A review on fault detection and diagnosis techniques: basics and beyond
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A review on fault detection and diagnosis techniques: basics and beyond Anam Abid1 · Muhammad Tahir Khan1 · Javaid Iqbal2 Accepted: 27 October 2020 © Springer Nature B.V. 2020
Abstract Safety and reliability are absolutely important for modern sophisticated systems and technologies. Therefore, malfunction monitoring capabilities are instilled in the system for detection of the incipient faults and anticipation of their impact on the future behavior of the system using fault diagnosis techniques. In particular, state-of-the-art applications rely on the quick and efficient treatment of malfunctions within the equipment/system, resulting in increased production and reduced downtimes. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. The review presents both traditional model-based and relatively new signal processingbased FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. Typical steps involved in the design and development of automatic FDD system, including system knowledge representation, data-acquisition and signal processing, fault classification, and maintenance related decision actions, are systematically presented to outline the present status of FDD. Future research trends, challenges and prospective solutions are also highlighted. Keywords Automatic fault diagnosis · Fault detection · Industrial applications · Signal processing
* Anam Abid [email protected] Muhammad Tahir Khan [email protected] Javaid Iqbal [email protected] 1
Department of Mechatronics Engineering, University of Engineering and Technology, Peshawar, Pakistan
2
Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, NUST, Rawalpindi, Pakistan
13
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A. Abid et al.
1 Introduction Real world applications; such as robotics, autonomous vehicles, surveillance, remote exploration systems, search and rescue, household robots, intelligent manufacturing and transportation systems; are considered safety-critical as any system failure may result in consequences regarding human safety and infrastructure loss. Traditionally, fault detection and diagnosis (FDD) schemes have been an essential attribute associated with safety-critical applications. However, due to the demands of increased production and reliable operation, FDD is incorporated in several modern and sophisticated systems/equipment also. Advanced design requires a comprehensive overall FDD system for quick and efficient treatment of malfunctions (Abid et al. 2018; Bolchini et al. 2015). A desirable FDD system has the traits of monitoring overall system health (Abid et al. 2017), coping with diverse system malfunctions (Shao et al. 2018), identifying and locating faults for the safe removal of faulty components within the system (McDonald and Fulton 2005). In the last three decades, a lot of work has been done on FDD and various techniques have been developed (Benbouzid et al. 1999; Blö
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