2020
DOI: 10.3390/e23010001
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Abstract: For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is … Show more

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Cited by 39 publications
(13 citation statements)
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References 135 publications
(128 reference statements)
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“…The way decisions are nowadays made in the industry is changing thanks to increasing data availability [19] and the development of intelligent tools for condition and fault assessment [20]. In commercial industrial gas turbines, data are captured by several sensors located along the machine and are then sent back to the engineering office where they are analyzed and processed to diagnose any unexpected event.…”
Section: Machine Learning Diagnosismentioning
confidence: 99%
“…The way decisions are nowadays made in the industry is changing thanks to increasing data availability [19] and the development of intelligent tools for condition and fault assessment [20]. In commercial industrial gas turbines, data are captured by several sensors located along the machine and are then sent back to the engineering office where they are analyzed and processed to diagnose any unexpected event.…”
Section: Machine Learning Diagnosismentioning
confidence: 99%
“…Several relevant proposals were recently published to design increasingly reliable monitoring systems [21] thanks to new signal-processing techniques such as linear prediction coefficients (LPC), mel-frequency cepstral coefficients (MFCC), and machine learning. Meiying et al [16] proposed to combine a CNN and LSTM for health assessment and failure diagnosis.…”
Section: Introductionmentioning
confidence: 99%
“…In traditional satellite fault detection methods, such as threshold-based methods [ 14 , 15 ] and model-based methods [ 16 , 17 ], the thresholds or the models required for fault detection necessitate manual setting. Therefore, the performance of these fault detection methods heavily relies on the experience of experts [ 18 ]. In recent years, data-driven fault detection methods have eliminated this heavy dependence on expert experience and become a popular research field [ 19 , 20 , 21 , 22 ].…”
Section: Introductionmentioning
confidence: 99%