轴承剩余寿命预测python

以下是一个使用Python实现轴承剩余寿命预测的示例代码：

import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

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

# 特征工程
data['acc_rms'] = np.sqrt(np.mean(data[['ax', 'ay', 'az']] ** 2, axis=1))
data['vel_rms'] = np.sqrt(np.mean(data[['bx', 'by', 'bz']] ** 2, axis=1))
data['temp'] = data['temp'] + 273.15

# 特征选择
features = ['acc_rms', 'vel_rms', 'temp']
X = data[features]
y = data['remaining_life']

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 随机森林模型
rf = RandomForestRegressor(n_estimators=100, random_state=42)
rf.fit(X_train, y_train)

# 模型评估
y_pred_train = rf.predict(X_train)
rmse_train = np.sqrt(mean_squared_error(y_train, y_pred_train))
print('Train RMSE:', rmse_train)

y_pred_test = rf.predict(X_test)
rmse_test = np.sqrt(mean_squared_error(y_test, y_pred_test))
print('Test RMSE:', rmse_test)

# 预测剩余寿命
X_new = np.array([[0.095, 0.201, 296.15]])
y_new = rf.predict(X_new)
print('New remaining life:', y_new)

该示例代码使用随机森林模型对轴承的剩余寿命进行预测。首先对原始数据进行特征工程，包括计算加速度和速度的RMS（均方根）值，以及将温度单位转换为开氏度。然后选择特征，划分训练集和测试集，构建随机森林模型，并对模型进行评估。最后，通过输入新的特征数据，即加速度RMS、速度RMS和温度，来预测轴承的剩余寿命。