A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification

Yinghang He; Guangbin Wang; Fengshou Gu; Andrew D. Ball

doi:10.1007/978-3-030-99075-6_46

A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification

Yinghang He, Guangbin Wang, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

As the amount of data generated by monitoring the condition of rolling bearings is increasing, matrix factorization-based collaborative filtering can effectively dig out valuable fault information from it. However, in practice, the amount of data generated by the normal state of the bearing is much larger than the amount of data of the bearing fault. As the total amount of data increases, this imbalance will become more and more and more severe, bearing fault information is often overwhelmed in it. In response to this problem, this paper starts from the perspective of mathematical statistics, a method of mean conjugate prior is proposed for the bearing normal condition data of bearing score matrix, from which the prior distribution of the probability distribution parameters of the bearing fault data is obtained. Then combined with the Bayesian method, we get the posterior distribution. According to the distribution, the random number is used to construct the Bayesian probabilistic scoring matrix (BPSM). Relying on BPSM, the collaborative filtering recommendation algorithm is used to identify different types of faults in rolling bearings. Under unbalanced data, comparing with the identification under a conventional joint score matrix (CJSM), the model built based on BPSM has a better identification effect on bearing fault state.

Original language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021
Subtitle of host publication	Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer, Cham
Pages	569-581
Number of pages	13
Volume	117
ISBN (Electronic)	9783030990756
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_46
Publication status	Published - 18 Sep 2022
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021 Conference number: 6 https://tepen.net/conference/tepen2021/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated title	TEPEN-2021 and IncoME-VI
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21
Internet address	https://tepen.net/conference/tepen2021/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-99075-6_46Licence: Unspecified

Cite this

He, Y., Wang, G., Gu, F., & Ball, A. D. (2022). A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification. In H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering (Vol. 117, pp. 569-581). (Mechanisms and Machine Science; Vol. 117). Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_46

He, Yinghang ; Wang, Guangbin ; Gu, Fengshou et al. / A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification. Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. pp. 569-581 (Mechanisms and Machine Science).

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title = "A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification",

abstract = "As the amount of data generated by monitoring the condition of rolling bearings is increasing, matrix factorization-based collaborative filtering can effectively dig out valuable fault information from it. However, in practice, the amount of data generated by the normal state of the bearing is much larger than the amount of data of the bearing fault. As the total amount of data increases, this imbalance will become more and more and more severe, bearing fault information is often overwhelmed in it. In response to this problem, this paper starts from the perspective of mathematical statistics, a method of mean conjugate prior is proposed for the bearing normal condition data of bearing score matrix, from which the prior distribution of the probability distribution parameters of the bearing fault data is obtained. Then combined with the Bayesian method, we get the posterior distribution. According to the distribution, the random number is used to construct the Bayesian probabilistic scoring matrix (BPSM). Relying on BPSM, the collaborative filtering recommendation algorithm is used to identify different types of faults in rolling bearings. Under unbalanced data, comparing with the identification under a conventional joint score matrix (CJSM), the model built based on BPSM has a better identification effect on bearing fault state.",

keywords = "Bayesian method, Collaborative filtering, Fault identification, Recommendation system",

author = "Yinghang He and Guangbin Wang and Fengshou Gu and Ball, {Andrew D.}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, TEPEN-2021 and IncoME-VI ; Conference date: 20-10-2021 Through 23-10-2021",

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He, Y, Wang, G, Gu, F & Ball, AD 2022, A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering. vol. 117, Mechanisms and Machine Science, vol. 117, Springer, Cham, pp. 569-581, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_46

A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification. / He, Yinghang; Wang, Guangbin; Gu, Fengshou et al.

Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. p. 569-581 (Mechanisms and Machine Science; Vol. 117).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification

AU - He, Yinghang

AU - Wang, Guangbin

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 6

PY - 2022/9/18

Y1 - 2022/9/18

N2 - As the amount of data generated by monitoring the condition of rolling bearings is increasing, matrix factorization-based collaborative filtering can effectively dig out valuable fault information from it. However, in practice, the amount of data generated by the normal state of the bearing is much larger than the amount of data of the bearing fault. As the total amount of data increases, this imbalance will become more and more and more severe, bearing fault information is often overwhelmed in it. In response to this problem, this paper starts from the perspective of mathematical statistics, a method of mean conjugate prior is proposed for the bearing normal condition data of bearing score matrix, from which the prior distribution of the probability distribution parameters of the bearing fault data is obtained. Then combined with the Bayesian method, we get the posterior distribution. According to the distribution, the random number is used to construct the Bayesian probabilistic scoring matrix (BPSM). Relying on BPSM, the collaborative filtering recommendation algorithm is used to identify different types of faults in rolling bearings. Under unbalanced data, comparing with the identification under a conventional joint score matrix (CJSM), the model built based on BPSM has a better identification effect on bearing fault state.

AB - As the amount of data generated by monitoring the condition of rolling bearings is increasing, matrix factorization-based collaborative filtering can effectively dig out valuable fault information from it. However, in practice, the amount of data generated by the normal state of the bearing is much larger than the amount of data of the bearing fault. As the total amount of data increases, this imbalance will become more and more and more severe, bearing fault information is often overwhelmed in it. In response to this problem, this paper starts from the perspective of mathematical statistics, a method of mean conjugate prior is proposed for the bearing normal condition data of bearing score matrix, from which the prior distribution of the probability distribution parameters of the bearing fault data is obtained. Then combined with the Bayesian method, we get the posterior distribution. According to the distribution, the random number is used to construct the Bayesian probabilistic scoring matrix (BPSM). Relying on BPSM, the collaborative filtering recommendation algorithm is used to identify different types of faults in rolling bearings. Under unbalanced data, comparing with the identification under a conventional joint score matrix (CJSM), the model built based on BPSM has a better identification effect on bearing fault state.

KW - Bayesian method

KW - Collaborative filtering

KW - Fault identification

KW - Recommendation system

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DO - 10.1007/978-3-030-99075-6_46

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T3 - Mechanisms and Machine Science

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BT - Proceedings of IncoME-VI and TEPEN 2021

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A2 - Feng, Guojin

A2 - Wang, Hongjun

A2 - Gu, Fengshou

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PB - Springer, Cham

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

Y2 - 20 October 2021 through 23 October 2021

ER -

He Y, Wang G, Gu F , Ball AD. A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021: Performance Engineering and Maintenance Engineering. Vol. 117. Springer, Cham. 2022. p. 569-581. (Mechanisms and Machine Science). Epub 2022 Sep 18. doi: 10.1007/978-3-030-99075-6_46

A Bayesian Probabilistic Score Matrix Based Collaborative Filtering Recommendation System for Rolling Bearing Fault Identification

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