Incipient Inter-turn Fault Diagnosis in Induction motors using CNN and LSTM based Methods

Khan, Tarannum; Pyla, Alekhya; Seshadrinath, Jeevanand

doi:10.1109/ias.2018.8544707

Cited by 21 publications

(19 citation statements)

References 15 publications

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“…The standardization or z-scoring will be done by the mean and standard deviation. Standardization will be done using Equations (11) and (12) from [27].…”

Section: Feature Standardizationmentioning

confidence: 99%

“…For standardization, the mean and standard deviation will be used to standardize all of the signals in the training dataset. This was done using calculations from Equations (11) and (12). The results are given below:…”

Section: Training With Feature Extraction and Standardizationmentioning

confidence: 99%

“…Unfortunately, the method proposed can hardly be applied in real conditions as there is no long term monitoring, the motor is working fine, but the error is induced instantly in the experiments. There is some research like [12], where authors detect inter-turn faults for induction motors in general. The authors used a more complex LSTM network, which was four layers deep with a Softmax layer at the end and 1000 hidden units in each of the LSTM layers.…”

Section: Introductionmentioning

confidence: 99%

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Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

et al. 2020

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In this research, electric motors faults and their identification is reviewed. Brushless direct-current (BLDC) motors stator fault identification using long short-term memory neural networks were analyzed. A proposed method of vibration data acquisition using cloud technologies with high accuracy, feature extraction using spectral entropy, and instantaneous frequency and standardization using mean and standard deviation was reviewed. Additionally, model training with raw and standardized data was compared. A total model accuracy of 97.10 percent was achieved. The proposed methods could successfully identify the motor stator status from normal, to loss of stator winding imminent and arcing, and lastly to open circuit in stator winding—motor needing to stop immediately—by using gathered data from real experiments, training the model and testing it theoretically.

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“…The standardization or z-scoring will be done by the mean and standard deviation. Standardization will be done using Equations (11) and (12) from [27].…”

Section: Feature Standardizationmentioning

confidence: 99%

Section: Training With Feature Extraction and Standardizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

et al. 2020

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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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“…Therefore, CNN is capable of working with the raw data. Several studies have utilized the CNN in detecting inter-turn fault in induction and PMSM motors [32]- [35], while no work is found in the literature for using CNN in LSPMSM. The performance of CNN in detecting inter-turn fault was compared with the Recurrent Neural Network (RNN), the Support Vector Machine (SVM), RBNN and MLFFNN.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

et al. 2020

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Stator inter-turn fault diagnosis system for electric motors is of a considerable concern due to its significant effect on industrial production. In this paper, a new method for detecting the inter-turn fault and quantifying its severity in the line start permanent magnet synchronous motor (LSMPSM) is proposed. The new method depends on monitoring the stator current during steady-state period to detect the fault. The convolutional neural network (CNN) method is proposed to correlate the motor steady-state current with the status of the motor winding conditions and detect any presence of inter-turn faults. The data used in this study is extracted from both an experimental setup of a one-horsepower LSPMSM and the corresponding verified mathematical model through several testing cases under various loading conditions. One of the main features of the proposed technique is that it does not require separate feature extraction phase. The results indicate that the proposed technique is able to detect the inter-turn fault under different loading conditions varies from 0NM to 4NM with accuracy of 97.75% for all defined fault levels. The use of steady-state current for fault detection regardless of motor load enables the proposed technique to detect the fault online without disturbing the system functionality and reliability as well as without adding any extra hardware to the system. INDEX TERMS Convolutional neural network (CNN), diagnosis, fault detection, inter-turn fault, LSPMSM.

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Incipient Inter-turn Fault Diagnosis in Induction motors using CNN and LSTM based Methods

Cited by 21 publications

References 15 publications

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

Application of Advanced Vibration Monitoring Systems and Long Short-Term Memory Networks for Brushless DC Motor Stator Fault Monitoring and Classification

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

Convolutional Neural Network-Based Inter-Turn Fault Diagnosis in LSPMSMs

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