Simulation-driven machine learning: Bearing fault classification

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

doi:10.1016/j.ymssp.2017.06.025

Cited by 181 publications

(105 citation statements)

References 54 publications

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“…Non-parametric methods are also very useful tools for classification purposes. We have selected methods which are, in our opinion, most commonly used for engineering purposes [6] and [15]. In the following paragraphs brief explanations of different classification methods are given.…”

Section: Non-parametric Classification Methodsmentioning

confidence: 99%

“…The bearing used for testing was ER16K ball bearing. In previous studies [6], [15] and [49] a plethora of features that could be extracted from the vibrational data were studied, specifically from the time domain, frequency domain or the timefrequency domain using various signal-processing tools such as the Fourier transform, Hilbert transform, Wavelet transform, etc. The feature-extraction part can greatly enhance the results of the classification and there is a lot of studies emerging on this topic [50].…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

“…However, since this paper represents the application of a classification method and its variants to one of the most dominant problems in the field of bearing and rotating-machinery fault detection we will simplify the feature-extraction process to only the statistical features of the vibrational signals in the time and frequency domains. This resulted in thirteen different most popular statistical features, judging by the literature [6], [15] and [49]. Features are given in Table 3, where x i is the i th amplitude of the acceleration signal, N is the number of samples in the signal, μ x is the mean value of the signal, σ x is the standard deviation of the signal, f i is the corresponding i th frequency amplitude.…”

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

“…The first type of studies covers different signal-processing techniques for the classification of bearing faults [7] and [13] or for feature extraction and selection from vibrational data, which are then used to enhance the results of an applied classification method [14]. Other studies mostly utilize the different classification methods to obtain better classification results [6] and [15]. This paper is of the latter type.…”

Section: Introductionmentioning

confidence: 99%

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Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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Condition monitoring and fault detection are nowadays popular topic. Different loads, enviroments etc. affect the components and systems differently and can induce the fault and faulty behaviour. Most of the approaches for the fault detection rely on the use of the good classification method. Gaussian mixture model based classification are stable and versatile methods which can be applied to a wide range of classification tasks. The main task is the estimation of the parameters in the Gaussian mixture model. Those can be estimated with various techniques. Therefore, the Gaussian mixture model based classification have different variants which can vary in performance. To test the performance of the Gaussian mixture model based classification variants and general usefulness of the Gaussian mixture model based classification for the fault detection, we have opted to use the bearing fault classification problem. Additionally, comparisons with other widely used non-parametric classification methods are made, such as support vector machines and neural networks. The performance of each classification method is evaluated by multiple repeated k-fold cross validation. From the results obtained, Gaussian mixture model based classification methods are shown to be competitive and efficient methods and usable in the field of fault detection and condition monitoring. Highlights• Gaussian-mixture-model-based classification was applied to the bearing-fault classification. • To discriminate the faulty from non-faulty bearings only simple statistics from vibrational data was used. • Two different datasets, the Case Western Rice University dataset and Bearing vibration data collected under time-varying rotational speed conditions dataset are used. • The Gaussian-mixture-model-based classification method showed to be a competitive and efficient method.

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Section: Non-parametric Classification Methodsmentioning

confidence: 99%

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Section: Feature Extraction and Construction Of Classification Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Panić

Klemenc

Nagode

2020

SV-JME

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“…This model allows to simulate the system in different operational conditions such as different path shapes, cart geometry, types of bearings, motion and load profiles. With the simulated data it is possible to reduce the complexity of the system by simulating only the most important phenomena of the system, it is also possible to use the simulated data to develop condition monitoring thresholds and to train machine learning and deep learning algorithms [16]. Many studies exist on dynamic models of bearing faults, especially for rotary motors that use different modelling techniques [17].…”

Section: Introductionmentioning

confidence: 99%

A bearing fault model for Independent Cart Conveyor System and its validation

et al. 2020

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Independent Cart Conveyor System is one of the most promising technology in automation industries. It combines the benefits of servo motors with the advantages of linear motors. It consists of a close path made up of modular linear motors having a curved or a straight shape that control a fleet of carts independently. Each cart is placed along the motors and it is connected, through rolling bearings, to a rail set on the motors themselves. The bearings are subject to wear and the condition monitoring of these elements is challenging for the non-stationary working conditions of variable load and variable speed profiles. This paper provides a bearing fault vibration model that takes into account the mechanical design of the cart, its motion profile, the shape of the conveyor path, the load variation and the type of fault on the rolling bearing.

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Zero‐Shot Learning

Kenett

2023

Wiley StatsRef: Statistics Reference Online

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Simulation-driven machine learning: Bearing fault classification

Cited by 181 publications

References 54 publications

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

Gaussian Mixture Model Based Classification Revisited: Application to the Bearing Fault Classification

A bearing fault model for Independent Cart Conveyor System and its validation

Zero‐Shot Learning

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