Bearing fault diagnosis based on Lasso regularization method

Duque-Pérez, Óscar; Pozo-Gallego, Carlos Del; Moriñigo-Sotelo, Daniel; Godoy, Wagner Fontes

doi:10.1109/demped.2017.8062376

Cited by 7 publications

(2 citation statements)

References 22 publications

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“…For example, Lateko et al introduced Lasso into the designed method to achieve effective optimization of learner parameters, and the experimental results confirmed the effectiveness of this method [ 30 ]. Duque-Perez et al improved the traditional Logistic regression classifier with the help of lasso to enhance the model performance of bearing fault diagnosis, and the experimental results confirmed its effectiveness [ 31 ]. However, these methods focus more on utilizing lasso to optimize the basic classifier parameters without explicitly incorporating the time domain, frequency domain, and deep representation features related to bearing faults.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Chu

Ali

et al. 2023

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In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis. Primarily, via signal processing methods, statistical features are extracted, and via the deep stack autoencoder (DSAE), deep representation features are acquired. Next, considering the different levels of predictive power of features, a modified lasso method incorporating the random subspace method is introduced to measure the features and produce better base classifiers. Finally, the majority voting strategy is applied to aggregate the outputs of these various base classifiers to enhance the diagnostic performance of the bearing fault. For the proposed method’s validity, two bearing datasets provided by the Case Western Reserve University Bearing Data Center and Paderborn University were utilized for the experiments. The results of the experiment revealed that in bearing fault diagnosis, the proposed method of IHF-RS can be successfully utilized.

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Section: Introductionmentioning

confidence: 99%

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Chu

Ali

et al. 2023

Entropy

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“…For example, a LASSO binary logistic regression model is developed in [23] to identify the influencing factors of outages in smart grids, i.e., covariates such as daily precipitation, minimum and maximum temperatures. In [24], a One-Vs-All (OVA) system of LASSO binary logistic regression models has been developed for diagnosing bearing faults. The main limitations of the LASSO in fault diagnostics applications are related to the degradation of its performances when a)…”

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Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

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Baraldi

et al. 2019

Aerospace Science and Technology

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The objective of this work is the development of a fault diagnostic system for a shaker blower used in on-board aeronautical systems. Features extracted from condition monitoring signals and selected by the ELastic NET (ELNET) algorithm, which combines 1 -penalty with the squared 2 -penalty on model parameters, are used as inputs of a Multinomial Logistic regression (MLR) model. For validation, the developed approach is applied to experimental data acquired on a shaker blower system (as representative of aeronautical on-board systems) and on three additional experimental datasets of literature. The satisfactory diagnostic performances obtained show the potential of the method for developing sound diagnostic classifiers from a very large set of features, even when only few training data are available.

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2019

Condition Monitoring With Vibration Signals

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Bearing fault diagnosis based on Lasso regularization method

Cited by 7 publications

References 22 publications

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Incorporating Heterogeneous Features into the Random Subspace Method for Bearing Fault Diagnosis

Elastic net multinomial logistic regression for fault diagnostics of on-board aeronautical systems

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