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464 lines
21 KiB
464 lines
21 KiB
#!/usr/bin/python3.6.5
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# -*- coding: utf-8 -*-
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# Time : 2020/3/29 23:03
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# Author : HeKang
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# Github : https://github.com/JustKeepSilence
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import time
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import numpy as np
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import pandas as pd
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from scipy.stats import norm
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from scipy.stats.distributions import chi2
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import json
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from json import JSONDecodeError
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import sys
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import traceback
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import pymssql
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import requests
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import datetime
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from scipy.stats import norm
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from scipy.stats import f
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from scipy.stats import chi2
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import jenkspy
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# import pca_train_k
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import pcamtcltest_k
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# import model_performance
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import xlrd
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import random
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from pylab import mpl
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import matplotlib.pyplot as plt
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from collections import Counter
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from operator import itemgetter
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import PCA_Test
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import config
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from recon import Lars, recon_fault_diagnosis_r, recon_fault_diagnosis_r_l, recon_fault_diagnosis_r_c
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def pca(model, LockVariable,Data_origin,number_sample, number_fault_variable,
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low_f,high_f,Test_type,Limit_value, time_data, spe_recon,expand=True, sort_data=False):
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if len(low_f) >= 12:
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para_length = 3
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elif 12 > len(low_f) >= 7:
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para_length = 2
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else:
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para_length = 1
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low_f=np.array(low_f)/np.array(model["Train_X_std"]) # 对最低幅度归一�?
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high_f=np.array(high_f)/np.array(model["Train_X_std"]) # 对最高幅度归一�?
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yy = list(range(0, Data_origin.shape[0]))
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time_sample = []
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if expand:
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# random_row = np.random.choice(a=yy, size=number_sample)
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if len(yy) < 1000:
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random_row = np.array(list(range(len(yy))))
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else:
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random_row = np.array(random.sample(yy, number_sample))
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time_original = time_data[random_row, :]
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Data_origin = Data_origin[random_row, :] # 扩充选择1000个样�?
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else:
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# random_row = np.array(random.sample(yy, number_sample))
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random_row = np.random.choice(a=yy, size=number_sample)
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time_original = time_data[random_row, :]
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Data_origin = Data_origin[random_row, :] # 扩充选择random_row个样�?
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Data = (Data_origin - model["Train_X_mean"]) / model["Train_X_std"]
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featValue = np.array(model["featValue"])+0.00001 # 训练数据的特征�?
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featVec = np.array(model["featVec"]) # 训练数据的特征向�?
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numbel_variable = featValue.shape[0]
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k = (model["K"]) # 主元个数
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selectVec = featVec[:, 0:k] ####自己选择�?
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featValue_sort = featValue # [index] # 排序后的特征�?
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############----------*********-SPE-**************----------########################
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# LockVariable="3,5"
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C_ = np.eye(numbel_variable) - np.dot(selectVec, selectVec.T)
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X_SPE = C_.T
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D_SPE = C_
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DIAG_SPE = np.eye(numbel_variable)
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############----------*********-T2-**************----------########################
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DIAG_T2 = np.linalg.pinv(np.diag(featValue_sort[:int(model["K"])]))
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D_T2 = selectVec.copy()
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X_T2 = np.dot(D_T2, np.linalg.cholesky(DIAG_T2)).T
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############----------*********-综合指标-**************----------########################
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II = featValue_sort.copy()
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II[:int(model["K"])] = II[:int(model["K"])] * model["T2CUL_99"]
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II[int(model["K"]):] = model["QCUL_99"]
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DIAG_Fai = np.linalg.inv(np.diag(II))
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D_Fai = featVec.copy()
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X_Fai = np.dot(D_Fai, np.linalg.cholesky(DIAG_Fai)).T
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# ************************调用LARS*******************************
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t = 50000
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original_line, recon_line = [], []
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limit_line=float(Limit_value)
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beta_path = []
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SPE_list = []
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FAI_list = []
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Fault_noise = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]]) # 故障变化幅度
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Fault_index = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]]) # 真实故障方向
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paraState = np.zeros([np.array(Data_origin).shape[0], np.array(Data_origin).shape[1]]) # 故障重构的方�?
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index_num, index_point = [], []
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all_FDR = []
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all_FAR = []
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fault_free = []
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fdr = {}
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far = {}
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beta_pro = []
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fault_index_row = []
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plots_matrix = [] # 贡献图法的矩�?
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plots_index = [] # 贡献图法的index
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findex = np.where(low_f != 0)[0] # 判断加在那个参数�?
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for ii in range(Data.shape[0]):
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Y = Data[ii, :] # 测试数据的每一�?
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noise=[]
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for j in range(number_fault_variable):
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noise.append(np.random.uniform(low_f[findex[j]], high_f[findex[j]])) # 均匀分布
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Fault_index[ii, findex] = 1
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Fault_noise[ii, findex] = noise
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if Test_type == "FAI":
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original_line.append(Y @ D_Fai @ DIAG_Fai @ D_Fai.T @ Y.T)
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elif Test_type == "SPE":
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original_line.append(Y @ X_SPE.T @ X_SPE @ Y.T)
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Y = Y + Fault_noise[ii, :]
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#########*********************计算SPE******************************
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SPE_line = np.dot(Y, C_).dot(Y.T)
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SPE_list.append(SPE_line)
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#########################计算综合指标##########
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FAI_list.append(np.dot(Y.T, D_Fai).dot(DIAG_Fai).dot(D_Fai.T).dot(Y))
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# **************计算LARS***************
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if Test_type=="FAI":
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beta, mse = Lars(X_Fai, Y, D_Fai, DIAG_Fai, t, limit_line, LockVariable)
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elif Test_type=="SPE":
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beta, mse = Lars(X_SPE, Y, D_SPE, DIAG_SPE, t, limit_line, LockVariable)
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else:
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beta, mse = Lars(X_T2, Y, D_T2, DIAG_T2, t, limit_line, LockVariable)
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beta_end = abs(beta[-1, :])
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pi = len(beta_end)
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if pi > 7:
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jenk = jenkspy.jenks_breaks(beta_end, 5)
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else:
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jenk = jenkspy.jenks_breaks(beta_end, 2)
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limt = (jenk[1] + jenk[2]) / 2
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index = np.where(beta_end > 0)[0]
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fault_index_row.append(ii)
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if len(index) > para_length:
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if Test_type == "SPE" and np.linalg.det(X_SPE @ X_SPE.T) > 1e-15:
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res = recon_fault_diagnosis_r(Y, X_SPE @ X_SPE.T, limit_line, list(zip(index, beta_end[index])), model, True,
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X_SPE @ X_SPE.T, LockVariable, selectVec, None)
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if not isinstance(res[0], list):
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if res[1] == "plot":
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# beta[-1, :] = res[0]
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plots_matrix.append(res[0])
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plots_index.append(ii)
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else:
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beta[-1, :], index = res[0].T, res[1]
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elif Test_type == "FAI" and np.linalg.det( D_Fai @ DIAG_Fai @ D_Fai.T) > 1e-15:
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res = recon_fault_diagnosis_r_c(Y, D_Fai @ DIAG_Fai @ D_Fai.T, limit_line,
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list(zip(index, beta_end[index])), model,
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True, X_SPE @ X_SPE.T, LockVariable, selectVec, rbc=None)
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if not isinstance(res[0], list):
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if res[1] == "plot":
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# beta[-1, :] = res[0]
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plots_matrix.append(res[0])
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plots_index.append(ii)
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else:
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beta[-1, :], index = res[0].T, res[1]
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elif len(index) <= para_length and len(index) != 0:
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if Test_type == "SPE":
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res = recon_fault_diagnosis_r_l(Y, X_SPE @ X_SPE.T, index)
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beta[-1, :], index = res[0].T, res[1]
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elif Test_type == "FAI":
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res = recon_fault_diagnosis_r_l(Y, D_Fai @ DIAG_Fai @ D_Fai.T, index)
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beta[-1, :], index = res[0].T, res[1]
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paraState[ii, index] = 1
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beta_new = beta[-1, :] * paraState[ii, :]
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beta_path.append(beta_new)
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############################################################
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if index.shape[0]==0:
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fault_free.append(ii)
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##### 统计fault detection rate (FDR)
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d = [x for x in index if x in findex]
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all_FDR.append(len(d) / (len(findex)+0.000001))
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##计算单个参数的fdr
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for i in findex:
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if i not in fdr:
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if i in index:
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fdr.update({i: [1]})
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else:
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fdr.update({i: [0]})
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else:
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if i in index:
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fdr[i].append(1)
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else:
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fdr[i].append(0)
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###### 统计false alarm rate（FAR)
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a = [x for x in index if x not in d]
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all_FAR.append(len(a) / ((numbel_variable - len(findex))+0.0001))
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###统计单个参数的far
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beta_path = np.array(beta_path)
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FDR = sum(all_FDR) / Data.shape[0]#诊出�?
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FAR = sum(all_FAR) / Data.shape[0]#误诊�?
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fdr_variable = []#单参数诊出率
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far_variable = []#单参数误诊率
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for variable in range(numbel_variable):
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if variable not in fdr:
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fdr_variable.append(0)
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far_variable.append(0)
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else:
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fdr_variable.append(sum(fdr[variable]) / len(fdr[variable]))
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far_variable.append(FAR)
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fault_data=Data+Fault_noise
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fault_data = fault_data[fault_index_row, :]
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Fault_data=np.add(np.multiply(fault_data, model["Train_X_std"]), model["Train_X_mean"])
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finalData = fault_data - beta_path
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reconData = np.add(np.multiply(finalData, model["Train_X_std"]), model["Train_X_mean"]) # 重构�?
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if len(plots_matrix) != 0:
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reconData[plots_index] = plots_matrix
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Data_origin = Data_origin[fault_index_row, :]
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real_error_data = Data_origin - reconData
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errorData = sum(abs(Data_origin - reconData)) # 偏差�?
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Reconstruction_precision=errorData/Data.shape[0]
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test_data = np.around(Data_origin.T, decimals=3).tolist()
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error_data = np.around(real_error_data.T, decimals=3).tolist()
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time_stamp = time_original.T.tolist()[0]
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max_recon = list(zip(np.max(beta_path, axis=0).tolist(), np.min(beta_path, axis=0).tolist()))
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train_mean = model["Train_X_mean"]
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train_std = model["Train_X_std"]
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items = [
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('fdr_variable', np.around(fdr_variable, decimals=3).tolist()),
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('far_variable', np.around(far_variable, decimals=3).tolist()),
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('Reconstruction_precision', np.around(Reconstruction_precision, decimals=3).tolist()),
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("error_data", error_data),
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("test_data", test_data),
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("original_line", original_line),
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("time_stamp", time_stamp),
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("max_recon", max_recon),
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("mean", train_mean),
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("std", train_std)
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]
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result = dict(items)
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return result
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def isnumber(limits):
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flag = True
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for item in limits:
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item = item.replace("-","")
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if not item.isdigit():
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flag = False
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break
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return flag
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def cleanmain(info):
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try:
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ItemsInfo, SamplingTimePeriods = [], []
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model_id = info["Model_id"]
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version = info["version"]
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if version == "v-test":
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res = PCA_Test.get_model_by_ID(model_id)
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else:
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res = PCA_Test.get_model_by_id_and_version(model_id, version)
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lock = []
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point_info = res["pointInfo"]
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interval = info["Test_Data"]["interval"]
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for i in range(len(point_info)):
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try:
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if point_info[i]["lock"]:
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lock.append(i)
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except:
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continue
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Test_Data = info["Test_Data"]
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points = Test_Data["points"].split(",")
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try:
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times = Test_Data["time"].split(";")
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for i in range(len(times)):
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Eachsampletime = {}
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timess = times[i].split(',')
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Eachsampletime["StartingTime"] = timess[0]
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Eachsampletime["TerminalTime"] = timess[1]
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SamplingTimePeriods.append(Eachsampletime)
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except KeyError:
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SamplingTimePeriods = [{"StartingTime": item["st"],
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"TerminalTime": item["et"]} for item in res["trainTime"]]
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model = res["para"]["Model_info"]
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condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
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dead = info["dead"].split(',')
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limit = info["limit"].split(',')
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uplower = info["uplow"].split(';')
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count = 0
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Constraint = ""
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for i in range(len(points)):
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iteminfo = {}
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iteminfo["ItemName"] = points[i] # 加点
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if dead[i] == "1": # 判断是否参与死区清洗
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iteminfo["ClearDeadZone"] = "true"
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else:
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iteminfo["ClearDeadZone"] = "false"
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if limit[i] == "1": # 参与上下限清�?
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limits = uplower[i].split(',')
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if isnumber(limits): # 输入上下限正�?
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count += 1
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Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
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1] + " and "
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ItemsInfo.append(iteminfo)
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if count != 0:
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Constraint = Constraint[:len(Constraint) - 4:]
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else:
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Constraint = "1==1" # 没有上下限清�?
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Constraint += " and (" + condition + ")"
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Constraint = Constraint.replace("\n", " ")
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url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
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ItemsInfo, SamplingTimePeriods, Constraint, interval)
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response = requests.get(url)
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content = json.loads(response.text)
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data = np.array([item for item in content["ClearData"]]).T # test data
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time_data = np.array([item for item in content["TimeData"]]).reshape(data.shape[0], -1)
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try:
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data = np.array(info["data"])
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except KeyError:
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pass
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number_sample = info["number_sample"]
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number_fault_variable = info["number_fault_variable"]
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low_f = [float(item) for item in info["low_f"].split(',')]
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high_f = [float(item) for item in info["high_f"].split(',')]
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test_type = info["Test_Type"]
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limit_value = info["Limit_Value"]
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try:
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expand = info["expand"]
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# sort_data = info["sort_data"]
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except KeyError:
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expand = True
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# sort_data = True
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spe_recon = False
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result = pca(model, lock, data, number_sample, number_fault_variable, low_f, high_f, test_type,
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limit_value,time_data,spe_recon, expand)
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try:
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index = Test_Data["time"].index(",")
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result["time"] = Test_Data["time"][:index:]
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except KeyError:
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result["time"] = sorted(res["trainTime"], key=lambda item: item["st"])[0]["st"]
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result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
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result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
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result["amplitude"] = high_f
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result["CleanOrNot"] = True
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return result
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except Exception as e:
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msg = traceback.format_exc()
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result = [{"CleanOrNot": False, 'msg': msg}]
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return result
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def main(info):
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try:
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ItemsInfo, SamplingTimePeriods = [], []
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model_id = info["Model_id"]
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version = info["version"]
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if version == "v-test":
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res = PCA_Test.get_model_by_ID(model_id)
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else:
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res = PCA_Test.get_model_by_id_and_version(model_id, version)
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lock = []
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point_info = res["pointInfo"]
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interval = info["Test_Data"]["interval"]
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for i in range(len(point_info)):
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try:
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if point_info[i]["lock"]:
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lock.append(i)
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except:
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continue
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Test_Data = info["Test_Data"]
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points = Test_Data["points"].split(",")
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try:
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times = Test_Data["time"].split(";")
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for i in range(len(times)):
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Eachsampletime = {}
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timess = times[i].split(',')
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Eachsampletime["StartingTime"] = timess[0]
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Eachsampletime["TerminalTime"] = timess[1]
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SamplingTimePeriods.append(Eachsampletime)
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except KeyError:
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SamplingTimePeriods = [{"StartingTime": item["st"],
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"TerminalTime": item["et"]} for item in res["trainTime"]]
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model = res["para"]["Model_info"]
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condition = info["condition"].replace("=", "==").replace(">=", ">").replace("<=", "<")
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dead = info["dead"].split(',')
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limit = info["limit"].split(',')
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uplower = info["uplow"].split(';')
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count = 0
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Constraint = ""
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for i in range(len(points)):
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iteminfo = {}
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iteminfo["ItemName"] = points[i] # 加点
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if dead[i] == "1": # 判断是否参与死区清洗
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iteminfo["ClearDeadZone"] = "true"
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else:
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iteminfo["ClearDeadZone"] = "false"
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if limit[i] == "1": # 参与上下限清�?
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limits = uplower[i].split(',')
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if isnumber(limits): # 输入上下限正�?
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count += 1
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Constraint += "[" + points[i] + "]>" + limits[0] + " and " + "[" + points[i] + "]<" + limits[
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1] + " and "
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ItemsInfo.append(iteminfo)
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if count != 0:
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Constraint = Constraint[:len(Constraint) - 4:]
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|
else:
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|
Constraint = "1==1" # 没有上下限清�?
|
|
Constraint += " and (" + condition + ")"
|
|
Constraint = Constraint.replace("\n", " ")
|
|
url = f"http://{config._CLEAN_IP}/exawebapi/exatime/GetCleaningData?ItemsInfo=%s&SamplingTimePeriods=%s&Constraint=%s&SamplingPeriod=%s&DCount=6" % (
|
|
ItemsInfo, SamplingTimePeriods, Constraint, interval)
|
|
response = requests.get(url)
|
|
content = json.loads(response.text)
|
|
data = np.array([item for item in content["ClearData"]]).T # test data
|
|
time_data = np.array([item for item in content["TimeData"]]).reshape(data.shape[0], -1)
|
|
test_type = info["Test_Type"]
|
|
limit_value = info["Limit_Value"]
|
|
number_sample = info["number_sample"]
|
|
try:
|
|
expand = info["expand"]
|
|
# sort_data = info["sort_data"]
|
|
except KeyError:
|
|
expand = True
|
|
# sort_data = True
|
|
spe_recon = False
|
|
far, fdr, amplitude = [], [], []
|
|
recon = []
|
|
result = {}
|
|
for _, ass in enumerate(info["assess"]):
|
|
number_fault_variable = ass["number_fault_variable"]
|
|
low_f = [float(item) for item in ass["low_f"].split(',')]
|
|
high_f = [float(item) for item in ass["high_f"].split(',')]
|
|
result_temp = pca(model, lock, data, number_sample, number_fault_variable, low_f, high_f, test_type,
|
|
limit_value, time_data, spe_recon, expand)
|
|
t_index = np.nonzero(high_f)[0][0]#索引high_f中第一个非0数位置的行数；在矩阵的第3行，t_index == 2
|
|
far.append(result_temp["far_variable"][t_index])
|
|
fdr.append(result_temp["fdr_variable"][t_index])
|
|
recon.append(result_temp["Reconstruction_precision"][t_index])
|
|
amplitude.append(high_f[t_index])
|
|
try:
|
|
index = Test_Data["time"].index(",")
|
|
result["time"] = Test_Data["time"][:index:]
|
|
except KeyError:
|
|
result["time"] = sorted(res["trainTime"], key=lambda item: item["st"])[0]["st"]
|
|
result["BeforeCleanSamNum"] = content["BeforeCleanSamNum"]
|
|
result["AfterCleanSamNum"] = content["AfterCleanSamNum"]
|
|
result["amplitude"] = amplitude
|
|
result["CleanOrNot"] = True
|
|
result["far_variable"] = far
|
|
result["fdr_variable"] = fdr
|
|
result["Reconstruction_precision"] = recon
|
|
return result
|
|
except Exception as e:
|
|
msg = traceback.format_exc()
|
|
result = [{"CleanOrNot": False, 'msg': msg}]
|
|
return result #
|
|
|
|
|
|
if __name__ == "__main__":
|
|
info_str = {"expand":False,"Test_Data":{"time":"2021-07-14 15:41:04,2021-07-15 15:41:04","points":"DH4_40LCA30CT304,DH4_40LCA30CT303,DH4_40LCA10CT301,DH4_40LCJ50CT301,DH4_40LCA30CT305,DH4_40LCJ50AA101GT,DH4_40LCJ60CT301,DH4_40LCA70CT301,DH4_40LCA70CT302","interval":300000},"dead":"1,1,1,1,1,1,1,1,1","limit":"0,0,0,0,0,0,0,0,0","Model_id":"292","Model_alg":"PCA","number_sample":5000,"assess":[{"number_fault_variable":1,"low_f":"3.31,0,0,0,0,0,0,0,0","high_f":"3.31,0,0,0,0,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,2.78,0,0,0,0,0,0,0","high_f":"0,2.78,0,0,0,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,3.34,0,0,0,0,0,0","high_f":"0,0,3.34,0,0,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,3.44,0,0,0,0,0","high_f":"0,0,0,3.44,0,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,3.54,0,0,0,0","high_f":"0,0,0,0,3.54,0,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,0,4.08,0,0,0","high_f":"0,0,0,0,0,4.08,0,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,0,0,3.31,0,0","high_f":"0,0,0,0,0,0,3.31,0,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,0,0,0,2.75,0","high_f":"0,0,0,0,0,0,0,2.75,0"},{"number_fault_variable":1,"low_f":"0,0,0,0,0,0,0,0,3.05","high_f":"0,0,0,0,0,0,0,0,3.05"}],"condition":"[DH4_40DEH-01FU101]>300 and [DH4_40DEH-01FU101]<600 ","Test_Type":"FAI","Limit_Value":1.359,"uplow":"null,null;null,null;null,null;null,null;null,null;null,null;null,null;null,null;null,null","number":0,"version":"v-2020-09-03 18:03:22"}
|
|
res = main(info_str)
|
|
print("aaa")
|
|
|