Fault diagnosis and investigation techniques for induction motor

Almounajjed, Abdelelah; Sahoo, Ashwin Kumar; Kumar, Mani Kant; Assaf, T.

doi:10.1080/01430750.2021.2016483

Cited by 16 publications

(9 citation statements)

References 183 publications

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“…The ITSC fault was simulated using the Clarks-Concordia transformation via Matlab/Simulink. To simulate the incipient fault, Phase-C turns are gradually shorted by (1,3,5,7,10,13,15,17,20 The detailed coefficients at level 7 were discriminated, and the statistical parameters, L 1 and L 2 norms, were calculated from the coefficients for the three phases. The acquisition time was 6 minutes.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Phase-C is considered the faulty phase, which was taken earlier for Simulink. The machine winding was modified and specially wound by adding 17 tappings on the stator coils of Phase C such that the number of defective turns is (1,3,5,7,10,13,15,17,20,23,25,35,45,55,65 an ITSC fault with various numbers of turns in the faulty phase, as depicted in Fig. 12.…”

Section: Resultsmentioning

confidence: 99%

“…Induction motors (IMs) with capacities ranging from a few watts to megawatts are employed as leading movers, and they play an essential role in recent industries. Owing to their robustness, reliability, and low maintenance costs, IMs have received increasing attention in automotive, electrical vehicle traction, and power conversion systems [1][2][3] . Despite its favorable characteristics, the induction motor is subjected to mechanical and electrical faults that can be caused by prolonged operation, current and voltage imbalance, and harsh operating conditions.…”

Section: Introductionmentioning

confidence: 99%

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Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Almounajjed

Sahoo

Kumar

et al. 2023

Chin. J. Electr. Eng.

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A novel approach by introducing a statistical parameter to estimate the severity of incipient stator inter-turn short circuit (ITSC) faults in induction motors (IMs) is proposed. Determining the incipient ITSC fault and its severity is challenging for several reasons. The stator currents in the healthy and faulty cases are highly similar during the primary stage of the fault. Moreover, the conventional statistical parameters resulting from the analysis of fault signals do not consistently show a systematic variation with respect to the increase in fault intensity. The objective of this study is the early detection of incipient ITSC faults. Furthermore, it aims to determine the percentage of shorted turns in the faulty phase, which acts as an indicator for severe damage to the stator winding.Modeling of the motor in healthy and defective cases is performed using the Clarke Concordia transform. A discrete wavelet transform is applied to the motor currents using a Daubechies-8 wavelet. The statistical parameters L 1 and L 2 norms are computed for the detailed coefficients. These parameters are obtained under a variety of loads and defects to acquire the most accurate and generalized features related to the fault. Combining L 1 and L 2 norms creates a novel statistical parameter with notable characteristics to achieve the research aim. An artificial neural network-based back propagation algorithm is employed as a classifier to implement the classification process. The classifier output defines the percentage of defective turns with a high level of accuracy. The competency of the adopted methodology is validated via simulations and experiments. The results confirm the merits of the proposed method, with a classification test correctness of 95.29%.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Almounajjed

Sahoo

Kumar

et al. 2023

Chin. J. Electr. Eng.

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“…Such systems do not meet modern operating requirements in built-in diagnostic transport systems for work under loading and can be used for bench tests. Currently, the most common following methods of general diagnostics of electric motors are known [20][21][22]: -based on the analysis of the electrical properties of the machine; -control of a condition of insulation; -based on the analysis of vibrations of individual elements of the unit; -based on the analysis of acoustic vibrations generated by a working machine; based on the measurement and analysis of the magnetic flux in the motor gap and the external magnetic field; on the measurement and analysis of the temperature of individual elements of the machine.…”

Section: Development Of the Structural Diagram Of The Diagnostics Sys...mentioning

confidence: 99%

Structural diagram of the built-in diagnostic system for electric drives of vehicles

Gubarevych¹,

Goolak²,

Melkonova³

et al. 2022

Diagnostyka

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Currently, in transport systems, as part of the main and auxiliary equipment, a large number of induction motors with a squirrel-cage rotor of different capacities are used. Their wide application in the transport industry is associated with the main advantages over other types of machinesa fairly high reliability, low cost and ease of maintenance. However, during the operation of these motors, a number of malfunctions can occur that affect the deterioration of the performance of the entire drive, the accuracy of its functions, or accelerate an emergency stop. To ensure proper control of the technical condition of electric motors, modern diagnostic systems are required that operate in real-time and operational loading mode with the transmission of data on the instantaneous state of the main control elements. The paper proposes a block diagram of the diagnostic built-in system and developed a modular unit for it to set the type and degree of the most complex damageinter-turn short circuit in the stator winding.

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“…Hence, several harmonic components are generated in the current that are dependable on the loading condition of the motor. These harmonics are expressed in equation (1) (Almounajjed et al, 2021b; Baruti et al, 2020; Sonje et al, 2019) as…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

Almounajjed¹,

Sahoo²

2022

Transactions of the Institute of Measurement and Control

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This work proposes a novel online detection scheme to diagnose incipient inter-turn short circuit fault and estimate the failure severity in induction motor. Incipient detection of the stator failure during the machine running, as well as identification of its intensity can reduce the risk of additional damage to the phase winding, improve the operational efficiency, and ensure machine availability. Hence, the incipient fault diagnosis provides a safe operating area for the motor. This work aims to specify the percentage of defective turns in the shorted winding by proposing a new mathematical parameter based on wavelet analysis, in addition to employ a multi-class support vector machine to perform the classification task. Discrete Wavelet Transform is used to analyze the stator currents after modeling the motor utilizing Clarke-Concordia transformation. From the detailed coefficients, Max and L2 norms are calculated. The adopted parameter is computed depending on the previous norms, which form the input vector to feed the classifier. The multi-class support vector machine–based one versus one algorithm is used to determine accurately the defect intensity. The acquired outcomes prove that the proposed approach, depending on the novel parameter along with multi-class support vector machine can give a robust and accurate indication about the machine status, which enables the estimation of the fault severity. To verify the competency of the methodology, various hardware experiments are carried out on the motor. The experimental results demonstrate the validity and practicability of the method, with a higher level of correctness, exceeding 96%.

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Fault diagnosis and investigation techniques for induction motor

Cited by 16 publications

References 183 publications

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Stator Fault Diagnosis of Induction Motor Based on Discrete Wavelet Analysis and Neural Network Technique

Structural diagram of the built-in diagnostic system for electric drives of vehicles

Wavelet-based multi-class support vector machine for stator fault diagnosis in induction motor

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