Detecting bearing defects under high noise levels: A classifier fusion approach

Batista, Luana; Badri, Bechir; Sabourin, Robert; Thomas, Marc

doi:10.1109/iecon.2012.6389272

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Some works have focused on the optimization of the bearing fault detection procedure [121]. Reference [143], likewise, proposes a methodology taking into account support vector machines (SVM) to consequently recognize and characterize bearing faults, with the help of noise reduction to simplify the presence of vibration signals. Reference [144] combines the envelope analysis of vibration signals, the sliding FFT procedure, and PCA to analyze bearing faults.…”

Section: Diagnosis Of Bearing and Gear-based Faultsmentioning

confidence: 99%

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

et al. 2022

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This review paper looks briefly at conventional approaches and examines the intelligent means for fault diagnosis (FD) and condition monitoring (CM) of electrical drives in detail, especially the ones that are common in Industry 4.0. After giving an overview on fault statistics, standard methods for the FD and CM of rotating machines are first visited, and then its orientation towards intelligent approaches is discussed. Major diagnostic procedures are addressed in detail together with their advancements to date. In particular, the emphasis is given to motor current signature analysis (MCSA) and digital signal processing techniques (DSPTs) mostly used for feature engineering. Consequently, the statistical procedures and machine learning techniques (stemming from artificial intelligence—AI) are also visited to describe how FD is carried out in various systems. The effectiveness of the amalgamation of the model, signal, and data-based techniques for the FD and CM of inductions motors (IMs) is also highlighted in this review. It is worth mentioning that a variety of neural- and non-neural-based approaches are discussed concerning major faults in rotating machines. Finally, after a thorough survey of the diagnostic techniques based on specific faults for electrical drives, several open problems are identified and discussed. The paper concludes with important recommendations on where to divert the research focus considering the current advancements in the FD and CM of rotating machines.

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Section: Diagnosis Of Bearing and Gear-based Faultsmentioning

confidence: 99%

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

et al. 2022

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“…Some works have focused on their optimization or even the automation of the bearing fault detection process, such as [50]. Reference [51] also proposes an approach based on SVM to automatically detect and classify bearing problems, relying on noise reduction to facilitate the detection of vibration components. Reference [52] combines the envelope analysis of vibration signals, the sliding fast Fourier transform (FFT) technique and principal component analysis (PCA) to diagnose bearing faults.…”

Section: Fault Diagnosis Of Mechanical Failures 1) Fault Diagnosismentioning

confidence: 99%

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Riera-Guasp

Antonino-Daviu

Capolino

2015

IEEE Trans. Ind. Electron.

487

234

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“…A few works have concentrated on the optimization of the bearing fault detection procedure such as [9]. In [12] likewise, it has been proposed a methodology taking into account Support Vector Machines (SVM) to consequently recognize and characterize bearing faults, depending on the noise reduction to simplify the presence of vibration signals. In [13], envelope analysis of Further, [14] presents a fascinating way to deal with plastic bearing FD in view of a two-stage process that combines envelope analysis and empirical mode decomposition (EMD) to preprocess vibration signals and concentrate on the fault related components.…”

Section: Introductionmentioning

confidence: 99%

Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

Cirrincione

Kumar

Mohammadi

et al. 2020

IEEE Trans. Energy Convers.

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Accurate gear defect detection in induction machinebased systems is a fundamental issue in several industrial applications. At this aim, shallow neural networks, i.e. architectures with only one hidden layer, have been used after a feature extraction step from vibration, torque, acoustic pressure and electrical signals. Their additional complexity is justified by their ability in extracting its own features and in the very high-test classification rates. These signals are here analyzed, both geometrically and topologically, in order to estimate the class manifolds and their reciprocal positioning. At this aim, the different states of the gears are studied by using linear (Pareto charts, biplots, principal angles) and nonlinear (curvilinear component analysis) techniques, while the class clusters are visualized by using the parallel coordinates. It is deduced that the class manifolds are compact and well separated. This result justifies the use of a shallow neural network, instead of a deep one, as already remarked in the literature, but with no theoretical justification. The experimental section confirms this assertion, and also compares the shallow neural network results with the other machine learning techniques used in the literature.

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Detecting bearing defects under high noise levels: A classifier fusion approach

Cited by 6 publications

References 19 publications

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

A Comprehensive Review of Conventional and Intelligence-Based Approaches for the Fault Diagnosis and Condition Monitoring of Induction Motors

Advances in Electrical Machine, Power Electronic, and Drive Condition Monitoring and Fault Detection: State of the Art

Shallow Versus Deep Neural Networks in Gear Fault Diagnosis

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