CMS/cms_class_20241201.py

import numpy as np
from scipy.signal import hilbert
from scipy.fft import ifft
import plotly.graph_objs as go
import pandas as pd
from sqlalchemy import create_engine, text
import sqlalchemy
import json
import ast
import math

'''
# 输入：
{
    "ids":[12345,121212],
    "windCode":"xxxx",
    "analysisType":"xxxxx",
    "fmin":int(xxxx) (None),
    "fmax":"int(xxxx) (None),
}

[{id:xxxx,"time":xxx},{}]

id[123456]

# 通过id，读取mysql，获取数据
engine = create_engine('mysql+pymysql://root:admin123456@192.168.50.235:30306/energy_data')

def get_by_id(table_name,id):
    lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
    # print(lastday_df_sql)
    df = pd.read_sql(lastday_df_sql, engine)
    return df

select distinct id, timeStamp from table_name group by ids

ids  time 
1   xxx
2   xxxx

df_data = []    
# for id in ids:
# sql_data = get_by_id('SKF001_wave',id)
# df_data.append(sql_data)
# print(sql_data)

[df1,df2]
'''


'''
数据库字段：
"samplingFrequency"
"timeStamp"
"mesureData"
'''
# %%

# %%

# 主要的类
class CMSAnalyst:
    def __init__(self, fmin, fmax, table_name, ids):

        # envelope_spectrum_analysis
        # datas是[df1,df2,.....]
        self.datas = self._get_by_id(table_name,ids)
        self.datas = [df[['mesure_data','time_stamp','sampling_frequency','wind_turbine_number','rotational_speed','mesure_point_name']] for df in self.datas]
        # 只输入一个id，返回一个[df],所以拿到self.data[0]
        self.data_filter = self.datas[0]
        # print(self.data_filter)
        # 取数据列
        self.data = np.array(ast.literal_eval(self.data_filter['mesure_data'][0]))
        self.envelope_spectrum_m = self.data.shape[0]
        self.envelope_spectrum_n = 1
        self.fs = int(self.data_filter['sampling_frequency'].iloc[0])
        self.envelope_spectrum_t = np.arange(self.envelope_spectrum_m) / self.fs
        self.fmin = fmin if fmin is not None else 0 
        self.fmax = fmax if fmax is not None else float('inf') 
        self.envelope_spectrum_y = self._bandpass_filter(self.data)
        self.f, self.HP = self._calculate_envelope_spectrum(self.envelope_spectrum_y)
        self.wind_code = self.data_filter['wind_turbine_number'].iloc[0]
        self.rpm_Gen = self.data_filter['rotational_speed'].iloc[0]
        self.mesure_point_name = self.data_filter['mesure_point_name'].iloc[0]
        self.fn_Gen = round(self.rpm_Gen/60,2)

        self.CF = self.Characteristic_Frequency()
        print(self.CF)
        self.CF = pd.DataFrame(self.CF,index=[0])
        print(self.CF)
        print(self.rpm_Gen)
        if self.CF['type'].iloc[0] == 'bearing':
            n_rolls_m = self.CF['n_rolls'].iloc[0]
            d_rolls_m = self.CF['d_rolls'].iloc[0]
            D_diameter_m = self.CF['D_diameter'].iloc[0]
            theta_deg_m = self.CF['theta_deg'].iloc[0]
            print(n_rolls_m)
            print(d_rolls_m)
            print(D_diameter_m)
            print(theta_deg_m)
            self.bearing_frequencies = self.calculate_bearing_frequencies(n_rolls_m, d_rolls_m, D_diameter_m, theta_deg_m, self.rpm_Gen)
        print(self.bearing_frequencies)
        self.bearing_frequencies = pd.DataFrame(self.bearing_frequencies,index=[0])
        print(self.bearing_frequencies)
        # frequency_domain_analysis
        (
            self.frequency_domain_analysis_t,
            self.frequency_domain_analysis_f,
            self.frequency_domain_analysis_m,
            self.frequency_domain_analysis_mag,
            self.frequency_domain_analysis_Xrms,
        ) = self._calculate_spectrum(self.data)

        # time_domain_analysis
        self.time_domain_analysis_t = np.arange(self.data.shape[0]) / self.fs
        
    def _get_by_id(self, windcode, ids):
        df_res = []
        engine = create_engine('mysql+pymysql://root:admin123456@106.120.102.238:10336/energy_data_prod')
        for id in ids:
            table_name=windcode+'_wave'
            lastday_df_sql = f"SELECT * FROM {table_name} where id = {id} "
            # print(lastday_df_sql)
            df = pd.read_sql(lastday_df_sql, engine)
            df_res.append(df)
        return df_res

    # envelope_spectrum_analysis
    def _bandpass_filter(self, data):
        """带通滤波"""
        
        m= data.shape[0]
        ni = round(self.fmin * self.envelope_spectrum_m / self.fs + 1)
        # na = round(self.fmax * self.envelope_spectrum_m / self.fs + 1)
        if self.fmax == float('inf'):
            na = m
        else:
            na = round(self.fmax * m / self.fs + 1)
        col = 1
        y = np.zeros((self.envelope_spectrum_m, col))
        # for p in range(col):
        #     print(data.shape,p)
        z = np.fft.fft(data)
        a = np.zeros(self.envelope_spectrum_m, dtype=complex)
        a[ni:na] = z[ni:na]
        a[self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1] = z[
            self.envelope_spectrum_m - na + 1 : self.envelope_spectrum_m - ni + 1
        ]
        z = np.fft.ifft(a)
        y[:, 0] = np.real(z)

        return y

    def _calculate_envelope_spectrum(self, y):
        """计算包络谱"""
        m, n = y.shape
        HP = np.zeros((m, n))
        col = 1
        for p in range(col):
            H = np.abs(hilbert(y[:, p] - np.mean(y[:, p])))
            HP[:, p] = np.abs(np.fft.fft(H - np.mean(H))) * 2 / m
        f = np.fft.fftfreq(m, d=1 / self.fs)
        return f, HP


    def envelope_spectrum(self):
        """绘制包络谱"""
        # 只取正频率部分
        positive_frequencies = self.f[: self.envelope_spectrum_m // 2]
        positive_HP = self.HP[: self.envelope_spectrum_m // 2, 0]

        x = positive_frequencies
        y = positive_HP
        title = "包络谱"
        xaxis = "频率(Hz)"
        yaxis = "加速度(m/s^2)"
        Xrms = np.sqrt(np.mean(y**2))  # 加速度均方根值（有效值）
        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':
            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,
            }
        # result = json.dumps(result, ensure_ascii=False)
        return result


    # frequency_domain_analysis

    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)
        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
        ]

        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':
            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,
            }
        result = json.dumps(result, ensure_ascii=False)
        return result

    # time_domain_analysis
    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

        }

        result = json.dumps(result, ensure_ascii=False)

        return result


    # trend_analysis

    def trend_analysis(self):

        all_stats = []

        # 定义积分函数
        def _integrate(data, dt):
            return np.cumsum(data) * dt

        # 定义计算统计指标的函数
        def _calculate_stats(data):
            mean_value = np.mean(data)
            max_value = np.max(data)
            min_value = np.min(data)
            Xrms = np.sqrt(np.mean(data**2))  # 加速度均方根值（有效值）
            # Xrms = filtered_acceleration_rms  # 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
            Cf = Xp / Xrms  # 峰值指标
            Sf = Xrms / mean_value  # 波形指标
            If = Xp / np.mean(np.abs(data))  # 脉冲指标
            Xr = np.mean(np.sqrt(np.abs(data))) ** 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(data) - 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(data) - mean_value) ** 4)
            # 计算峭度指标
            Cq = sum_abs_diff_cubed_4 / (Xrms**4)
            #

            return {
                "fs":self.fs,#采样频率
                "Mean": round(mean_value, 2),#平均值
                "Max": round(max_value, 2),#最大值
                "Min": round(min_value, 2),#最小值
                "Xrms": round(Xrms, 2),#有效值
                "Xp": round(Xp, 2),#峰值
                "If": round(If, 2), # 脉冲指标
                "Cf": round(Cf, 2),#峰值指标
                "Sf": round(Sf, 2),#波形指标
                "Ce": round(Ce, 2),#裕度指标
                "Cw": round(Cw, 2) ,#偏度指标
                "Cq": round(Cq, 2),#峭度指标
                #velocity_rms :速度有效值
                #time_stamp:时间戳
            }

        for data in self.datas:
            fs=int(self.data_filter['sampling_frequency'].iloc[0])
            dt = 1 / fs
            time_stamp=data['time_stamp'][0]
            print(time_stamp)
            data=np.array(ast.literal_eval(data['mesure_data'][0]))

            velocity = _integrate(data, dt)
            velocity_rms = np.sqrt(np.mean(velocity**2))
            stats = _calculate_stats(data)
            stats["velocity_rms"] = round(velocity_rms, 2)#速度有效值
            stats["time_stamp"] = str(time_stamp)#时间戳

            all_stats.append(stats)

            # df = pd.DataFrame(all_stats)
        all_stats = json.dumps(all_stats,  ensure_ascii=False)
        return all_stats

    def Characteristic_Frequency(self):
        """提取轴承、齿轮等参数"""
        # 1、从测点名称中提取部件名称（计算特征频率的部件）
        str1 = self.mesure_point_name
        str2 = ["main_bearing", "front_main_bearing", "rear_main_bearing", "generator_non_drive_end"]
        for str in str2:
            if str in str1:
                parts = str
                if parts == "front_main_bearing":
                    parts = "front_bearing"
                elif parts == "rear_main_bearing":
                    parts = "rear_bearing"

        print(parts)

        # 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
        Engine2 = 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'} "
        df2 = pd.read_sql(df_sql2, Engine2)
        mill_type_code = df2['mill_type_code'].iloc[0]
        print(mill_type_code)

        # 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
        Engine3 = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')
        df_sql3 = f"SELECT * FROM {'unit_bearings'} where mill_type_code = {'mill_type_code'} "
        df3 = pd.read_sql(df_sql3, Engine3)
        brand = 'front_bearing' + '_brand'  # parts代替'front_bearing'
        model = 'front_bearing' + '_model'  # parts代替'front_bearing'
        print(brand)
        _brand = df3[brand].iloc[0]
        _model = df3[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
        Engine4 = create_engine('mysql+pymysql://admin:admin123456@106.120.102.238:16306/energy_show')
        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, Engine4, params=params)
        if 'bearing' in parts:
            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

        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）

        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)

        # 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

        # 基础频率计算（转/秒）
        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

        return {
            "BPFI": round(BPFI, 2),
            "BPFO": round(BPFO, 2),
            "BSF": round(BSF, 2),
            "FTF": round(FTF, 2),

        }

if __name__ == "__main__":
    # table_name = "SKF001_wave"
    # ids = [67803,67804]
    # fmin, fmax = None, None

    cms = CMSAnalyst(fmin, fmax,table_name,ids)
    time_domain = cms.time_domain()
    # print(time_domain)

'''
    trace = go.Scatter(
        x=time_domain['x'],
        y=time_domain['y'],
        mode="lines",
        name=time_domain['title'],
    )
    layout = go.Layout(
        title= time_domain['title'],
        xaxis=dict(title=time_domain["xaxis"]),
        yaxis=dict(title=time_domain["yaxis"]),
    )
    fig = go.Figure(data=[trace], layout=layout)
    fig.show()
'''

    # data_path_lsit = ["test1.csv", "test2.csv"]
    # trend_analysis_test = cms.trend_analysis(data_path_lsit, fmin, fmax)
    # print(trend_analysis_test)