Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Lee, Jong‐Hyun; Pack, Jae-Hyung; Lee, In-Soo

doi:10.3390/app9152950

Cited by 62 publications

(48 citation statements)

References 17 publications

(19 reference statements)

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“…In [54], authors used a vibration signal and CNN for fault detection and diagnosis, achieving accuracy between 88-99% for different ratios of data. Another method proposed in [55] attained 98% and 100% accuracy for detecting rotor fault and bearing fault respectively when using a CNN. In comparison with these recent studies, our proposed method produced a comparable result using only statistical features and GA for feature extraction and selection leading to less computational complexity than the other methods.…”

Section: Resultsmentioning

confidence: 99%

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Toma

Prosvirin

Kim

2020

Sensors

148

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Efficient fault diagnosis of electrical and mechanical anomalies in induction motors (IMs) is challenging but necessary to ensure safety and economical operation in industries. Research has shown that bearing faults are the most frequently occurring faults in IMs. The vibration signals carry rich information about bearing health conditions and are commonly utilized for fault diagnosis in bearings. However, collecting these signals is expensive and sometimes impractical because it requires the use of external sensors. The external sensors demand enough space and are difficult to install in inaccessible sites. To overcome these disadvantages, motor current signal-based bearing fault diagnosis methods offer an attractive solution. As such, this paper proposes a hybrid motor-current data-driven approach that utilizes statistical features, genetic algorithm (GA) and machine learning models for bearing fault diagnosis. First, the statistical features are extracted from the motor current signals. Second, the GA is utilized to reduce the number of features and select the most important ones from the feature database. Finally, three different classification algorithms namely KNN, decision tree, and random forest, are trained and tested using these features in order to evaluate the bearing faults. This combination of techniques increases the accuracy and reduces the computational complexity. The experimental results show that the three classifiers achieve an accuracy of more than 97%. In addition, the evaluation parameters such as precision, F1-score, sensitivity, and specificity show better performance. Finally, to validate the efficiency of the proposed model, it is compared with some recently adopted techniques. The comparison results demonstrate that the suggested technique is promising for diagnosis of IM bearing faults.

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Section: Resultsmentioning

confidence: 99%

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Toma

Prosvirin

Kim

2020

Sensors

148

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“…Ishikawa and Igarashi [48] analyzed the demagnetization of a permanent magnet synchronous motor using finite element analysis. Glowacz [48] applied the acoustics analysis and Lee et al [49] adopted the deep learning for motor failure detection.…”

Section: Contentmentioning

confidence: 99%

Fault Diagnosis of Rotating Machine

Królczyk

Antonino-Daviu

2020

Applied Sciences

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“…A small number of works related to electrical damage of IM mainly concern rotor damages [24,27]. Most DNN-based systems use vibration measurements [39][40][41], less frequently stator currents [42][43][44] and voltages [45]. This fact results from clear changes occurring in the diagnostic signal, due to a mechanical damage and the resulting simplicity of the signal analysis.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

et al. 2020

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In this paper, the idea of using a convolutional neural network (CNN) for the detection and classification of induction motor stator winding faults is presented. The diagnosis inference of the stator inter-turn short-circuits is based on raw stator current data. It offers the possibility of using the diagnostic signal direct processing, which could replace well known analytical methods. Tests were carried out for various levels of stator failures. In order to assess the sensitivity of the applied CNN-based detector to motor operating conditions, the tests were carried out for variable load torques and for different values of supply voltage frequency. Experimental tests were conducted on a specially designed setup with the 3 kW induction motor of special construction, which allowed for the physical modelling of inter-turn short-circuits in each of the three phases of the machine. The on-line tests prove the possibility of using CNN in the real-time diagnostic system with the high accuracy of incipient stator winding fault detection and classification. The impact of the developed CNN structure and training method parameters on the fault diagnosis accuracy has also been tested.

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Fault Diagnosis of Induction Motor Using Convolutional Neural Network

Cited by 62 publications

References 17 publications

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers

Fault Diagnosis of Rotating Machine

Convolutional Neural Network-Based Stator Current Data-Driven Incipient Stator Fault Diagnosis of Inverter-Fed Induction Motor

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