![]() J Low Freq Noise Vib Active Control 35(1):52–63Ībaei G, Selamat A (2014) A survey on software fault detection based on different prediction approaches. ![]() Future research trends, challenges and prospective solutions are also highlighted.Ībad MRAA, Moosavian A, Khazaee M (2016) Wavelet transform and least square support vector machine for mechanical fault detection of an alternator using vibration signal. 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. The review presents both traditional model-based and relatively new signal processing-based FDD approaches, with a special consideration paid to artificial intelligence-based FDD methods. This paper presents developments within Fault Detection and Diagnosis (FDD) methods and reviews of research work in this area. 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. ![]() 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. Safety and reliability are absolutely important for modern sophisticated systems and technologies. ![]()
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