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Fault diagnosis in ...
Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier
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- Rajabi, S. (författare)
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
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- Saman Azari, Mehdi (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),DISA;DISA-SIG
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- Santini, S. (författare)
- Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
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- Flammini, Francesco, Senior Lecturer, 1978- (författare)
- Linnéuniversitetet,Mälardalens universitet,Innovation och produktrealisering,Department of Computer Science and Media Technology, Linnaeus University, Växjo, Sweden,Institutionen för datavetenskap och medieteknik (DM),Mälardalen University, Sweden
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(creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Expert systems with applications. - : Elsevier. - 0957-4174 .- 1873-6793. ; 206
- Relaterad länk:
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https://doi.org/10.1...
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https://lnu.diva-por... (primary) (Raw object)
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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Abstract
Ämnesord
Stäng
- Rotating equipment is considered as a key component in several industrial sectors. In fact, the continuous operation of many industrial machines such as sub-sea pumps and gas turbines relies on the correct performance of their rotating equipment. In order to reduce the probability of malfunctions in this equipment, condition monitoring, and fault diagnosis systems are essential. In this work, a novel approach is proposed to perform fault diagnosis in rotating equipment based on permutation entropy, signal processing, and artificial intelligence. To that aim, vibration signals are employed for an indication of bearing performance. In order to facilitate fault diagnosis, fault detection and isolation are performed in two separate steps. As first, once a vibration signal is received, the faulty state of the bearing is determined by permutation entropy. In case a faulty state is detected, the fault type is determined using an approach based on signal processing and artificial intelligence. Wavelet packet transform and envelope analysis of the vibration signals are utilized to extract the frequency components of the fault. The proposed approach allows for the automatic selection of a frequency band that includes the characteristic resonance frequency of the fault, which is subject to change in different operational conditions. The method works by extracting the proper features of the signals that are used to decide about the faulty bearing's condition by a multi-output adaptive neuro-fuzzy inference system classifier. The effectiveness of the approach is assessed by the Case Western Reserve University dataset: the analysis demonstrates the proposed method's capabilities in accurately diagnosing faults in rotating equipment as compared to existing approaches.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Fault diagnosis
- Multi output adaptive neuro-fuzzy inference system
- Permutation entropy
- Rolling element bearing
- Wavelet transform
- Computer Science
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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