Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

Widodo, Achmad; Kim, Eric Y.; Son, Jong-Duk; Yang, Bo‐Suk; Tan, Andy; Gu, Dong-Sik; Choi, Byeong-Keun; Mathew, Joseph

doi:10.1016/j.eswa.2008.09.033

Cited by 281 publications

(142 citation statements)

References 21 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

“…Yang et al [225] applied artificial bee colony algorithm for SVM parameter optimization of gearbox fault diagnosis, and found that the accuracy of the artificial bee colony algorithm is higher compared with GA and PSO. Widodo et al [226] studied the incipient fault diagnosis of lowspeed bearings using multi-class relevance vector machine and SVM. Another study [227] employed the Hilbert transform-based envelope spectrum analysis to extract fault bearing features, and then used the improved SVM to classify the fault rolling bearings into ball fault, inner race fault, and outer race fault.…”

Section: Ai Approachesmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…In order to solve this problem, researchers have proposed some improved methods, such as image extension, neural network extension, similarity extremum extension, signal sequence even extension, support vector regression and so on [6] [7]. However, there is a common problem in these extension methods, that is, after the extension [8], the endpoint of the signal is still uncertain, so that as the decomposition progresses, there is still a divergence phenomenon at both ends of the envelope, ", Leading to its endpoint effect problem still exists [9].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Diagnosis of Rotating Machinery of Vehicle Transmission

Zhang¹,

Hui-yuan²

2017

Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)

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Abstract. In this paper, the gearbox as the research object, under the conditions of strong interference signal acquisition and analysis, denoising, eigenvalue extraction, pattern recognition and fault prediction has very important practical significance. Aiming at the problem of end effect in Empirical Mode Decomposition (EMD), an improved method for the continuation of the even-extended cosine window function (EECW) of signal sequences is proposed. Firstly, the signal sequence is continually extended to realize the smooth transition between the extension data and the original signal, avoiding the jump of the instantaneous frequency of the signal. Secondly, there is the problem of continuation error for this extension method. (Or slow speed) to the internal development of data to ensure the correct decomposition of the signal valid data to improve the accuracy of the decomposition of the signal to achieve the improvement of EMD algorithm. Through the simulation analysis and fault diagnosis, it is shown that the method can effectively suppress EMD endpoint effect and realize the effective diagnosis of rotating machinery fault. The experimental results show that the proposed method has high precision and good performance

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Fault diagnosis of low speed bearing based on relevance vector machine and support vector machine

Cited by 281 publications

References 21 publications

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

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

Basic research on machinery fault diagnostics: Past, present, and future trends

Research on Fault Diagnosis of Rotating Machinery of Vehicle Transmission

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