lstm轴承寿命预测

1.读取数据集

import numpy as np
import torch
from torch import nn
import matplotlib.pyplot as plt
import pandas as pd
from torch.autograd import Variable
all_date = pd.read_csv(u"D:/故障诊断数据集/西交轴承数据/XJTU-SY_Bearing_Datasets/Data/XJTU-SY_Bearing_Datasets/35Hz12kN/Bearing1_1/all.csv")
all_date['RUL'] = np.arange(0, 1,1/len(all_date))
all_dateNd = all_date.values

2.搭建lstm网络

class LstmRNN(nn.Module):
    """
        Parameters：
        - input_size: feature size
        - hidden_size: number of hidden units
        - output_size: number of output
        - num_layers: layers of LSTM to stack
    """
    def __init__(self, input_size, hidden_size=1, output_size=4, num_layers=1):
        super().__init__()
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers)  # utilize the LSTM model in torch.nn
        self.forwardCalculation = nn.Linear(hidden_size, output_size)
    def forward(self, _x):
        x, _ = self.lstm(_x)  # _x is input, size (seq_len, batch, input_size)
        s, b, h = x.shape  # x is output, size (seq_len, batch, hidden_size)
        x = x.view(s * b, h)
        x = self.forwardCalculation(x)
        x = x.view(s, b, -1)
        return x

3.数据处理和网络训练

if __name__ == '__main__':
    # create database
    data_len = len(all_dateNd)
    data_x = all_dateNd[:,0:2]    #二维输入
    data_y = all_dateNd[:,2]      #输出  
    dataset = np.zeros((data_len, 3))    #用一个dataset表示输入输出
    dataset[:, 0] = data_x[:,0]
    dataset[:, 1] = data_x[:,1]
    dataset[:, 2] = data_y
    dataset = dataset.astype('float32')
    # choose dataset for training and testing
    train_data_ratio = 0.1  # 数据量太大，仅使用10%做训练
    train_data_len = int(data_len * train_data_ratio)
    train_x = dataset[:train_data_len, 0:2]
    train_y = dataset[:train_data_len, 2]
    INPUT_FEATURES_NUM = 2            #输入是2维：水平和垂直振动信号
    OUTPUT_FEATURES_NUM = 1            #输出是一维：寿命
    # test_x = dataset[train_data_len:, 0:2]
    # test_y = dataset[train_data_len:, 2]
    # ----------------- train -------------------
    train_x_tensor = train_x.reshape(-1, 14, INPUT_FEATURES_NUM)  # 每14个做一个batch
    train_y_tensor = train_y.reshape(-1, 14, OUTPUT_FEATURES_NUM)  # set batch size to 14
    # transfer data to pytorch tensor
    train_x_tensor = torch.from_numpy(train_x_tensor)        #数组转tensor
    train_y_tensor = torch.from_numpy(train_y_tensor)
    train_x_tensor_GPU = train_x_tensor.cuda()               #用GPU训练
    train_y_tensor_GPU = train_y_tensor.cuda()
    DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")    #用GPU训练网络
    lstm_model = LstmRNN(INPUT_FEATURES_NUM, 16, output_size=OUTPUT_FEATURES_NUM, num_layers=1).to(DEVICE)  # 16 hidden units
    print('LSTM model:', lstm_model)
    print('model.parameters:', lstm_model.parameters)
    loss_function = nn.MSELoss()
    optimizer = torch.optim.Adam(lstm_model.parameters(), lr=0.01)
    max_epochs = 100000
    for epoch in range(max_epochs):
        output = lstm_model(train_x_tensor_GPU)
        loss = loss_function(output, train_y_tensor_GPU)
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
        if loss.item()  
4.绘图
 
predictive_y_for_training = lstm_model(train_x_tensor_GPU)
predictive_y_for_training = predictive_y_for_training.view(-1, OUTPUT_FEATURES_NUM).data.cpu().numpy()   #tensor转numpy数组
plt.figure()
plt.plot(train_y, 'b')
plt.plot(output, 'y--')
plt.plot(predictive_y_for_training, 'y--')
plt.show()