轴承故障诊断python代码

以下是基于机器学习的轴承故障诊断Python代码示例：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

# 读取数据文件
data = pd.read_csv('bearing_failure.csv')

# 数据预处理
X = data.drop(['status'], axis=1)
y = data['status']

# 将数据集拆分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 构建随机森林分类器模型
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

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

# 输出模型的准确率和混淆矩阵
print('Accuracy score:', accuracy_score(y_test, y_pred))
print('Confusion matrix:', confusion_matrix(y_test, y_pred))
print('Classification report:', classification_report(y_test, y_pred))

需要注意的是，此示例代码仅为参考，实际应用中需要根据数据集的特点进行适当的调整和优化。