Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

Abbasion, Saeed; Rafsanjani, Ahmad; Farshidianfar, Anoshirvan; Irani, Nilgoon

doi:10.1016/j.ymssp.2007.02.003

Cited by 351 publications

(181 citation statements)

References 23 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

“…If a https://doi.org/10.24846/v26i3y201704 failure exists, then the PE of a subset of selected IMFs is computed and used as the input of a SVM in order to classify the type of the failure as well as its severity. Moreover, WT can decompose a signal into several independent frequency subbands and show features of hidden failures [1,15,18,24]. In [8] authors combine WT and EMD to create a new time-frequency analysis method namely empirical wavelet transform (EWT).…”

Section: Extreme Learning Machine Based On Stationary Wavelet Singulamentioning

confidence: 99%

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Rodríguez¹,

Lagos²,

Cabrera³

et al. 2017

STUD INFORM CONTROL

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Abstract:Intelligence condition monitoring based on vibration signal analysis plays a key role in improving rolling bearings failure diagnosis in mechanical transmission systems. Unexpected failures in the bearings may cause machine breakdowns that are very expensive for the industry. Hence, this study proposes a method to the rolling element bearing failure diagnosis which is based on an extreme learning machine (ELM) algorithm combined with stationary wavelet transform (SWT) and singular value decomposition (SVD). The SWT is used to separate the vibration signals into a series of wavelet component signals. Then, the obtained wavelet components matrix is decomposed by means of a SVD method to obtain a set of wavelet singular values. Finally, the wavelet singular values are used as input to the extreme learning machine for classification among ten different bearing failure types. Obtained results using the proposed model shown high accuracy of diagnosis under variable speed condition.

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“…The raw vibration data are oftentimes pre-processed to aid the detection process. Envelope analysis (Sawalhi et al, 2007;Wang and Lee, 2013) and wavelet-based decompositions (Caesarendra et al, 2013;Lou and Loparo, 2004;Smith et al, 2007;Purushotham et al, 2005;Altmann and Mathew, 2001;Abbasion et al, 2007) are the commonly used pre-processing methods. With the increasing technology, several vibration-based PR methods are used also to diagnose machinery faults (Rauber et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

Tahir

2018

Int. j. adv. appl. sci.

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Identification of localized faults in rolling element bearing (REB) frequently utilizes vibration-based pattern recognition (PR) methods. Time domain (TD) statistical features are often part of the diagnostic models. The extracted statistical values are, however, influenced by the fluctuations present in random vibration signals. These inaccurate values consequently affect the diagnostic capability of the supervised learning based classifiers. This study examines the sensitivity of TD features to signal fluctuations. Vibration data is acquired from different REBs containing localized faults using a test rig, and a central tendency (CT) based feature extraction (CTBFE) method is proposed. The CTBFE ensures the supply of reliable feature values to the PR models. The method selects the fault related appropriate portion of a vibration signal prior to extract TD features. Variety of classifiers is used to judge the effect of CTBFE method on their fault classification accuracies, which are enhanced considerably. The results are also compared with a similar sort of existing method, where the proposed method provides better results and feasibility for on-line applications.

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Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine

Cited by 351 publications

References 23 publications

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

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

Extreme Learning Machine Based on Stationary Wavelet Singular Values for Bearing Failure Diagnosis

Extracting accurate time domain features from vibration signals for reliable classification of bearing faults

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