Feature selection and classification of mechanical fault of an induction motor using random forest classifier

Patel, Raj Kumar; Giri, V. K.

doi:10.1016/j.pisc.2016.04.068

Cited by 77 publications

(33 citation statements)

References 10 publications

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“…This led to the modification of the existing commercial ADXL 335 accelerometer. The required band width is obtained by removing existing capacitor and replacing it with a new capacitor of value 0.03 µF in series [20]. The vibration signals were acquired at 10k samples/sec for ten seconds.…”

Section: Modification Of Adxl335 Accelerometer For Band Width Selectionmentioning

confidence: 99%

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

Kumar¹,

Tjprc²

2019

IJMPERD

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A sudden bearing failure leads to fatal accidents and results in loss of human life and increases the down time of the machine. Neural Network and Support Vector Machines (SVM) are widely used in rotating machinery fault diagnosis, while Random Forest (RF) based on the ensemble learning method, is relatively unknown in this field. Currently, use of Micro Electro Mechanical Sensors (MEMS) for machinery fault diagnosis is receiving more attention as it is low cost, compact and portable. In this paper, vibration signals are collected from the Rolling Element Bearing (REB) using the MEMS sensor. Statistical features have been extracted from the wavelet packet coefficients of the vibration signals and used as input feature for the classification purpose. A framework for the comparison of RF and SVM is presented to identify the best classifier for bearing fault diagnosis. RF emerged as the best classifier based on the classification accuracy especially with a small training set leading to a promising tool for bearing fault diagnosis.

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Section: Modification Of Adxl335 Accelerometer For Band Width Selectionmentioning

confidence: 99%

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

Kumar¹,

Tjprc²

2019

IJMPERD

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show abstract

“…The proposed method also employs vibration and current signals and achieves nice performance; however, the method lacks the flexibility to easily fit with specific types of machines. Random Forest (RF) classifier was developed for multi-class bearing faults in [91]. This work also employs input features extracted from vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

et al. 2019

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Fault Detection and Diagnosis of electrical machine and drive systems are of utmost importance in modern industrial automation. The widespread use of Machine Learning techniques has made it possible to replace traditional motor fault detection techniques with more efficient solutions that are capable of early fault recognition by using large amounts of sensory data. However, the detection of concurrent failures is still a challenge in the presence of disturbing noises or when the multiple faults cause overlapping features. Multi-label classification has recently gained popularity in various application domains as an efficient method for fault detection and monitoring of systems with promising results. The contribution of this work is to propose a novel methodology for multi-label classification for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. In this research, the Electrical Signature Analysis as well as traditional vibration data have been considered for modeling. Furthermore, the performance of various multi-label classification models is compared. Current and vibration signals are acquired under normal and fault conditions. The applicability of the proposed method is experimentally validated under diverse fault conditions such as unbalance and misalignment.

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“…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. 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].…”

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%

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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Feature selection and classification of mechanical fault of an induction motor using random forest classifier

Cited by 77 publications

References 10 publications

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

An Experimental Comparison of Random Forest and Support Vector Machine for Bearing Fault Diagnosis, A Micro Electro Mechanical Sensor Approach

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

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

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