Real-Time Bearing Fault Diagnosis of Induction Motors with Accelerated Deep Learning Approach

Afrasiabi, Shahabodin; Afrasiabi, Mousa; Parang, Benyamin; Mohammadi, Mohammad

doi:10.1109/pedstc.2019.8697244

Cited by 38 publications

(29 citation statements)

References 14 publications

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“…Over the past three years, there has been a growing interest in DNN-based electrical machine damage detection systems noticed. For the most part, they are associated with the analysis of mechanical damage to the IM [35,36] or mechanical system components [37,38]. A small number of works related to electrical damage of IM mainly concern rotor damages [24,27].…”

Section: Introductionmentioning

confidence: 99%

“…An important aspect in the use of DNN in diagnostic processes is the appropriate adjustment of the measured signal to the structure and properties of the network. The input vector of DNNs can result from the signal analysis [24,25] and the directly provided diagnostic signal [35,46]. Due to the principle of operation of deep learning structures, in most cases the measured signal is converted into a 2D [25,48,52] or 3D [51] matrix.…”

Section: Introductionmentioning

confidence: 99%

“…The idea of the CNN-based diagnostic system that performs the task of the fault detecting and assessing the degree of damage of the IM stator windings is described. Unlike the methods presented in [35,46] based on an easy to analyze vibration signal for the detection of mechanical damages, the developed method consists of the direct processing of the phase current signals through a DNN, which is a novelty in the detection systems of stator faults. It should be emphasized that the development of a diagnostic system based on direct analysis of the stator current is associated with additional difficulties, due to the fact that the increase in the number of shorted turns causes a similar effect as an increase in the load torque, i.e., an increase in stator current amplitude.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…As a result, the latest FDD systems demand more artificial intelligent solutions to incorporate multiple fault events or dynamically changing load profiles in case of incomplete or noisy measurements [44][45][46][47][48][49]. Commonly, the diagnosis and predictions are calculated through motor current signature analysis (MCSA) [50,51], i.e., examining the output signals of the motor stator's current while running on a steady-state operating mood [52][53][54][55][56]. MCSA analyses the time-frequency decomposition of the current signals or by faults' frequencies in the frequency domain.…”

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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“…Commonly, the diagnosis and predictions is calculated through current signature analysis (MCSA) [50,51], i.e., examining the output signals of the motor stator's current while running on a steady-state operating mood [52][53][54][55][56]. MCSA analyses the timefrequency decomposition of the current signals or by faults' frequencies in the frequency domain.…”

Section: Introductionmentioning

confidence: 99%

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

Dineva¹,

Mosavi²,

Gyimesi³

et al. 2019

Preprint

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Primary importance is devoted to Fault Detection and Diagnosis (FDI) of electrical machine and drive systems 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. The contribution of this work is to propose a novel methodology using multi-label classification method for simultaneously diagnosing multiple faults and evaluating the fault severity under noisy conditions. Performance of various multi-label classification models are 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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Real-Time Bearing Fault Diagnosis of Induction Motors with Accelerated Deep Learning Approach

Cited by 38 publications

References 14 publications

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

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

Fault Diagnosis of Rotating Electrical Machines Using Multi-Label Classification

Multi-Label Classification for Fault Diagnosis of Rotating Electrical Machines

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