基于机器学习的轴承故障检测分析代码

基于机器学习的轴承故障检测通常涉及到数据分析、特征提取和模型训练的过程。以下是一个简化的示例流程：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

# 1. 数据预处理
data = pd.read_csv('bearing_data.csv')  # 假设数据来源于CSV文件
X = data.iloc[:, :-1]  # 特征数据
y = data['fault_label']  # 目标标签，0表示正常，1表示故障

# 2. 数据划分
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 3. 特征缩放
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# 4. 模型训练
clf = RandomForestClassifier(n_estimators=100, random_state=42)  # 使用随机森林分类器
clf.fit(X_train_scaled, y_train)

# 5. 预测及评估
y_pred = clf.predict(X_test_scaled)
print(classification_report(y_test, y_pred))

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