CMS/Temp_Diag.PY

# @Time    : 2019-6-19
# @Author  : wilbur.cheng@SKF
# @FileName: baseMSET.py
# @Software: a package in skf anomaly detection library to realize multivariate state estimation technique
#            this package has included all the functions used in MSET, similarity calculation, state memory matrix D,
#            normal state matrix L, residual calculation, and the basic SPRT (Sequential Probability Ratio Test)
#            function for binary hypothesis testing.
#

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.neighbors import BallTree
import openpyxl
import time
#from scikit-learn.neighbors import BallTree

class MSET_Temp():

    matrixD = None
    matrixL = None
    healthyResidual = None

    normalDataBallTree = None



    def __init__(self):
        self.model = None

    def _get_by_id(self, table_name, ids):
            """Get data from MySQL database by 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+'_minute'
                lastday_df_sql = f"SELECT * FROM {table_name} where id = {id}"
                df = pd.read_sql(lastday_df_sql, engine)
                df_res.append(df)
            return df_ress

    def calcSimilarity(self, x, y, m = 'euc'):
        """
        Calcualte the similartity of two feature vector
        :param x: one of input feature list
        :param y: one of input feature list
        :param m: method of the similarity calculation method, default is the Eucilidean distance named 'euc';
                    a city block distance function is used when m set to "cbd'.
        :return: the two feature similarity, float, range (0,1)
        """

        if (len(x) != len(y)):
            return 0

        if (m == 'cbd'):
            dSimilarity = [1/(1+np.abs(p-q)) for p,q in zip(x,y)]
            dSimilarity = np.sum(dSimilarity)/len(x)
        else:
            dSimilarity = [np.power(p-q,2) for p, q in zip(x, y)]
            dSimilarity = 1/(1+np.sqrt(np.sum(dSimilarity)))

        return dSimilarity


    def genDLMatrix(self, trainDataset, dataSize4D=100, dataSize4L=50):
        """
        automatically generate the D and L matrix from training data set, assuming the training data set is all normal
        state data.
        :param trainDataset: 2D array, [count of data， length of feature]
        :param dataSize4D: minimized count of state for matrix D
        :param dataSize4L: minimized count of state for matrix L
        :return: 0 if successful, -1 if fail
        """
        [m,n] = np.shape(trainDataset)

        if m < dataSize4D + dataSize4L:
            print('training dataset is too small to generate the matrix D and L')
            return -1

        self.matrixD = []
        selectIndex4D = []
        # Step 1: add all the state with minimum or maximum value in each feature into Matrix D

        for i in range(0, n):
            feature_i = trainDataset[:,i]
            minIndex = np.argmin(feature_i)
            maxIndex = np.argmax(feature_i)

            self.matrixD.append(trainDataset[minIndex, :].tolist())
            selectIndex4D.append(minIndex)
            self.matrixD.append(trainDataset[maxIndex, :].tolist())
            selectIndex4D.append(maxIndex)

        # Step 2: iteratively add the state with the maximum average distance to the states in selected matrixD
        while(1):

            if (len(selectIndex4D) >= dataSize4D):
                break

            # Get the free state list
            freeStateList = list(set(np.arange(0, len(trainDataset))) - set(selectIndex4D))

            # Calculate the average dist of each state in free to selected state in matrix D
            distList = []
            for i in freeStateList:
                tmpState = trainDataset[i]
                tmpDist = [1-self.calcSimilarity(x, tmpState) for x in self.matrixD]
                distList.append(np.mean(tmpDist))

            # select the free state with largest average distance to states in matrixD, and update matrixD
            selectId = freeStateList[distList.index(np.max(distList))]
            self.matrixD.append(trainDataset[selectId, :].tolist())
            selectIndex4D.append(selectId)
        #format matrixD from list to array
        self.matrixD = np.array(self.matrixD)
        self.normalDataBallTree = BallTree(self.matrixD, leaf_size=4, metric = lambda i,j: 1-self.calcSimilarity(i,j))


        # Step 3. select remaining state for matrix L
        #index4L = list(set(np.arange(0, len(trainDataset))) - set(selectIndex4D))
        #self.matrixL = trainDataset[index4L, :]
        # consider the limited dataset, using all the train data to matrix L
        self.matrixL = trainDataset

        # Calculate the healthy Residual from matrix L
        lamdaRatio = 1e-3
        [m, n] = np.shape(self.matrixD)
        self.DDSimilarity = np.array([[1-self.calcSimilarity(x,y) for x in self.matrixD] for y in self.matrixD] + lamdaRatio*np.eye(n))
        self.DDSimilarity = 1/self.DDSimilarity

        #self.healthyResidual = self.calcResidual(self.matrixL)
        self.healthyResidual = self.calcResidualByLocallyWeightedLR(self.matrixL)


        return 0

    def calcResidualByLocallyWeightedLR(self, newStates):
        """
        find the K-nearest neighbors for each input state, then calculate the estimated state by locally weighted average.
        :param newStates: input states list
        :return: residual R_x
        """
        [m,n] = np.shape(newStates)
        est_X = []
        # print(newStates)
        for x in newStates:
            (dist, iList) = self.normalDataBallTree.query([x], 20, return_distance=True)
            weight = 1/(dist[0]+1e-1)
            weight = weight/sum(weight)
            eState = np.sum([w*self.matrixD[i] for w,i in zip(weight, iList[0])])
            est_X.append(eState)

        est_X = np.reshape(est_X, (len(est_X),1))
        # print(est_X)
        # print(newStates)
        return est_X - newStates

    def calcStateResidual(self, newsStates):
        stateResidual = self.calcResidualByLocallyWeightedLR(newsStates)
        return stateResidual

    def calcSPRT(self, newsStates, feature_weight, alpha=0.1, beta=0.1, decisionGroup=5):
        """
        anomaly detection based Wald's SPRT algorithm, refer to A.Wald, Sequential Analysis,Wiley, New York, NY, USA, 1947
        :param newsStates: input states list
        :param feature_weight: the important weight for each feature, Normalized and Nonnegative
        :param alpha: prescribed false alarm rate， 0 < alpha < 1
        :param beta: prescribed miss alarm rate, 0 < beta < 1
        :param decisionGroup: length of the test sample when the decision is done

        :return: anomaly flag for each group of states, 1:anomaly, -1:normal, (-1:1): unable to decision
        """

        # Step 1. transfer the raw residual vector to dimension reduced residual using feature weight
        #stateResidual = self.calcResidual(newsStates)
        stateResidual = self.calcResidualByLocallyWeightedLR(newsStates)
        # print(stateResidual)
        weightedStateResidual = [np.dot(x, feature_weight) for x in stateResidual]
        # print(weightedStateResidual)
        weightedHealthyResidual = [np.dot(x, feature_weight) for x in self.healthyResidual]

        '''
        plt.subplot(211)
        plt.plot(weightedHealthyResidual)
        plt.xlabel('Sample index')
        plt.ylabel('Healthy State Residual')
        plt.subplot(212)
        plt.plot(weightedStateResidual)
        plt.xlabel('Sample index')
        plt.ylabel('All State Residual')
        plt.show()
        '''
        # Calculate the distribution of health state residual
        mu0 = np.mean(weightedHealthyResidual)
        sigma0 = np.std(weightedHealthyResidual)


        #sigma1 = np.std(weightedStateResidual)

        lowThres = np.log(beta/(1-alpha))
        highThres = np.log((1-beta)/alpha)

        flag = []
        for i in range(0, len(newsStates)-decisionGroup+1):
            # For each group to calculate the new state residual distribution
            # Then check the  hypothesis testing results
            mu1 = np.mean(weightedStateResidual[i:i+decisionGroup])
            si = np.sum(weightedStateResidual[i:i+decisionGroup])*(mu1-mu0)/sigma0**2 - decisionGroup*(mu1**2-mu0**2)/(2*sigma0**2)

            if (si > highThres):
                si = highThres
            if (si < lowThres):
                si = lowThres

            if (si > 0):
                si = si/highThres
            if (si < 0):
                si = si/lowThres

            flag.append(si)

        return flag



if __name__=='__main__':

    start_time = time.time()
    myMSET = MSET_Temp()
    # 1、计算各子系统的健康度(子系统包括：发电机组、传动系统(直驱机组无齿轮箱、无数据)、机舱系统、变流器系统、电网环境、辅助系统（无数据))
    # 1.1、发电机组健康度评分
    # Title = pd.read_excel(r'/Users/xmia/Documents/code/Temp_Diag/34_QITAIHE.xlsx', header=None, nrows=1, usecols=[12, 13, 14])
    # df_D = pd.read_excel(r'/Users/xmia/Documents/code/Temp_Diag/34_QITAIHE.xlsx',
    #                        usecols=[0, 12, 13, 14], parse_dates=True)  # 读取温度指标：齿轮箱油温12、驱动侧发电机轴承温度13、非驱动侧发电机轴承温度14
    df = pd.read_csv('/Users/xmia/Desktop/ZN/华电中光/471_QTH1125/2024/#34.csv', usecols=[
                        'wind_turbine_number',
                        'time_stamp', 
                        'main_bearing_temperature', 
                        'gearbox_oil_temperature', 
                        'generatordrive_end_bearing_temperature', 
                        'generatornon_drive_end_bearing_temperature'
                    ], parse_dates=['time_stamp']) 
    df = df[df['wind_turbine_number'] == 'WOG01312']
    df = df.drop(columns=['wind_turbine_number'])
    df_D = df[df['time_stamp'] > '2024-11-15']

    cols = df_D.columns
    df_D[cols] = df_D[cols].apply(pd.to_numeric, errors='coerce')
    df_D = df_D.dropna()  # 删除空行和非数字项

    x_date = df_D.iloc[len(df_D) // 2 + 1:, 0]  #获取时间序列
    x_date = pd.to_datetime(x_date)
    df_Temp = df_D.iloc[:, 1:]  #获取除时间列以外的数据
    df_Temp_values = df_Temp.values
    df_Temp_values = np.array(df_Temp_values)
    [m, n] = np.shape(df_Temp_values)
    # Residual = []
    flag_Spart_data = []
    for i in range(0, n):
        df_Temp_i = df_Temp_values[:, i]
        trainDataSet_data = df_Temp_i[0:len(df_Temp_i) // 2]
        testDataSet_data = df_Temp_i[len(df_Temp_i) // 2 + 1:]
        trainDataSet_data = np.reshape(trainDataSet_data, (len(trainDataSet_data), 1))
        testDataSet_data = np.reshape(testDataSet_data, (len(testDataSet_data), 1))

        myMSET.genDLMatrix(trainDataSet_data, 60, 5)
        # stateResidual = self.calcStateResidual(testDataSet_data)
        flag_data = myMSET.calcSPRT(testDataSet_data, np.array(1), decisionGroup=1)
        # Residual.append(stateResidual)
        flag_Spart_data.append(flag_data)
    flag_Spart_data = np.array(flag_Spart_data)
    flag_Spart_data = flag_Spart_data.T
    Temp1 = flag_Spart_data[:,0]
    Temp2 = flag_Spart_data[:,1]
    Temp3 = flag_Spart_data[:,2]
    Temp1_lable = "gearbox_oil_temperature"
    Temp2_lable = "generatordrive_end_bearing_temperature"
    Temp3_lable = "generatornon_drive_end_bearing_temperature"

    print(x_date)

    # alarmedFlag = np.array([[i, Temp1[i]] for i, x in enumerate(Temp1) if x > 0.99])  # Flag中选出大于0.99的点

    plt.rcParams['font.sans-serif'] = ['SimHei']  # 显示中文标签
    plt.close('all')
    plt.subplot(311)
    plt.plot(x_date, Temp1, 'b-o', label=Temp1_lable)
    plt.ylabel(Temp1_lable)
    plt.xlabel('时间')

    # plt.scatter(alarmedFlag1[:, 0], alarmedFlag1[:, 2], marker='x', c='r')
    plt.subplot(312)
    plt.plot(x_date, Temp2)
    plt.ylabel(Temp2_lable)
    plt.xlabel('时间')
    #plt.scatter(alarmedFlag[:, 0], alarmedFlag[:, 1], marker='x', c='r')
    plt.subplot(313)
    plt.plot(x_date, Temp3)
    plt.ylabel(Temp3_lable)
    plt.xlabel('时间')
    #plt.scatter(alarmedFlag[:, 0], alarmedFlag[:, 1], marker='x', c='r')
    plt.show()
    print(flag_Spart_data)
    print(np.shape(flag_Spart_data))


    end_time = time.time()
    execution_time = end_time - start_time
    print(f"Execution time: {execution_time} seconds")



#

    '''
    f = open("speed_vib.txt", "r")
    data1 = f.read()
    f.close()
    data1 = data1.split('\n')
    rpm = [(row.split('\t')[0]).strip() for row in data1]
    vib = [(row.split('\t')[-1]).strip() for row in data1]

    # print(rpm)
    rpm = np.array(rpm).astype(np.float64)
    vib = np.array(vib).astype(np.float64)

    #vib = [(x-np.mean(vib))/np.std(vib) for x in vib]
    #print(vib)
    trainDataSet = [vib[i] for i in range(0,100) if vib[i] < 5]
    trainDataSet = np.reshape(trainDataSet,(len(trainDataSet),1))
    testDataSet = np.reshape(vib, (len(vib),1))
    '''


    # Title = pd.read_csv(r'F1710001001.csv', header=None, nrows=1, usecols=[36,37,38], encoding='gbk')

    '''
    alarmedFlag = np.array([[i, flag[i]] for i, x in enumerate(flag) if x > 0.99])  # Flag中选出大于0.99的点
    plt.close('all')
    plt.subplot(211)
    plt.plot(testDataSet)
    plt.ylabel('Vibration')
    plt.xlabel('Sample index')
    plt.subplot(212)
    plt.plot(flag)
    plt.ylabel('SPRT results')
    plt.xlabel('Sample index')
    plt.scatter(alarmedFlag[:,0], alarmedFlag[:,1], marker='x',c='r')

    plt.show()
    '''