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Fault diagnosis of induction motors rotor using current signature with different signal processing techniques

Guezam Abdelhak, Bessedik Sid Ahmed, Djekidel Rabah

Diagnostyka

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2022

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Vol. 23, No. 2

art. no. 2022201

EN

The popularity of asynchronous machines, particularly squirrel cage machines, stems from their inexpensive production costs, resilience, and low maintenance requirements. Unfortunately, potential flaws in these devices might have a negative impact on the facility's profitability and service quality. As a result, diagnostic tools for detecting flaws in these types of devices must be developed. Asynchronous machine problems can be diagnosed using a variety of methods. Signal processing techniques based on extracting information from characteristic quantities of electrical machine operation can provide highly useful information about flaws. The purpose of this research is to develop efficient algorithms based on numerous signal processing approaches for correctly detecting asynchronous cage machine rotor defects (rotor bar ruptures).

2

Optimization of induced voltage on buried pipeline from HV power lines using grasshopper algorithm (GOA)

Bouallag Khadidja, Djekidel Rabah, Bessedik Sid Ahmed

Diagnostyka

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2021

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Vol. 22, No. 2

105--115

EN

The buried metallic pipeline which parallels to the HV power line is subject to induced voltages from the AC currents flowing in the conductors, these voltages can affect the operating personnel, pipeline associated equipment, and the pipeline integrity. This paper analyses the induced voltage and current on the buried pipeline running parallel to HV power lines. It also presents an optimization procedure of different parameters that affect the level of the induced voltage in the pipeline during normal operating conditions. A comparison study between the proposed optimization algorithms (GOA, GE, DE and PSO) is done with a maximization of a given objective function. The simulation results establish that the GOA algorithm provides a faster convergence and better solution than the other optimization algorithms. Thus, the statistical analysis according to Friedman’s rank test confirmed the superiority of this proposed algorithm. Furthermore, the results show that the parameters optimization of the metallic pipeline is an effective approach to provide the best performance for mitigation which is generally sufficient to reduce the induced voltage experienced by the buried metallic pipeline to enforce the safety limit.

3

Detection of partial rotor bar rupture of a cage induction motor using least square support vector machine approach

Birame M’hamed, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

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2021

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Vol. 22, No. 1

57--63

EN

Squirrel cage induction motors suffer from numerous faults, for example cracks in the rotor bars. This paper aims to present a novel algorithm based on Least Squares Support Vector Machine (LS-SVM) for detection partial rupture rotor bar of the squirrel cage asynchronous machine. The stator current spectral analysis based on FFT method is applied in order to extract the fault frequencies related to rotor bar partial rupture. Afterward the LS-SVM approach is established as monitoring system to detect the degree of rupture rotor bar. The training and testing data sets used are derived from the spectral analysis of one stator phase current, containing information about characteristic harmonics related to the partial rupture rotor bar. Satisfactory and more accurate results are obtained by applying LS-SVM to fault diagnosis of rotor bar.

4

Least square support vectors machines approach to diagnosis of stator winding short circuit fault in induction motor

Birame M’hamed, Taibi Djamel, Bessedik Sid Ahmed, Benkhoris Mohamed Fouad

Diagnostyka

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2020

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Vol. 21, No. 4

35--41

EN

Various approaches have been proposed to monitor the state of machines by intelligent techniques such as the neural network, fuzzy logic, neuro-fuzzy, pattern recognition. However, the use of LS-SVM. This article presents an automatic computerized system for the diagnosis and the monitoring of faults between turns of the stator in IM applying the LS-SVM least square support vector machine. in this study for the detection of short circuit faults in the stator winding of the induction motor. Since it requires a mathematical model suitable for modelling defects, a defective IM model is presented. The proposed method uses the stator current as input and at the output decides the state of the motor, indicating the severity of the short-circuit fault.