Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Li, Zhixiong; Yan, Xinping; Tian, Zhe; Yuan, Chengqing; Peng, Zhongxiao; Li, Li

doi:10.1016/j.measurement.2012.06.013

Cited by 133 publications

(66 citation statements)

References 29 publications

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“…Existing applications include bearings (Abbasion et al, 2007;Kankar et al, 2011;Samanta et al, 2006;Sharma et al, 2015;Sugumaran et al, 2007;Widodo et al, 2009) and gearboxes (Chen et al, 2013;Li et al, 2011Li et al, , 2013Staszewski et al, 1997). The combination of CM data, signal processing and data analysis is also known as fault detection or fault diagnosis.…”

Section: Condition Monitoring Using Probabilistic Datamentioning

confidence: 99%

Risk-Based Maintenance Scheduling with application to naval vessels and ships

Cullum

Binns

Lonsdale³

et al. 2018

Ocean Engineering

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Maintenance scheduling for naval vessels and ships requires ongoing improvement to manage rising maintenance costs within availability constraints. Existing maintenance scheduling approaches are not optimal as maintenance costs continue to rise without an improvement in vessel availability. This paper reviews the Risk-Based Maintenance Scheduling (RBM) framework as applied to ships and naval vessels, and provides a critical analysis of Risk Assessment and Maintenance Scheduling techniques used. Further, objectives and considerations are defined for future applications for ships and naval vessels, and the framework evaluated as an improvement on existing Preventative Maintenance (PM) and Reliability Centered Maintenance (RCM) methods. A probabilistic approach supported by condition monitoring data in combination with Decision Theory is suggested for the Risk Assessment and Maintenance Scheduling elements comprising an RBM Scheduling framework. Implementation of this framework from both periodic PM and RCM is presented. Development of applications from the component level upwards is suggested. Availability and overall maintenance cost are suggested as evaluation metrics against existing methods. The development of an application is formalized within a proposed framework. The development of an application within the RBM Scheduling framework is expected to result in reduced maintenance costs while meeting availability requirements for ship and naval vessel applications.

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Section: Condition Monitoring Using Probabilistic Datamentioning

confidence: 99%

Risk-Based Maintenance Scheduling with application to naval vessels and ships

Cullum

Binns

Lonsdale³

et al. 2018

Ocean Engineering

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show abstract

“…The internal structure of the gearbox system is interrelated and coupled inside [2]. During the operation process, a lot of factors need to be considered when evaluating the performance status of the equipment, such as vibration, noise temperature, the debris contaminants in the oil and grease, torque of the power input and output, and stress distribution on the tooth surface.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Chen

Zhou

Fang

et al. 2017

International Journal of Rotating Machinery

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A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system. When faults occur on gear system, it is difficult to extract the fault feature. In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox. The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise. The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types. To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out. Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

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“…In addition, the frequency of the vibration source signals may also interlap from each other, which makes fault characteristic information extraction much more difficultly. With the statistical independence among multiple random source signals, blind source separation (BSS) can makes the output close to the source signals as much as possible only from a set of mixed signals received by sensors when position, number of source signals and transmission channel parameters are unknown [2].…”

Section: Introductionmentioning

confidence: 99%

Multiple random fault sources adaptive blind separation in situation of time-varying source signals and system

Wang¹,

Hu²,

Zhan³

et al. 2017

Vib. proced.

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In order to separate multiple random fault source signals adaptively only from mixed vibration measurement signals of mechanical system in the situation of time-varying source signals and mixture system, two adaptive blind identification and separation methods are proposed to solve this kind of problem. One is based on recursive least squares (RLS) algorithm and another is based on recursive EASI algorithm. The simulation results show that both of these methods can separate source signals from the mixed signal in the situation of time-varying source signals and mixture system very well.

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Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis

Cited by 133 publications

References 29 publications

Risk-Based Maintenance Scheduling with application to naval vessels and ships

Risk-Based Maintenance Scheduling with application to naval vessels and ships

Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Multiple random fault sources adaptive blind separation in situation of time-varying source signals and system

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