Role of artificial intelligence in rotor fault diagnosis: a comprehensive review
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Role of artificial intelligence in rotor fault diagnosis: a comprehensive review Aneesh G. Nath1 · Sandeep S. Udmale2 · Sanjay Kumar Singh1
© Springer Nature B.V. 2020
Abstract Artificial intelligence (AI)-based rotor fault diagnosis (RFD) poses a variety of challenges to the prognostics and health management (PHM) of the Industry 4.0 revolution. Rotor faults have drawn more attention from the AI research community in terms of utilizing fault-specific characteristics in its feature engineering, compared to any other rotating machinery faults. While the rotor faults, specifically structural rotor faults (SRF), have proven to be the root cause of most of the rotating machinery issues, the research in this field largely revolves around bearing and gear faults. Within this scenario, this paper is the first of its kind to attempt to review and define the role of AI in RFD and provides an all-encompassing review of rotor faults for the researchers and academics. In addition, this study is unique in three ways: (i) it emphasizes the use of fault-specific characteristic features with AI, (ii) it is grounded in fault-wise analysis rather than component-wise analysis with appropriate fault categorization, and (iii) it portrays the current research and analysis in accordance with different phases of an AI-based RFD framework. Finally, the section on future research directions is aimed at bridging the gap between a laboratory-based solution and a real-world industrial solution for RFD. Keywords Rotating machinery fault diagnosis · Structural rotor faults · Artificial intelligence · Machine health monitoring
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s1046 2-020-09910-w) contains supplementary material, which is available to authorized users. * Aneesh G. Nath [email protected] Sandeep S. Udmale [email protected] Sanjay Kumar Singh [email protected] 1
Department of Computer Science and Engineering, Indian Institute of Technology (BHU), Varanasi, Uttar Pradesh 221005, India
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Department of Computer Engineering and IT, Veermata Jijabai Technological Institute (VJTI), Mumbai, Maharashtra 400019, India
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1 Introduction Industry 4.0 is bolstered by the benefits of self-configuration, self-optimization, early awareness, decision making, and predictive maintenance capabilities (Qin et al. 2016). In order to accelerate the transition from the conventional manufacturing systems to one that caters to the contemporary industrial revolution, the cyber-physical system (CPS) (Mosterman and Zander 2016) has been integrated into Industry 4.0. In CPS, the physical machine working environment is virtually modeled by incorporating sensors in the machine components. These technical advancements significantly contributed to the emergence of the discipline known as prognostics and health management (PHM) as an indispensable arm of Industry 4.0 (Lee et al. 2018). Failure detection and predictive maintenance (FDPM) is a v
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