HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis

In response to the high-noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). E...

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Main Authors: Gang Yang, Yu Wang, Dezhao Qin, Rui Zhu, Qingpeng Han
Format: Article
Language:English
Published: Wiley 2024-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2024/5582169
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author Gang Yang
Yu Wang
Dezhao Qin
Rui Zhu
Qingpeng Han
author_facet Gang Yang
Yu Wang
Dezhao Qin
Rui Zhu
Qingpeng Han
author_sort Gang Yang
collection DOAJ
description In response to the high-noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). Experimental results demonstrate that HMM can accurately diagnose and predict faults in ECS turbofan rolling bearings. The HMM method enhances diagnostic accuracy, and its effectiveness and feasibility in fault diagnosis based on different rolling bearing fault instances are elaborated. By employing the HMM model to establish precise models from decomposed dynamic data, it successfully identifies faults such as the fracture of the bearing cage under biased load conditions, although its performance in recognizing overheating faults is suboptimal.
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issn 1875-9203
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publishDate 2024-01-01
publisher Wiley
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series Shock and Vibration
spelling doaj-art-7a0a91cd47e14831bb240b112c54d7d92025-08-20T02:07:19ZengWileyShock and Vibration1875-92032024-01-01202410.1155/2024/5582169HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault DiagnosisGang Yang0Yu Wang1Dezhao Qin2Rui Zhu3Qingpeng Han4School of Energy and Mechanical EngineeringSchool of Energy and Mechanical EngineeringSchool of Energy and Mechanical EngineeringSchool of Energy and Mechanical EngineeringSchool of Energy and Mechanical EngineeringIn response to the high-noise, nonlinear, and nonstationary characteristics of vibration signals from aircraft environmental control system (ECS) turbofan rolling bearings, this paper proposes a diagnostic method for the degree of ECS turbofan bearing faults based on the Hidden Markov Model (HMM). Experimental results demonstrate that HMM can accurately diagnose and predict faults in ECS turbofan rolling bearings. The HMM method enhances diagnostic accuracy, and its effectiveness and feasibility in fault diagnosis based on different rolling bearing fault instances are elaborated. By employing the HMM model to establish precise models from decomposed dynamic data, it successfully identifies faults such as the fracture of the bearing cage under biased load conditions, although its performance in recognizing overheating faults is suboptimal.http://dx.doi.org/10.1155/2024/5582169
spellingShingle Gang Yang
Yu Wang
Dezhao Qin
Rui Zhu
Qingpeng Han
HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
Shock and Vibration
title HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
title_full HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
title_fullStr HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
title_full_unstemmed HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
title_short HMM-Based Method for Aircraft Environmental Control System Turbofan Rolling Bearing Fault Diagnosis
title_sort hmm based method for aircraft environmental control system turbofan rolling bearing fault diagnosis
url http://dx.doi.org/10.1155/2024/5582169
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AT dezhaoqin hmmbasedmethodforaircraftenvironmentalcontrolsystemturbofanrollingbearingfaultdiagnosis
AT ruizhu hmmbasedmethodforaircraftenvironmentalcontrolsystemturbofanrollingbearingfaultdiagnosis
AT qingpenghan hmmbasedmethodforaircraftenvironmentalcontrolsystemturbofanrollingbearingfaultdiagnosis