Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: A state-of-the-art review. (October 2020)

• Record Type: Journal Article
• Authors: Gangsar, Purushottam; Tiwari, Rajiv
• Is Part Of: Mechanical systems and signal processing. Volume 144(2020)
• Publication Date: 2020-10
• Subjects: Induction motor (IM) -- Mechanical and electrical faults -- Vibration and current signal -- Multi-fault diagnostic -- Machine learning algorithms; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.106908
• Languages: English
• ISSNs: 0888-3270

Abstract

Graphical abstract: Highlights: Review on induction motor fault diagnosis. Signal based mechanical and electrical fault diagnosis. Application of machine learning algorithms. Abstract: Uninterrupted and trouble-free operation of induction motors (IMs) is the compulsion of the modern industries. Firstly, the paper reviews the conventional time and spectrum signal analyses of two most effective type of signals, i.e. the vibration and the current for various IM faults. The vibration and the current signal analyses (time and spectral) is performed using the signals measured from different faulty IMs from a laboratory setup. Subsequently, the advantages and difficulties associated with these conventional procedures are discussed. Next, this paper presents and summarizes the existing research and development in the field of signal based automation of condition monitoring methodologies for the fault detection and diagnosis of various electrical and mechanical faults of IMs. Nowadays, artificial intelligent (AI) methods are being employed for the IM and other machine fault diagnosis. Advancements of the AI based fault diagnosis including the popular approaches are reviewed in details. These techniques are being integrated with traditional monitoring techniques. The AI based fault monitoring and detection techniques for IMs published up to 2000 are briefly described, however, more attention is paid to the techniques that are introduced in roughly past two decades, i.e. during 2000–2019. In overall, this paper includes review of system signals, conventional and advance signal processing techniques; however, it mainly covers, the selection of effective statistical features, AI methods, and associated training and testing strategies for fault diagnostics of IMs. Finally, dedicated discussions on the recent developments, research gaps and future scopes in the fault monitoring and diagnosis of IMs are added.