Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

Ann Smith; William Lee

doi:10.1007/978-3-030-99075-6_17

Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

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

Abstract

A viable data driven approach for determining dynamical systems describing engineering processes would be a valuable tool in condition monitoring. The application of the SINDy algorithm for dynamical system discovery is investigated in the context of a reciprocating compressor. A feasibility study was carried out in which an attempt was made to recover a model of the compressor from synthetic data obtained from that model. A simplified model of the compressor with two degrees of freedom was developed from an existing model. Following the SINDy approach a parsimonious model was constructed from a large library of functions using sparse regression. This model has the same structure as and similar coefficients to the original model thus demonstrating the potential of this approach.

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	199-205
Number of pages	7
Volume	117
Edition	1st
ISBN (Electronic)	9783030990756
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_17
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/

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10.1007/978-3-030-99075-6_17Licence: Unspecified

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Smith, A., & Lee, W. (2022). Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example. 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 (1st ed., Vol. 117, pp. 199-205). (Mechanisms and Machine Science; Vol. 117). Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_17

Smith, Ann ; Lee, William. / Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example. 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 1st. ed. Springer, Cham, 2022. pp. 199-205 (Mechanisms and Machine Science).

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abstract = "A viable data driven approach for determining dynamical systems describing engineering processes would be a valuable tool in condition monitoring. The application of the SINDy algorithm for dynamical system discovery is investigated in the context of a reciprocating compressor. A feasibility study was carried out in which an attempt was made to recover a model of the compressor from synthetic data obtained from that model. A simplified model of the compressor with two degrees of freedom was developed from an existing model. Following the SINDy approach a parsimonious model was constructed from a large library of functions using sparse regression. This model has the same structure as and similar coefficients to the original model thus demonstrating the potential of this approach.",

keywords = "Dynamical systems theory, condition mointoring, Reciprocating compressors, Digital Twin, SINDy, Dynamical systems, Condition monitoring, Digital twin",

author = "Ann Smith and William Lee",

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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Smith, A & Lee, W 2022, Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. 1st edn, vol. 117, Mechanisms and Machine Science, vol. 117, Springer, Cham, pp. 199-205, 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_17

Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example. / Smith, Ann; Lee, William.

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 1st. ed. Springer, Cham, 2022. p. 199-205 (Mechanisms and Machine Science; Vol. 117).

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

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AU - Smith, Ann

AU - Lee, William

N1 - Conference code: 6

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Y1 - 2022/9/18

N2 - A viable data driven approach for determining dynamical systems describing engineering processes would be a valuable tool in condition monitoring. The application of the SINDy algorithm for dynamical system discovery is investigated in the context of a reciprocating compressor. A feasibility study was carried out in which an attempt was made to recover a model of the compressor from synthetic data obtained from that model. A simplified model of the compressor with two degrees of freedom was developed from an existing model. Following the SINDy approach a parsimonious model was constructed from a large library of functions using sparse regression. This model has the same structure as and similar coefficients to the original model thus demonstrating the potential of this approach.

AB - A viable data driven approach for determining dynamical systems describing engineering processes would be a valuable tool in condition monitoring. The application of the SINDy algorithm for dynamical system discovery is investigated in the context of a reciprocating compressor. A feasibility study was carried out in which an attempt was made to recover a model of the compressor from synthetic data obtained from that model. A simplified model of the compressor with two degrees of freedom was developed from an existing model. Following the SINDy approach a parsimonious model was constructed from a large library of functions using sparse regression. This model has the same structure as and similar coefficients to the original model thus demonstrating the potential of this approach.

KW - Dynamical systems theory

KW - condition mointoring

KW - Reciprocating compressors

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KW - SINDy

KW - Dynamical systems

KW - Condition monitoring

KW - Digital twin

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BT - Proceedings of IncoME-VI and TEPEN 2021

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A2 - Gu, Fengshou

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

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ER -

Smith A, Lee W. Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. 1st ed. Vol. 117. Springer, Cham. 2022. p. 199-205. (Mechanisms and Machine Science). Epub 2022 Sep 18. doi: 10.1007/978-3-030-99075-6_17

Towards Data Driven Dynamical System Discovery for Condition Monitoring a Reciprocating Compressor Example

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