python的机器人工具包：Python环境下基于机器学习的往复式压缩机故障识别

python的机器人工具包：Python环境下基于机器学习的往复式压缩机故障识别Classifier: SVC- rbf Gamma= 0.003211151769260453 PenaltyPrameter= 3.2794598503610994 Test Acuuracy= 0.9583333333333334Classifier: SVC- poly Gamma= 0.02472261261696812 PenaltyPrameter= 8.084311259878596 Test Acuuracy= 0.875import time import pandas as pd import numpy as np import matplotlib.pyplot as plt MaxExpNo=10 counter=-1 #标签 labels=['Bearing' 'Flywheel' 'Healt

本项目使用声信号检测往复式压缩机的 7 种故障，分别为出口阀泄漏Leakage Outlet Valve（LOV） 入口阀泄露Leakage Inlet Valve(LIV)，止逆阀泄露Non-Return Valve(NRV)，轴承损伤Bearing，惯性轮损伤Flywheel，活塞损伤Piston，皮带损伤Riderbelt和健康状态。经过特征提取后，利用各种机器学习算法对往复式压缩机故障进行分类。

所测得的声信号如下

以Flywheel为例

接下来开始步入正题，由于代码包含众多模块，为了避免篇幅过长，只写主函数。提取的特征可以选择维数是否约简，分类器也可选择是否进行参数优化（粒子群算法，pip install pyswarm），代码必要的标注都有了，也比较容易看懂。

import time import pandas as pd import numpy as np import matplotlib.pyplot as plt MaxExpNo=10 counter=-1 #标签 labels=['Bearing' 'Flywheel' 'Healthy' 'LIV' 'LOV' 'NRV' 'Piston' 'Riderbelt'] import PrimaryStatFeatures #特征提取模块 import FFT_Module #FFT变换 #所要提取的特征 data_columns_PrimaryStatFeatures=['Mean' 'Min' 'Max' 'StdDv' 'RMS' 'Skewness' 'Kurtosis' 'CrestFactor' 'ShapeFactor'] data_columns_Target=['Fault'] Faults={labels[0]:int(0) labels[1]:int(1) labels[2]:int(2) labels[3]:int(3) labels[4]:int(4) labels[5]:int(5) labels[6]:int(6) labels[7]:int(7)} for label in labels: for ExpNo in range(1 MaxExpNo 1): counter =1 file='Data\\' label '\\preprocess_Reading' str(ExpNo) '.txt' X=np.loadtxt(file delimiter=' ') if (counter==0): print('Lading files: ' str(counter/(len(labels)*MaxExpNo)*100) '% completed') StatFeatures=PrimaryStatFeatures.PrimaryFeatureExtractor(X) FFT_Features data_columns_FFT_Features=FFT_Module.FFT_BasedFeatures(X) data_columns=data_columns_PrimaryStatFeatures data_columns_FFT_Features data_columns_Target if (label==labels[0] and ExpNo==1): data=pd.DataFrame(columns=data_columns) StatFeatures[0].extend(FFT_Features) StatFeatures[0].extend([Faults[label]]) df_temp=pd.DataFrame(StatFeatures index=[counter] columns=data_columns) data=data.append(df_temp) input_data=data.drop(columns=['Fault']) #数据标准化处理 #参考: http://benalexkeen.com/feature-scaling-with-scikit-learn/ from sklearn import preprocessing normalization_status='RobustScaler' ''' Choices: 1. Normalization 2. StandardScaler 3. MinMaxScaler 4. RobustScaler 5. Normalizer 6. WithoutNormalization ''' input_data_columns=data_columns_PrimaryStatFeatures data_columns_FFT_Features if (normalization_status=='Normalization'): data_array=preprocessing.normalize(input_data norm='l2' axis=0) input_data=pd.DataFrame(data_array columns=input_data_columns) elif (normalization_status=='StandardScaler'): scaler = preprocessing.StandardScaler() scaled_df = scaler.fit_transform(input_data) input_data = pd.DataFrame(scaled_df columns=input_data_columns) elif (normalization_status=='MinMaxScaler'): scaler = preprocessing.MinMaxScaler() scaled_df = scaler.fit_transform(input_data) input_data = pd.DataFrame(scaled_df columns=input_data_columns) elif (normalization_status=='RobustScaler'): scaler = preprocessing.RobustScaler() scaled_df = scaler.fit_transform(input_data) input_data = pd.DataFrame(scaled_df columns=input_data_columns) elif (normalization_status=='Normalizer'): scaler = preprocessing.Normalizer() scaled_df = scaler.fit_transform(input_data) input_data = pd.DataFrame(scaled_df columns=input_data_columns) elif (normalization_status=='WithoutNormalization'): print ('No normalization is required') target_data=pd.DataFrame(data['Fault'] columns=['Fault'] dtype=int) DimReductionStatus=False if (DimReductionStatus==True): for nComponents in range(1 110): #降维 #主成分分析 from sklearn import decomposition pca = decomposition.PCA(n_components=nComponents) pca.fit(input_data) input_data_reduced = pca.transform(input_data) #训练集和测试集划分 from sklearn.model_selection import train_test_split x_train x_test y_train y_test=train_test_split(input_data_reduced target_data test_size=0.3 random_state=42 stratify=target_data) #使用 KNN（K 最近邻）训练 import KNN_Classifier test_accuracy_max=KNN_Classifier.KNNClassifier(x_train x_test y_train y_test) plt.figure(10) plt.scatter(nComponents test_accuracy_max) plt.xlabel('Number of utilized components based on PCA') plt.ylabel('Test Accuracy') #使用 SVC（支持向量分类器）进行训练 import SVC_Classifier test_accuracy_max=SVC_Classifier.SVCClassifier(x_train x_test y_train y_test) plt.figure(11) plt.scatter(nComponents test_accuracy_max) else: from sklearn.model_selection import train_test_split x_train x_test y_train y_test=train_test_split(input_data target_data test_size=0.3 random_state=42 stratify=target_data) #使用决策树进行训练 import DT_Classifier DT_Classifier.DTClassifier(x_train x_test y_train y_test) from sklearn.neural_network import MLPClassifier from sklearn.neighbors import KNeighborsClassifier from sklearn.svm import SVC from sklearn.gaussian_process import GaussianProcessClassifier from sklearn.gaussian_process.kernels import rbf from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier AdaBoostClassifier from sklearn.naive_bayes import GaussianNB from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis #写入最佳分类器参数 file1 = open('Optimized Parameters for Classifiers.txt' 'w') print('\nThis file contains the output optimized parameters for different classifiers\n\n\n' file=file1) #分类器参数优化 #************************************* SVMOptStatus=True KNNOptStatus=True MLPOptStatus=True DTOptStatus=True import CLFOptimizer #pip install pyswarm #优化 SVC 参数 if (SVMOptStatus==True): SVM_kernels = ['linear' 'poly' 'rbf' 'sigmoid'] for KernelType in SVM_kernels: print('\n\nstage: Optimizing SVC:' KernelType) SVCParams_opt SVCAccuracy_opt=CLFOptimizer.SVCOPT(KernelType x_train x_test y_train y_test) print('\nClassifier: SVC-' KernelType ' Gamma=' SVCParams_opt[0] ' PenaltyPrameter=' SVCParams_opt[1] ' Test Acuuracy=' SVCAccuracy_opt file=file1) #优化 KNN 参数 if (KNNOptStatus==True): print('\n\nstage: Optimizing KNN') KNNParams_opt KNNAccuracy_opt=CLFOptimizer.KNNOPT(x_train x_test y_train y_test) print('\nClassifier: KNN n_neighbors=' KNNParams_opt ' Test Acuuracy=' KNNAccuracy_opt file=file1) #优化 MLP 分类器 if (MLPOptStatus==True): print('\n\nstage: Optimizing MLP') MLPParams_opt MLPAccuracy_opt=CLFOptimizer.MLPOPT(x_train x_test y_train y_test) print('\nClassifier: MLP hidden_layer_sizes=(' MLPParams_opt[0] ' ' MLPParams_opt[1] ' ' MLPParams_opt[2] ') Test Acuuracy=' MLPAccuracy_opt file=file1) #优化决策树分类器 if (DTOptStatus==True): print('\n\nstage: Optimizing Decision Tree') DTParams_opt DTAccuracy_opt=CLFOptimizer.DTOPT(x_train x_test y_train y_test) print('\nClassifier: Decision Tree max_depth=' DTParams_opt[0] ' min_samples_split=' DTParams_opt[1] ' min_samples_leaf=' DTParams_opt[2] ' Test Acuuracy=' DTAccuracy_opt file=file1) file1.close() #生成分类器名称及其配置 classifiers=[] CLFnames=[] CLFnames= CLFnames ["SVC-linear" "K-Nearest Neighbors" "Multi-Layer Perceptron" "Decision Tree" "Random Forest" "Gaussian Process" "AdaBoost" "Naive Bayes" "QDA"] #classifiers=classifiers [ # SVC(kernel='linear' gamma=1.785 C=3.463) # KNeighborsClassifier(n_neighbors=3) # MLPClassifier(hidden_layer_sizes=(28 34 80 ) alpha=1) # DecisionTreeClassifier() # RandomForestClassifier() # GaussianProcessClassifier(1.0 * RBF(1.0)) # AdaBoostClassifier() # GaussianNB() # QuadraticDiscriminantAnalysis()] classifiers=classifiers [ SVC() KNeighborsClassifier() MLPClassifier() DecisionTreeClassifier() RandomForestClassifier() GaussianProcessClassifier() AdaBoostClassifier(DecisionTreeClassifier() n_estimators=1000 learning_rate=1) GaussianNB() QuadraticDiscriminantAnalysis()] #将分类结果写入文件 f = open('ClassificationResults.txt' 'w') print('\nThis file contains an overall comparison of different classifiers performance\n\n\n' file=f) import ClassificationModule ClassificationModule.Classifiers(CLFnames classifiers x_train x_test y_train y_test)

分类器优化参数结果

This file contains the output optimized parameters for different classifiers

Classifier: SVC- linear Gamma= 1.61614372336136 PenaltyPrameter= 6.455613795161285 Test Acuuracy= 1.0

Classifier: SVC- poly Gamma= 0.02472261261696812 PenaltyPrameter= 8.084311259878596 Test Acuuracy= 0.875

Classifier: SVC- rbf Gamma= 0.003211151769260453 PenaltyPrameter= 3.2794598503610994 Test Acuuracy= 0.9583333333333334

Classifier: SVC- sigmoid Gamma= 0.001 PenaltyPrameter= 9.44412249946835 Test Acuuracy= 1.0

Classifier: KNN n_neighbors= 3 Test Acuuracy= 0.875

Classifier: MLP hidden_layer_sizes=( 19.0 97.0 14.0 ) Test Acuuracy= 1.0

Classifier: Decision Tree max_depth= 2121.0 min_samples_split= 6.0 min_samples_leaf= 4.0 Test Acuuracy= 1.0

Training accuracy for SVC-linear is: 0.9642857142857143 and Prediction accuracy is: 0.875

Training accuracy for K-Nearest Neighbors is: 0.8928571428571429 and Prediction accuracy is: 0.7916666666666666

分类结果

Training accuracy for Multi-Layer Perceptron is: 1.0 and Prediction accuracy is: 0.9583333333333334

Training accuracy for Decision Tree is: 1.0 and Prediction accuracy is: 0.7916666666666666

Training accuracy for Random Forest is: 1.0 and Prediction accuracy is: 0.9583333333333334

Training accuracy for Gaussian Process is: 1.0 and Prediction accuracy is: 0.8333333333333334

Training accuracy for AdaBoost is: 1.0 and Prediction accuracy is: 0.9583333333333334

Training accuracy for Naive Bayes is: 1.0 and Prediction accuracy is: 0.9166666666666666

Training accuracy for QDA is: 1.0 and Prediction accuracy is: 0.3333333333333333

实际上，工业上大部分还是基于频谱或者3西格玛准则之类的方法对部件进行监测诊断，简单方便可解释性强。

完整的数据及代码见如下链接

https://mianbaoduo.com/o/bread/Y5eclpxr