gru轴承故障诊断代码

以下是一个使用GRU模型进行轴承故障诊断的代码示例：

import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import GRU, Dense, Dropout
from tensorflow.keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler

# 读取数据
data = pd.read_csv('bearing_data.csv')

# 数据预处理
scaler = MinMaxScaler(feature_range=(-1, 1))
data_scaled = scaler.fit_transform(data)

# 定义参数
lookback = 50
n_features = 1
batch_size = 32
epochs = 100

# 创建训练数据集
X = []
y = []
for i in range(lookback, len(data_scaled)):
    X.append(data_scaled[i-lookback:i])
    y.append(data_scaled[i])
X, y = np.array(X), np.array(y)

# 划分训练集和测试集
train_size = int(len(data_scaled) * 0.8)
X_train, X_test = X[:train_size], X[train_size:]
y_train, y_test = y[:train_size], y[train_size:]

# 构建GRU模型
model = Sequential()
model.add(GRU(50, return_sequences=True, input_shape=(lookback, n_features)))
model.add(GRU(50))
model.add(Dropout(0.2))
model.add(Dense(1))
model.compile(optimizer='adam', loss='mse')

# 训练模型
model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_data=(X_test, y_test))

# 预测测试集
y_pred = model.predict(X_test)

# 反归一化
y_pred = scaler.inverse_transform(y_pred)
y_test = scaler.inverse_transform(y_test)

# 计算R2得分
from sklearn.metrics import r2_score
print(r2_score(y_test, y_pred))

这个代码演示了如何使用GRU模型进行轴承故障诊断，其中使用了归一化和反归一化来处理数据，使用R2得分来评估模型的性能。请注意，这只是一个示例代码，实际应用时需要根据具体情况进行修改和优化。