Experimental study of a teeth flux sensor for detection, location and severity evaluation of induction machine stator faults

IF 1.6 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION Sensor Review Pub Date : 2024-03-26 DOI:10.1108/sr-11-2023-0618
Abdelmalek Saidoune, Hamza Houassine, Samir Bensaid, Nacera Yassa, Sadia Abbas
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Abstract

Purpose

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

Design/methodology/approach

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

Findings

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

Research limitations/implications

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

Practical implications

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

Originality/value

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.

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用于感应机定子故障检测、定位和严重性评估的齿通量传感器的实验研究
目的 本文旨在研究齿形磁通传感器在检测、定位和评估感应机定子绕组短路故障严重程度方面的功效。对机器诊断采取了两个关键步骤:在负载运行条件下进行测量,以检测故障;在空载条件下进行测量,以确定故障位置和严重程度。实验结果实验结果表明,所提出的使用齿通量传感器的方法在检测、定位和评估定子绕组故障严重程度方面是可靠和有效的。研究限制/意义虽然本研究侧重于短路故障,但未来的研究可以探索其他故障类型和替代传感器配置,以提高故障诊断的全面性。 原创性/价值本研究提出了一种使用齿通量传感器对感应机进行全面故障诊断的创新方法,为该领域做出了贡献。其独创性在于这种方法能有效提供可靠的故障检测、定位和严重性评估。
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来源期刊
Sensor Review
Sensor Review 工程技术-仪器仪表
CiteScore
3.40
自引率
6.20%
发文量
50
审稿时长
3.7 months
期刊介绍: Sensor Review publishes peer reviewed state-of-the-art articles and specially commissioned technology reviews. Each issue of this multidisciplinary journal includes high quality original content covering all aspects of sensors and their applications, and reflecting the most interesting and strategically important research and development activities from around the world. Because of this, readers can stay at the very forefront of high technology sensor developments. Emphasis is placed on detailed independent regular and review articles identifying the full range of sensors currently available for specific applications, as well as highlighting those areas of technology showing great potential for the future. The journal encourages authors to consider the practical and social implications of their articles. All articles undergo a rigorous double-blind peer review process which involves an initial assessment of suitability of an article for the journal followed by sending it to, at least two reviewers in the field if deemed suitable. Sensor Review’s coverage includes, but is not restricted to: Mechanical sensors – position, displacement, proximity, velocity, acceleration, vibration, force, torque, pressure, and flow sensors Electric and magnetic sensors – resistance, inductive, capacitive, piezoelectric, eddy-current, electromagnetic, photoelectric, and thermoelectric sensors Temperature sensors, infrared sensors, humidity sensors Optical, electro-optical and fibre-optic sensors and systems, photonic sensors Biosensors, wearable and implantable sensors and systems, immunosensors Gas and chemical sensors and systems, polymer sensors Acoustic and ultrasonic sensors Haptic sensors and devices Smart and intelligent sensors and systems Nanosensors, NEMS, MEMS, and BioMEMS Quantum sensors Sensor systems: sensor data fusion, signals, processing and interfacing, signal conditioning.
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