Application of Random Forest Algorithm in Machine Fault Diagnosis

Han, Xiao; Yang, Bo‐Suk; Lee, Soo-Jong

doi:10.1007/978-1-84628-814-2_82

Cited by 6 publications

(12 citation statements)

References 3 publications

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“…Fault analysis in induction motors has been widely applied. MCSA has been used to analyze faults in induction motors, such as rotor faults, bearing faults, eccentricity, misalignment, and stator faults [7][8][9][10][11]. Similar techniques have also been used to analyze vibration [12][13][14][15][16] and acoustic [17] signals of induction motors.…”

Section: Introductionmentioning

confidence: 99%

“…Similar techniques have also been used to analyze vibration [12][13][14][15][16] and acoustic [17] signals of induction motors. The limitation of prior work is that most fault analysis has been applied to induction motors, electrical motors, fans, and gear boxes [7][8][9][10][11][12][13][14][15][16][17]. Yet, fault analysis in LS-PMSMs has been limited to a smaller set of faults, such as rotor faults, static eccentricity faults, and demagnetization [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

“…The rotor faults in our LS-PMSM machine were created by physically damaging the rotors of the LS-PMSM. We also analyzed the LS-PMSM starting with an initial load, as opposed to introducing a load after the motor had reached steady state, which is the common practice in prior work [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…That is, we started the motor from standstill and waited for the motor to reach steady state, with the current from this transient period used for our analysis. Prior research uses the current from the steady state for fault analysis [7][8][9][10][11]. Finally, our third contribution is that our work is the first to apply machine learning for rotor fault detection in LS-PMSMs.…”

Section: Introductionmentioning

confidence: 99%

“…To train these machine learning algorithms, we extracted thirteen time domain features from the transient current signal of the LS-PMSM, with the selection of the features based on prior work [22,23]. While machine learning methods have been used for fault detection in induction motors [7][8][9][10][11], to the best of our knowledge this is the first work to present fault analysis in LS-PMSMs by comparing various machine learning algorithms and using features extracted from the transient current signal to train and test these algorithms.…”

Section: Introductionmentioning

confidence: 99%

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Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Abstract-This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naïve Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault detection of broken rotor bar in LS-PMSM using random forests

et al. 2018

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Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

et al. 2015

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This paper addresses the development of a random forest classifier for the multi-class fault diagnosis in spur gearboxes. The vibration signal's condition parameters are first extracted by applying the wavelet packet decomposition with multiple mother wavelets, and the coefficients' energy content for terminal nodes is used as the input feature for the classification problem. Then, a study through the parameters' space to find the best values for the number of trees and the number of random features is performed. In this way, the best set of mother wavelets for the application is identified and the best features are selected through the internal ranking of the random forest classifier. The results show that the proposed method reached 98.68% in classification accuracy, and high efficiency and robustness in the models.

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Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review

Paturi

Cheruku

2021

Materials Today: Proceedings

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Application of Random Forest Algorithm in Machine Fault Diagnosis

Cited by 6 publications

References 3 publications

Fault detection of broken rotor bar in LS-PMSM using random forests

Fault detection of broken rotor bar in LS-PMSM using random forests

Fault diagnosis of spur gearbox based on random forest and wavelet packet decomposition

Application and performance of machine learning techniques in manufacturing sector from the past two decades: A review

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