A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

Chen, Fafa; Tang, Baoping; Chen, Renxiang

doi:10.1016/j.measurement.2012.06.009

Cited by 131 publications

(59 citation statements)

References 33 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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“…Here we choose crossover and mutation. So, next step is crossover and mutation using the equation3 and 4 17 . According to the probability Pi, randomly select two antibodies a and a l from the population, then the crossover operation of two antibodies in j-bit can be described as follows:…”

Section: Gabp Modelmentioning

confidence: 99%

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

Xie¹,

Wang²,

MacIntyre³

et al. 2018

IJCIS

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This paper proposes using engine's sensors data flow and exhaust emissions information to diagnose engine's faults, enhancing the accuracy of fault diagnosis. Engine fault diagnosis model is built using both this information and the mature BP neural network and genetic algorithms. In order to verify the method, we build a test platform, which includes South Korea Hyundai fault test vehicle and X-431 diagnosis instrument and AUTO5-1 exhaust gas analyzer and computer. The diagnostic accuracy rate can reach 98.33%, which is higher than using sensors data flow or the exhaust emissions information alone.

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“…More published literature of structural parameter optimization of ANN or support vector machine (SVM) based on EAs can be found in Refs. [60][61][62][63][64]. Conversely, feature selection based on EAs combining with classification method is used for fault diagnosis.…”

Section: Fault Classification Using Easmentioning

confidence: 99%

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

Zhang

Jia

Zhu

et al. 2017

Chin. J. Mech. Eng.

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Computational intelligence is one of the most powerful data processing tools to solve complex nonlinear problems, and thus plays a significant role in intelligent fault diagnosis and prediction. However, only few comprehensive reviews have summarized the ongoing efforts of computational intelligence in machinery condition monitoring and fault diagnosis. The recent research and development of computational intelligence techniques in fault diagnosis, prediction and optimal sensor placement are reviewed. The advantages and limitations of computational intelligence techniques in practical applications are discussed. The characteristics of different algorithms are compared, and application situations of these methods are summarized. Computational intelligence methods need to be further studied in deep understanding algorithm mechanism, improving algorithm efficiency and enhancing engineering application. This review may be considered as a useful guidance for researchers in selecting a suitable method for a specific situation and pointing out potential research directions.

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A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm

Cited by 131 publications

References 33 publications

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

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

Using Sensors Data and Emissions Information to Diagnose Engine’s Faults

Comprehensive Overview on Computational Intelligence Techniques for Machinery Condition Monitoring and Fault Diagnosis

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