Background

An induction motor is at the heart of every rotating machine and hence it is a very vital component. Almost in every industry, around 90% of the machines apply an induction motor as a prime mover. It is a very important driving unit of the machine. Hence, it is necessary to monitor its condition to avoid any catastrophic failure and stoppage of production. The breakdown of the induction motor would not be affordable due to remarkable financial loss, unpredicted shutdown, and the associated repair cost.

Purpose

Vibration is a manifestation of induction motor due to the issues in alignment, balancing, and clearances. Bearing, the most vulnerable to failure due to continuous working under fatigue loading leads to defects. These defects cause changes in the vibration signature over time. The vibration monitoring techniques helps to effectively diagnose mechanical faults such as bearing defect and stator rotor rub. The purpose of this review paper is to summarize the major faults in induction motor, recent diagnostics methods augmented with advanced signal processing techniques, and real-life applications in electric vehicles. It also discusses possible research gaps and opportunities to contribute based on the review findings in the field of condition monitoring.

Methods

This article presents a detailed review of recent trends in the research of condition monitoring and fault diagnosis of the induction motor. The emphasis is given on the major faults in the induction motor covering time-domain, frequency-domain, and time–frequency domain methods along with an application of artificial intelligence techniques for fault detection.

Review Factor

This article presents a comprehensive review of literature which highlights the development and new propositions by researchers in the field of diagnostic techniques for the different faults of induction motor in the last decade. Researchers documented applications of the different conventional methods, advanced signal processing techniques, and soft computing techniques for fault identification of induction motor. This review is carried out for fault identification of induction motor used in machines in general and in particular for identifying the faults in an induction motor of an electric vehicle. A dedicated discussion on the review findings, research gaps, future trends in the field of condition monitoring of induction motor is presented. Condition monitoring of the induction motor in an electric vehicle is also discussed in this paper.

Conclusions

It is observed that the vibration-based techniques are reported to be effective for the identification of mechanical faults while motor current signature analysis is effective for electrical fault in an induction motor. The review presented to analyze the suitability of various condition monitoring techniques for the induction motor fault identification in general and particularly its application in an electric vehicle. It is observed that the diagnosis of faults at the incipient level without using the signal processing technique is challenging. Fault diagnosis of induction motor has witnessed the changes from traditional diagnosis techniques to advanced techniques with a hybrid application of signal processing and artificial intelligence techniques. Still, there is a potential of improvement in reliability, efficiency, robustness, computational time, and real-time diagnostics of faults in IM.

中文翻译：

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感应电动机的状态监测与故障诊断

背景

感应电动机是每台旋转机器的核心，因此它是至关重要的组件。几乎在每个行业中，大约90％的机器都将感应电动机用作原动机。这是机器非常重要的驱动单元。因此，有必要监视其状况，以免发生任何灾难性的故障和生产中断。由于显着的经济损失，意外的停机以及相关的维修费用，感应电动机的故障将无法承受。

目的

由于对准，平衡和间隙问题，振动是感应电动机的一种表现。由于在疲劳载荷下连续工作而最容易发生故障的轴承会导致缺陷。这些缺陷会导致振动信号随时间变化。振动监测技术有助于有效诊断机械故障，例如轴承缺陷和定子转子摩擦。本文的目的是总结感应电动机的主要故障，采用先进信号处理技术的最新诊断方法以及电动汽车的实际应用。它还根据状态监测领域的审查结果，讨论了可能的研究差距和贡献机会。

方法

本文详细介绍了感应电动机状态监测和故障诊断研究的最新趋势。重点介绍了感应电动机中的主要故障，包括时域，频域和时频域方法，以及人工智能技术在故障检测中的应用。

审查因素

本文对文献进行了全面的回顾，突出了研究人员在过去十年中针对感应电动机不同故障的诊断技术领域的发展和新主张。研究人员记录了不同的常规方法，先进的信号处理技术和软计算技术在感应电动机故障识别中的应用。为了通常用于机器中的感应电动机的故障识别，尤其是为了识别电动车辆的感应电动机的故障，进行了这种审查。提出了关于感应电动机状态监测领域的综述，研究差距，未来趋势的专门讨论。本文还讨论了电动汽车中感应电动机的状态监测。

结论

据观察，据报道，基于振动的技术对于识别机械故障是有效的，而电动机电流信号分析对于感应电动机中的电气故障是有效的。这篇综述旨在分析各种状态监测技术对于感应电动机故障识别的适用性，尤其是其在电动汽车中的应用。可以看出，在不使用信号处理技术的情况下，在初期进行故障诊断是一项挑战。感应电动机的故障诊断见证了从传统的诊断技术到先进技术的转变，信号处理和人工智能技术相结合。尽管如此，在可靠性，效率，鲁棒性，