基于lightgbm hyperopt的旋转机械故障诊断(Python)

发布于:2024-06-29 ⋅ 阅读:(59) ⋅ 点赞:(0)

前置文章:

将一维机械振动信号构造为训练集和测试集(Python)

https://mp.weixin.qq.com/s/DTKjBo6_WAQ7bUPZEdB1TA

旋转机械振动信号特征提取(Python)

https://mp.weixin.qq.com/s/VwvzTzE-pacxqb9rs8hEVw

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.colors import ListedColormap
import matplotlib.patches as mpatches
import lightgbm as lgb
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
df_train = pd.read_csv("statistics_10_train.csv" , sep = ',')
df_test = pd.read_csv("statistics_10_test.csv" , sep = ',')
X_train = df_train[['Kurtosis', 'Impulse factor', 'RMS', 'Margin factor', 'Skewness',
               'Shape factor', 'Peak to peak', 'Crest factor']].values
y_train = df_train['Tipo'].values
X_test = df_test[['Kurtosis', 'Impulse factor', 'RMS', 'Margin factor', 'Skewness',
               'Shape factor', 'Peak to peak', 'Crest factor']].values
y_test = df_test['Tipo'].values
from hyperopt import fmin, atpe, tpe, STATUS_OK, STATUS_FAIL, Trials
from hyperopt import hp
from hyperopt import space_eval
class HPOpt(object):


    def __init__(self, x_train, x_test, y_train, y_test):
        self.x_train = x_train
        self.x_test  = x_test
        self.y_train = y_train
        self.y_test  = y_test


    def process(self, fn_name, space, trials, algo, max_evals):
        fn = getattr(self, fn_name)
        try:
            result = fmin(fn=fn, space=space, algo=algo, max_evals=max_evals, trials=trials)
        except Exception as e:
            return {'status': STATUS_FAIL,
                    'exception': str(e)}
        return result, trials


    def lgb_clas(self, para):
        clf = lgb.LGBMClassifier(**para['clas_params'])
        return self.train_clf(clf, para)
    
    def train_clf(self, clf, para):
        clf.fit(self.x_train, self.y_train,
                eval_set=[(self.x_train, self.y_train), (self.x_test, self.y_test)], 
                verbose = False, early_stopping_rounds = 20)
        pred = clf.predict(self.x_test)
        loss = para['loss_func'](self.y_test, pred)
        return {'loss': loss, 'status': STATUS_OK}
from sklearn.metrics import accuracy_score
lgb_clas_params = {
    'learning_rate':    hp.choice('learning_rate',    np.arange(0.001, 0.5, 0.001)),
    'max_depth':        hp.choice('max_depth',        np.arange(5, 10, 1, dtype=int)),
    'min_child_weight': hp.choice('min_child_weight', np.arange(0, 10, 1)),
    'min_data_in_leaf': hp.choice('min_data_in_leaf', np.arange(0, 10, 1)),
    'subsample':        hp.choice('subsample',        np.arange(0.1, 1, 0.05)),
    'n_estimators':     hp.choice('n_estimators',     np.arange(10, 200, 10, dtype=int)),
    'num_leaves':       hp.choice('num_leaves',       np.arange(5, 51, 1, dtype=int)),
    }


lgb_para = dict()
lgb_para['clas_params'] = lgb_clas_params
lgb_para['loss_func' ] = lambda y, pred: accuracy_score(y, pred)# squared = False)
lgb_para["max_evals"] = 100
# Optimización 
obj = HPOpt(X_train, X_test, y_train, y_test)


lgb_opt = obj.process(fn_name='lgb_clas', space=lgb_para, trials=Trials(), algo=tpe.suggest, max_evals=lgb_para["max_evals"])
parametros = space_eval(lgb_clas_params, lgb_opt[0])
clf = lgb.LGBMClassifier()
clf.set_params(**parametros) 
clf.fit(X_train, y_train)
LGBMClassifier(learning_rate=0.342, max_depth=9, min_child_weight=0,
               min_data_in_leaf=7, n_estimators=90, num_leaves=33,
               subsample=0.15000000000000002)
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
pred = clf.predict(X_test)
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred))
[[27  3  0]
 [ 0 30  0]
 [ 0  1 29]]
              precision    recall  f1-score   support

       Inner       1.00      0.90      0.95        30
       Outer       0.88      1.00      0.94        30
        Sano       1.00      0.97      0.98        30

    accuracy                           0.96        90
   macro avg       0.96      0.96      0.96        90
weighted avg       0.96      0.96      0.96        90
clf = lgb.LGBMClassifier(n_estimators = 100, learning_rate = 0.01, min_data_in_leaf = 0)
clf.fit(X_train, y_train)
pred = clf.predict(X_test)
target_names = ['Inner', 'Outer', 'Healthy']
print(confusion_matrix(y_test, pred))
print(classification_report(y_test, pred, target_names = target_names))
[[29  1  0]
 [ 0 30  0]
 [ 0  3 27]]
              precision    recall  f1-score   support

       Inner       1.00      0.97      0.98        30
       Outer       0.88      1.00      0.94        30
     Healthy       1.00      0.90      0.95        30

    accuracy                           0.96        90
   macro avg       0.96      0.96      0.96        90
weighted avg       0.96      0.96      0.96        90
pred_train = clf.predict(X_train)
print(confusion_matrix(y_train, pred_train))
print(classification_report(y_train, pred_train, target_names = target_names))

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工学博士,担任《Mechanical System and Signal Processing》《中国电机工程学报》《控制与决策》等期刊审稿专家,擅长领域:现代信号处理,机器学习,深度学习,数字孪生,时间序列分析,设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。


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