'rear_bearing_brand'
|
|
|
+ model_col = 'rear_bearing_model'
|
|
|
else:
|
|
|
- # 当没有指定 front 或 end 时，自动选择有值的轴承信息
|
|
|
- front_brand_col = 'front_bearing_brand'
|
|
|
- front_model_col = 'front_bearing_model'
|
|
|
- rear_brand_col = 'rear_bearing_brand'
|
|
|
- rear_model_col = 'rear_bearing_model'
|
|
|
- # 检查 front_bearing 是否有值
|
|
|
- front_has_value = not pd.isna(df3[front_brand_col].iloc[0]) and not pd.isna(df3[front_model_col].iloc[0])
|
|
|
- # 检查 end_bearing 是否有值
|
|
|
- end_has_value = not pd.isna(df3[rear_brand_col].iloc[0]) and not pd.isna(df3[rear_model_col].iloc[0])
|
|
|
- # 根据检查结果选择合适的列
|
|
|
- if front_has_value and end_has_value:
|
|
|
- # 如果两者都有值，默认选择 front
|
|
|
- brand = front_brand_col
|
|
|
- model = front_model_col
|
|
|
- elif front_has_value:
|
|
|
- brand = front_brand_col
|
|
|
- model = front_model_col
|
|
|
- elif end_has_value:
|
|
|
- brand = rear_brand_col
|
|
|
- model = rear_model_col
|
|
|
- else:
|
|
|
- # 如果两者都没有有效值，设置默认值或抛出异常
|
|
|
- print("警告: 没有找到有效的轴承信息")
|
|
|
- brand = front_brand_col # 默认使用 front
|
|
|
- model = front_model_col # 默认使用 front
|
|
|
- print(brand)
|
|
|
- _brand = df3[brand].iloc[0]
|
|
|
- _model = df3[model].iloc[0]
|
|
|
- print(_brand)
|
|
|
- print(_model)
|
|
|
- #unit_dynamo 发电机参数表 关键词generator stator
|
|
|
- elif 'generator'in str1 or 'stator' in str1:
|
|
|
- print("generator or 'stator'")
|
|
|
- # df_sql3 = f"SELECT * FROM {'unit_dynamo'} where mill_type_code = {'mill_type_code'} "
|
|
|
- df_sql3 = f"SELECT * FROM unit_dynamo WHERE mill_type_code = '{mill_type_code}' "
|
|
|
- df3 = pd.read_sql(df_sql3, Engine)
|
|
|
+ candidates = [
|
|
|
+ ('front_bearing_brand', 'front_bearing_model'),
|
|
|
+ ('rear_bearing_brand', 'rear_bearing_model')
|
|
|
+ ]
|
|
|
+ brand_col = model_col = None
|
|
|
+ for b, m in candidates:
|
|
|
+ if b in df3.columns and m in df3.columns:
|
|
|
+ if pd.notna(df3[b].iloc[0]) and pd.notna(df3[m].iloc[0]):
|
|
|
+ brand_col, model_col = b, m
|
|
|
+ break
|
|
|
+ if brand_col is None:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ if brand_col not in df3.columns or model_col not in df3.columns:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ _brand = df3[brand_col].iloc[0]
|
|
|
+ _model = df3[model_col].iloc[0]
|
|
|
+
|
|
|
+ # --------------------------
|
|
|
+ # generator / stator
|
|
|
+ # --------------------------
|
|
|
+ elif 'generator' in str1 or 'stator' in str1:
|
|
|
+ df3 = pd.read_sql(
|
|
|
+ f"SELECT * FROM unit_dynamo WHERE mill_type_code = '{mill_type_code}'",
|
|
|
+ Engine
|
|
|
+ )
|
|
|
+ if df3.empty:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
if 'non' in str1:
|
|
|
- brand = 'non_drive_end_bearing' + '_brand'
|
|
|
- model = 'non_drive_end_bearing' + '_model'
|
|
|
+ brand_col = 'non_drive_end_bearing_brand'
|
|
|
+ model_col = 'non_drive_end_bearing_model'
|
|
|
else:
|
|
|
- brand = 'drive_end_bearing' + '_brand'
|
|
|
- model = 'drive_end_bearing' + '_model'
|
|
|
- print(brand)
|
|
|
- _brand = df3[brand].iloc[0]
|
|
|
- _model = df3[model].iloc[0]
|
|
|
- print(_brand)
|
|
|
- print(_model)
|
|
|
-
|
|
|
- #齿轮箱区分行星轮/平行轮 和 轴承两个表
|
|
|
+ brand_col = 'drive_end_bearing_brand'
|
|
|
+ model_col = 'drive_end_bearing_model'
|
|
|
+
|
|
|
+ if brand_col not in df3.columns or model_col not in df3.columns:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ _brand = df3[brand_col].iloc[0]
|
|
|
+ _model = df3[model_col].iloc[0]
|
|
|
+
|
|
|
+ # --------------------------
|
|
|
+ # gearbox
|
|
|
+ # --------------------------
|
|
|
elif 'gearbox' in str1:
|
|
|
- print("gearbox")
|
|
|
- #根据mill_type_code从unit_gearbox表中获得gearbox_code
|
|
|
- df_sql3 = f"SELECT * FROM unit_gearbox WHERE mill_type_code = '{mill_type_code}' "
|
|
|
- df3 = pd.read_sql(df_sql3, Engine)
|
|
|
- gearbox_code =df3['code'].iloc[0]
|
|
|
- print(gearbox_code)
|
|
|
- #Engine33 = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')
|
|
|
- #如果是行星轮/平行轮 则从unit_gearbox_structure 表中取数据
|
|
|
- if 'planet'in str1 or 'sun' in str1:
|
|
|
- print("'planet' or 'sun' ")
|
|
|
- gearbox_structure =1 if 'planet'in str1 else 2
|
|
|
- planetary_gear_grade =1
|
|
|
- if 'first' in str1:
|
|
|
- planetary_gear_grade =1
|
|
|
- elif 'second'in str1:
|
|
|
- planetary_gear_grade =2
|
|
|
- elif 'third'in str1:
|
|
|
- planetary_gear_grade =3
|
|
|
- # df_sql33 = f"SELECT * FROM unit_gearbox_structure WHERE gearbox_code = '{gearbox_code}' "
|
|
|
- df_sql33 = f"""
|
|
|
- SELECT bearing_brand, bearing_model
|
|
|
- FROM unit_gearbox_structure
|
|
|
- WHERE gearbox_code = '{gearbox_code}'
|
|
|
- AND gearbox_structure = '{gearbox_structure}'
|
|
|
- AND planetary_gear_grade = '{planetary_gear_grade}'
|
|
|
- """
|
|
|
- df33 = pd.read_sql(df_sql33, Engine)
|
|
|
+ df3 = pd.read_sql(
|
|
|
+ f"SELECT * FROM unit_gearbox WHERE mill_type_code = '{mill_type_code}'",
|
|
|
+ Engine
|
|
|
+ )
|
|
|
+ if df3.empty or 'code' not in df3.columns:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ gearbox_code = df3['code'].iloc[0]
|
|
|
+
|
|
|
+ # 行星轮/太阳轮：unit_gearbox_structure
|
|
|
+ if ('planet' in str1) or ('sun' in str1):
|
|
|
+ gearbox_structure = 1 if 'planet' in str1 else 2
|
|
|
+ planetary_gear_grade = 1
|
|
|
+ if 'second' in str1:
|
|
|
+ planetary_gear_grade = 2
|
|
|
+ elif 'third' in str1:
|
|
|
+ planetary_gear_grade = 3
|
|
|
+
|
|
|
+ df33 = pd.read_sql(
|
|
|
+ f"""
|
|
|
+ SELECT bearing_brand, bearing_model
|
|
|
+ FROM unit_gearbox_structure
|
|
|
+ WHERE gearbox_code = '{gearbox_code}'
|
|
|
+ AND gearbox_structure = '{gearbox_structure}'
|
|
|
+ AND planetary_gear_grade = '{planetary_gear_grade}'
|
|
|
+ """,
|
|
|
+ Engine
|
|
|
+ )
|
|
|
if df33.empty:
|
|
|
- print("unit_gearbox_structure没有该测点的参数")
|
|
|
- else:
|
|
|
- brand = 'bearing' + '_brand'
|
|
|
- model = 'bearing' + '_model'
|
|
|
- print(brand)
|
|
|
- _brand = df33[brand].iloc[0]
|
|
|
- _model = df33[model].iloc[0]
|
|
|
- has_value = not pd.isna(df33[brand].iloc[0]) and not pd.isna(df33[model].iloc[0])
|
|
|
- if has_value:
|
|
|
- print(_brand)
|
|
|
- print(_model)
|
|
|
- else:
|
|
|
- print("警告: 没有找到有效的轴承信息")
|
|
|
- #如果是齿轮箱轴承 则从unit_gearbox_bearings 表中取数据
|
|
|
- elif 'shaft' in str1 or'input' in str1:
|
|
|
- print("'shaft'or'input'")
|
|
|
- # df_sql33 = f"SELECT * FROM unit_gearbox_bearings WHERE gearbox_code = '{gearbox_code}' "
|
|
|
- # df33 = pd.read_sql(df_sql33, Engine33)
|
|
|
- #高速轴 低速中间轴 取bearing_rs/gs均可
|
|
|
- parallel_wheel_grade=1
|
|
|
- if 'low_speed' in str1:
|
|
|
- parallel_wheel_grade =1
|
|
|
- elif 'low_speed_intermediate' in str1:
|
|
|
- parallel_wheel_grade =2
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ if 'bearing_brand' not in df33.columns or 'bearing_model' not in df33.columns:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ _brand = df33['bearing_brand'].iloc[0]
|
|
|
+ _model = df33['bearing_model'].iloc[0]
|
|
|
+
|
|
|
+ # 轴承：unit_gearbox_bearings
|
|
|
+ elif ('shaft' in str1) or ('input' in str1) or ('low_speed' in str1) or ('high_speed' in str1):
|
|
|
+ parallel_wheel_grade = 1
|
|
|
+ if 'low_speed_intermediate' in str1:
|
|
|
+ parallel_wheel_grade = 2
|
|
|
elif 'high_speed' in str1:
|
|
|
- parallel_wheel_grade =3
|
|
|
- # df_sql33 = f"SELECT * FROM unit_gearbox_bearings WHERE gearbox_code = '{gearbox_code}' "
|
|
|
- df_sql33 = f"""
|
|
|
- SELECT bearing_rs_brand, bearing_rs_model, bearing_gs_brand, bearing_gs_model
|
|
|
- FROM unit_gearbox_bearings
|
|
|
- WHERE gearbox_code = '{gearbox_code}'
|
|
|
- AND parallel_wheel_grade = '{parallel_wheel_grade}'
|
|
|
- """
|
|
|
- df33 = pd.read_sql(df_sql33, Engine)
|
|
|
- if not df33.empty:
|
|
|
- if 'high_speed' in str1 or 'low_speed_intermediate' in str1:
|
|
|
- rs_brand = 'bearing_rs' + '_brand'
|
|
|
- rs_model = 'bearing_rs' + '_model'
|
|
|
- gs_brand = 'bearing_gs' + '_brand'
|
|
|
- gs_model = 'bearing_gs' + '_model'
|
|
|
- rs_has_value = not pd.isna(df33[rs_brand].iloc[0]) and not pd.isna(df33[rs_model].iloc[0])
|
|
|
- gs_has_value = not pd.isna(df33[gs_brand].iloc[0]) and not pd.isna(df33[gs_model].iloc[0])
|
|
|
- if rs_has_value and gs_has_value:
|
|
|
- brand = rs_brand
|
|
|
- model = rs_model
|
|
|
- elif rs_has_value:
|
|
|
- brand = rs_brand
|
|
|
- model = rs_model
|
|
|
- elif gs_has_value:
|
|
|
- brand = gs_brand
|
|
|
- model = gs_model
|
|
|
- else:
|
|
|
- print("警告: 没有找到有效的品牌信息")
|
|
|
- brand = rs_brand
|
|
|
- model = rs_model
|
|
|
- #低速轴 取bearing_model
|
|
|
- elif 'low_speed'in str1:
|
|
|
- brand = 'bearing' + '_brand'
|
|
|
- model = 'bearing' + '_model'
|
|
|
+ parallel_wheel_grade = 3
|
|
|
+
|
|
|
+ df33 = pd.read_sql(
|
|
|
+ f"""
|
|
|
+ SELECT bearing_rs_brand, bearing_rs_model, bearing_gs_brand, bearing_gs_model, bearing_brand, bearing_model
|
|
|
+ FROM unit_gearbox_bearings
|
|
|
+ WHERE gearbox_code = '{gearbox_code}'
|
|
|
+ AND parallel_wheel_grade = '{parallel_wheel_grade}'
|
|
|
+ """,
|
|
|
+ Engine
|
|
|
+ )
|
|
|
+ if df33.empty:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ # 高速/中间优先 rs/gs；低速取 bearing_brand/model
|
|
|
+ if ('high_speed' in str1) or ('low_speed_intermediate' in str1):
|
|
|
+ # rs/gs 选有值的
|
|
|
+ candidates = [
|
|
|
+ ('bearing_rs_brand', 'bearing_rs_model'),
|
|
|
+ ('bearing_gs_brand', 'bearing_gs_model')
|
|
|
+ ]
|
|
|
+ for b, m in candidates:
|
|
|
+ if b in df33.columns and m in df33.columns:
|
|
|
+ if pd.notna(df33[b].iloc[0]) and pd.notna(df33[m].iloc[0]):
|
|
|
+ _brand = df33[b].iloc[0]
|
|
|
+ _model = df33[m].iloc[0]
|
|
|
+ break
|
|
|
+ if _brand is None:
|
|
|
+ return empty_result()
|
|
|
else:
|
|
|
- print("警告: 没有找到有效的轴承信息")
|
|
|
- print(brand)
|
|
|
- _brand = df33[brand].iloc[0]
|
|
|
- _model = df33[model].iloc[0]
|
|
|
- print(_brand)
|
|
|
- print(_model)
|
|
|
-
|
|
|
-
|
|
|
- # 4、从表'unit_dict_brand_model'中通过'_brand'、'_model'查找部件的参数信息
|
|
|
-
|
|
|
- df_sql4 = f"SELECT * FROM unit_dict_brand_model where manufacture = %s AND model_number = %s"
|
|
|
- params = [(_brand, _model)]
|
|
|
- df4 = pd.read_sql(df_sql4, Engine, params=params)
|
|
|
- n_rolls = df4['rolls_number'].iloc[0]
|
|
|
- d_rolls = df4['rolls_diameter'].iloc[0]
|
|
|
- D_diameter = df4['circle_diameter'].iloc[0]
|
|
|
- theta_deg = df4['theta_deg'].iloc[0]
|
|
|
- result = {
|
|
|
- "type":'bearing',
|
|
|
- "n_rolls":round(n_rolls, 2),
|
|
|
- "d_rolls":round(d_rolls, 2),
|
|
|
- "D_diameter":round(D_diameter, 2),
|
|
|
- "theta_deg":round(theta_deg, 2),
|
|
|
- }
|
|
|
- # result = json.dumps(result, ensure_ascii=False)
|
|
|
- return result
|
|
|
- def calculate_bearing_frequencies(self, n, d, D, theta_deg, rpm):
|
|
|
- """
|
|
|
- 计算轴承各部件特征频率
|
|
|
+ # low_speed：bearing_brand/model
|
|
|
+ if 'bearing_brand' in df33.columns and 'bearing_model' in df33.columns:
|
|
|
+ if pd.notna(df33['bearing_brand'].iloc[0]) and pd.notna(df33['bearing_model'].iloc[0]):
|
|
|
+ _brand = df33['bearing_brand'].iloc[0]
|
|
|
+ _model = df33['bearing_model'].iloc[0]
|
|
|
+ else:
|
|
|
+ return empty_result()
|
|
|
+ else:
|
|
|
+ return empty_result()
|
|
|
|
|
|
- 参数：
|
|
|
- n (int): 滚动体数量
|
|
|
- d (float): 滚动体直径（单位：mm）
|
|
|
- D (float): 轴承节圆直径（滚动体中心圆直径，单位：mm）
|
|
|
- theta_deg (float): 接触角（单位：度）
|
|
|
- rpm (float): 转速（转/分钟）
|
|
|
+ else:
|
|
|
+ return empty_result()
|
|
|
|
|
|
- 返回：
|
|
|
- dict: 包含各特征频率的字典（单位：Hz）
|
|
|
- """
|
|
|
- # 转换角度为弧度
|
|
|
- theta = math.radians(theta_deg)
|
|
|
+ else:
|
|
|
+ # 其它测点：直接返回空，不炸
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ # 最终检查
|
|
|
+ if _brand is None or _model is None or (pd.isna(_brand) or pd.isna(_model)):
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ # --------------------------
|
|
|
+ # unit_dict_brand_model 查询
|
|
|
+ # --------------------------
|
|
|
+ df4 = pd.read_sql(
|
|
|
+ "SELECT * FROM unit_dict_brand_model WHERE manufacture = %s AND model_number = %s",
|
|
|
+ Engine,
|
|
|
+ params=(str(_brand), str(_model))
|
|
|
+ )
|
|
|
+ if df4.empty:
|
|
|
+ return empty_result()
|
|
|
+
|
|
|
+ # 字段安全读取
|
|
|
+ needed = ['rolls_number', 'rolls_diameter', 'circle_diameter', 'theta_deg']
|
|
|
+ for col in needed:
|
|
|
+ if col not in df4.columns:
|
|
|
+ return empty_result()
|
|
|
|
|
|
- # 转换直径单位为米（保持单位一致性，实际计算中比值抵消单位影响）
|
|
|
- # 注意：由于公式中使用的是比值，单位可以保持mm不需要转换
|
|
|
- ratio = d / D
|
|
|
+ return {
|
|
|
+ "type": "bearing",
|
|
|
+ "n_rolls": None if pd.isna(df4['rolls_number'].iloc[0]) else float(df4['rolls_number'].iloc[0]),
|
|
|
+ "d_rolls": None if pd.isna(df4['rolls_diameter'].iloc[0]) else float(df4['rolls_diameter'].iloc[0]),
|
|
|
+ "D_diameter": None if pd.isna(df4['circle_diameter'].iloc[0]) else float(df4['circle_diameter'].iloc[0]),
|
|
|
+ "theta_deg": None if pd.isna(df4['theta_deg'].iloc[0]) else float(df4['theta_deg'].iloc[0]),
|
|
|
+ }
|
|
|
|
|
|
- # 基础频率计算（转/秒）
|
|
|
+ # ==========================================================
|
|
|
+ # 轴承频率公式
|
|
|
+ # ==========================================================
|
|
|
+ def calculate_bearing_frequencies(self, n, d, D, theta_deg, rpm):
|
|
|
+ theta = math.radians(theta_deg)
|
|
|
+ ratio = d / D
|
|
|
f_r = rpm / 60.0
|
|
|
|
|
|
- # 计算各特征频率
|
|
|
- BPFI = n / 2 * (1 + ratio * math.cos(theta)) * f_r # 内圈故障频率
|
|
|
- BPFO = n / 2 * (1 - ratio * math.cos(theta)) * f_r # 外圈故障频率
|
|
|
- BSF = (D / (2 * d)) * (1 - (ratio ** 2) * (math.cos(theta) ** 2)) * f_r # 滚动体故障频率
|
|
|
- FTF = 0.5 * (1 - ratio * math.cos(theta)) * f_r # 保持架故障频率
|
|
|
+ BPFI = n / 2 * (1 + ratio * math.cos(theta)) * f_r
|
|
|
+ BPFO = n / 2 * (1 - ratio * math.cos(theta)) * f_r
|
|
|
+ BSF = (D / (2 * d)) * (1 - (ratio ** 2) * (math.cos(theta) ** 2)) * f_r
|
|
|
+ FTF = 0.5 * (1 - ratio * math.cos(theta)) * f_r
|
|
|
|
|
|
return {
|
|
|
- "BPFI": round(BPFI, 2),
|
|
|
- "BPFO": round(BPFO, 2),
|
|
|
- "BSF": round(BSF, 2),
|
|
|
- "FTF": round(FTF, 2),
|
|
|
-
|
|
|
+ "BPFI": round(float(BPFI), 2),
|
|
|
+ "BPFO": round(float(BPFO), 2),
|
|
|
+ "BSF": round(float(BSF), 2),
|
|
|
+ "FTF": round(float(FTF), 2),
|
|
|
}
|
|
|
-
|
|
|
- #检查返回结果是否有nan 若有，则替换成none
|
|
|
+
|
|
|
+ # ==========================================================
|
|
|
+ # NaN 替换
|
|
|
+ # ==========================================================
|
|
|
def replace_nan(self, obj):
|
|
|
if isinstance(obj, dict):
|
|
|
return {k: self.replace_nan(v) for k, v in obj.items()}
|
|
|
- elif isinstance(obj, list):
|
|
|
- return [self.replace_nan(item) for item in obj]
|
|
|
- elif isinstance(obj, float) and math.isnan(obj):
|
|
|
+ if isinstance(obj, list):
|
|
|
+ return [self.replace_nan(x) for x in obj]
|
|
|
+ if isinstance(obj, float) and math.isnan(obj):
|
|
|
return None
|
|
|
return obj
|
|
|
|
Xmia