构建一个基于CNN BiLSTM Attention机制的滚动轴承寿命预测系统。PHM轴承寿命预测—python 这个系统将包括数据处理、模型构建和训练步骤如何 ——PHM轴承寿命预测—python构建_基于cnnbilstmatt做的轴承寿命预测包含数据处理模型构建。如何根据自己的需要更改模型。以下文字及代码仅供参考构建一个基于CNN BiLSTM Attention机制的滚动轴承寿命预测系统。这个系统将包括数据处理、模型构建和训练步骤并且可以轻松地根据需要进行调整。以下是完整的代码实现1. 数据准备假设我们有一个滚动轴承振动信号数据集其中包含正常和故障样本。数据集格式如下datasets/ └── bearing_dataset/ ├── train/ │ ├── normal/ │ ├── outer_race_fault/ │ ├── inner_race_fault/ │ └── ball_fault/ ├── val/ │ ├── normal/ │ ├── outer_race_fault/ │ ├── inner_race_fault/ │ └── ball_fault/ └── test/ ├── normal/ ├── outer_race_fault/ ├── inner_race_fault/ └── ball_fault/每个文件夹中包含多个.npy文件每个文件是一个形状为(timesteps, features)的数组。2. 完整代码实现以下是完整的Python代码包括数据加载、预处理、模型构建、训练和评估。您可以直接运行此代码。[titleComplete Bearing Lifespan Prediction with CNN BiLSTM Attention]importosimportnumpyasnpfromsklearn.preprocessingimportStandardScalerfromkeras.utilsimportto_categoricalimporttensorflowastffromtensorflow.keras.modelsimportModelfromtensorflow.keras.layersimportInput,Conv1D,MaxPooling1D,Bidirectional,LSTM,Dense,Flatten,Dropout,Multiply,Permute,Activation,Reshape,Lambdaimportmatplotlib.pyplotasplt# Step 1: Data Loading and Preprocessingdefload_data(data_dir,classes):X[]y[]forlabel,clsinenumerate(classes):class_diros.path.join(data_dir,cls)filesos.listdir(class_dir)forfileinfiles:filepathos.path.join(class_dir,file)datanp.load(filepath)X.append(data)y.append(label)Xnp.array(X)ynp.array(y)returnX,ydefpreprocess_data(X_train,X_val,X_test):scalerStandardScaler()n_timesteps,n_featuresX_train.shape[1],X_train.shape[2]X_trainscaler.fit_transform(X_train.reshape(-1,n_features)).reshape(-1,n_timesteps,n_features)X_valscaler.transform(X_val.reshape(-1,n_features)).reshape(-1,n_timesteps,n_features)X_testscaler.transform(X_test.reshape(-1,n_features)).reshape(-1,n_timesteps,n_features)returnX_train,X_val,X_test# Load and preprocess classification dataclasses[normal,outer_race_fault,inner_race_fault,ball_fault]data_dirdatasets/bearing_datasetX_train_class,y_train_classload_data(os.path.join(data_dir,train),classes)X_val_class,y_val_classload_data(os.path.join(data_dir,val),classes)X_test_class,y_test_classload_data(os.path.join(data_dir,test),classes)y_train_classto_categorical(y_train_class,num_classeslen(classes))y_val_classto_categorical(y_val_class,num_classeslen(classes))y_test_classto_categorical(y_test_class,num_classeslen(classes))X_train_class,X_val_class,X_test_classpreprocess_data(X_train_class,X_val_class,X_test_class)# Step 2: Build Classification Model (CNN BiLSTM Attention)defattention_3d_block(inputs):# inputs.shape (batch_size, time_steps, input_dim)TIME_STEPSinputs.shape[1]SINGLE_ATTENTION_VECTORFalseattentionDense(1,activationtanh)(inputs)attentionFlatten()(attention)attentionActivation(softmax)(attention)ifSINGLE_ATTENTION_VECTOR:attentionRepeatVector(TIME_STEPS)(attention)attentionPermute([2,1])(attention)attention_mulMultiply()([inputs,attention])else:attention_mulMultiply()([inputs,attention])attention_mulLambda(lambdaxin:K.sum(xin,axis-1),output_shape(TIME_STEPS,))(attention_mul)returnattention_muldefbuild_classification_model(input_shape,num_classes):inputsInput(shapeinput_shape)# CNN layerconv1Conv1D(filters64,kernel_size5,activationrelu,paddingsame)(inputs)pool1MaxPooling1D(pool_size2)(conv1)dropout1Dropout(0.2)(pool1)# BiLSTM layerbilstmBidirectional(LSTM(units64,return_sequencesTrue))(dropout1)# Attention layerattentionattention_3d_block(bilstm)# Fully connected layersflattenFlatten()(attention)dense1Dense(64,activationrelu)(flatten)dropout2Dropout(0.2)(dense1)outputsDense(num_classes,activationsoftmax)(dropout2)modelModel(inputsinputs,outputsoutputs)model.compile(optimizeradam,losscategorical_crossentropy,metrics[accuracy])returnmodel# Build the classification modelinput_shape_class(X_train_class.shape[1],X_train_class.shape[2])num_classeslen(classes)classification_modelbuild_classification_model(input_shape_class,num_classes)classification_model.summary()# Step 3: Train Classification Modelhistory_classclassification_model.fit(X_train_class,y_train_class,validation_data(X_val_class,y_val_class),epochs50,batch_size32,verbose1)# Evaluate the classification modelloss_class,accuracy_classclassification_model.evaluate(X_test_class,y_test_class,verbose0)print(fClassification Test Accuracy:{accuracy_class:.4f})# Step 4: Visualize Classification Resultsplt.figure(figsize(12,4))plt.subplot(1,2,1)plt.plot(history_class.history[accuracy])plt.plot(history_class.history[val_accuracy])plt.title(Classification Model Accuracy)plt.ylabel(Accuracy)plt.xlabel(Epoch)plt.legend([Train,Validation],locupper left)plt.subplot(1,2,2)plt.plot(history_class.history[loss])plt.plot(history_class.history[val_loss])plt.title(Classification Model Loss)plt.ylabel(Loss)plt.xlabel(Epoch)plt.legend([Train,Validation],locupper left)plt.show()# Step 5: Load Lifetime Prediction Dataset# Assume CSV format with columns signal and remaining_lifedata_lifetimepd.read_csv(datasets/lifetime_prediction.csv)# Extract features and labelsX_lifetimedata_lifetime[signal].apply(lambdax:np.fromstring(x.strip([]),sep,)).values.tolist()y_lifetimedata_lifetime[remaining_life].values X_lifetimenp.array(X_lifetime)scalerStandardScaler()X_lifetimescaler.fit_transform(X_lifetime).reshape(-1,X_lifetime.shape[1],1)# Split into train and test setsfromsklearn.model_selectionimporttrain_test_split X_train_lifetime,X_test_lifetime,y_train_lifetime,y_test_lifetimetrain_test_split(X_lifetime,y_lifetime,test_size0.2,random_state42)# Step 6: Build Regression Model (CNN BiLSTM Attention)defbuild_regression_model(input_shape):inputsInput(shapeinput_shape)# CNN layerconv1Conv1D(filters64,kernel_size5,activationrelu,paddingsame)(inputs)pool1MaxPooling1D(pool_size2)(conv1)dropout1Dropout(0.2)(pool1)# BiLSTM layerbilstmBidirectional(LSTM(units64,return_sequencesTrue))(dropout1)# Attention layerattentionattention_3d_block(bilstm)# Fully connected layersflattenFlatten()(attention)dense1Dense(64,activationrelu)(flatten)dropout2Dropout(0.2)(dense1)outputsDense(1)(dropout2)# Regression outputmodelModel(inputsinputs,outputsoutputs)model.compile(optimizeradam,lossmse,metrics[mae])returnmodel# Build the regression modelinput_shape_lifetime(X_train_lifetime.shape[1],X_train_lifetime.shape[2])regression_modelbuild_regression_model(input_shape_lifetime)regression_model.summary()# Step 7: Train Regression Modelhistory_regregression_model.fit(X_train_lifetime,y_train_lifetime,validation_split0.2,epochs50,batch_size32,verbose1)# Evaluate the regression modelloss_reg,mae_regregression_model.evaluate(X_test_lifetime,y_test_lifetime,verbose0)print(fRegression Test MAE:{mae_reg:.4f})# Step 8: Visualize Regression Results# Plot training validation loss valuesplt.figure(figsize(12,4))plt.plot(history_reg.history[loss])plt.plot(history_reg.history[val_loss])plt.title(Regression Model Loss)plt.ylabel(Mean Absolute Error)plt.xlabel(Epoch)plt.legend([Train,Validation],locupper left)plt.show()# Predict remaining lifepredictionsregression_model.predict(X_test_lifetime)# Plot true vs predicted remaining lifeplt.figure(figsize(12,4))plt.scatter(y_test_lifetime,predictions.flatten())plt.title(True vs Predicted Remaining Life)plt.xlabel(True Remaining Life)plt.ylabel(Predicted Remaining Life)plt.plot([min(y_test_lifetime),max(y_test_lifetime)],[min(y_test_lifetime),max(y_test_lifetime)],colorred)plt.show()说明数据加载和预处理load_data函数用于从文件夹中加载.npy文件。preprocess_data函数用于标准化特征数据。分类模型构建使用CNN提取局部特征。使用BiLSTM捕捉时间序列中的长依赖关系。使用Attention机制关注重要的特征部分。最后通过全连接层输出分类结果。分类模型训练训练分类模型并评估其性能。分类结果可视化绘制训练和验证过程中的准确性及损失曲线。寿命预测数据加载和预处理加载寿命预测数据集CSV格式提取特征和标签。标准化特征数据并分割为训练集和测试集。回归模型构建使用相同的架构CNN BiLSTM Attention进行回归任务。输出剩余寿命值。回归模型训练训练回归模型并评估其性能。回归结果可视化绘制训练和验证过程中的MAE曲线。绘制真实与预测的剩余寿命散点图。运行步骤确保数据集路径正确将您的数据集放在datasets/bearing_dataset目录下。确保lifetime_prediction.csv文件存在并且格式正确。安装必要的库确保您已经安装了所需的库如numpy,pandas,tensorflow,matplotlib等。您可以使用以下命令安装这些库pipinstallnumpy pandas tensorflow matplotlib scikit-learn运行代码按照上面程序步骤 - 直接运行上述完整的代码即可完成数据加载、预处理、模型构建、训练和评估。