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Attention Based Echo State Network: A Novel Approach for Fault Prognosis

Authors:

Yuan LiaoAuthors Info & Claims

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

Pages 489 - 493

https://doi.org/10.1145/3318299.3318325

Published: 22 February 2019 Publication History

Abstract

Recurrent neural networks (RNNs) are widely studied in recent years, since RNNs are capable of modeling the significant nonlinear dynamical systems. Echo state network (ESN) is a novel type of RNN with an interconnected reservoir to model temporal dynamics of complex sequential information. In this paper, a novel ESN structure is developed and employed to conduct fault prognosis. Fault prognosis is vital in predictive maintenance, which is a prevalent research area that mainly concentrates on predicting the remaining useful life of a machine and reducing the machine's downtime. Attention model is integrated to a typical ESN and thus different importance levels of different input elements can be adaptively treated. To further enhance the generalization of the prediction model, genetic algorithm is applied to adaptively optimize the parameters of the attention-based ESN. The proposed prognostic approach is verified on the NASA's turbofan benchmark dataset. Experimental results show that the attention-based ESN can not only achieve superior prediction accuracy but also obtain substantial improvement on stability.

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Cited By

Bampoula XNikolakis NAlexopoulos K(2024)Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer EncodersSensors10.3390/s2410321524:10(3215)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103215
Macktoobian MShu ZZhao Q(2023)Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant ApproachIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2023.33282759(786-799)Online publication date: 2023
https://doi.org/10.1109/TSIPN.2023.3328275
Chen ZO’Neill ZWen JPradhan OYang TLu XLin GMiyata SLee SShen CChiosa RPiscitelli MCapozzoli AHengel FKührer APritoni MLiu WClauß JChen YHerr T(2023)A review of data-driven fault detection and diagnostics for building HVAC systemsApplied Energy10.1016/j.apenergy.2023.121030339(121030)Online publication date: Jun-2023
https://doi.org/10.1016/j.apenergy.2023.121030
Show More Cited By

Index Terms

Attention Based Echo State Network: A Novel Approach for Fault Prognosis
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by regression
    2. Machine learning approaches
      1. Neural networks

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ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

February 2019

563 pages

ISBN:9781450366007

DOI:10.1145/3318299

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Southwest Jiaotong University

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New York, NY, United States

Publication History

Published: 22 February 2019

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ICMLC '19

ICMLC '19: 2019 11th International Conference on Machine Learning and Computing

February 22 - 24, 2019

Zhuhai, China

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Cited By

Bampoula XNikolakis NAlexopoulos K(2024)Condition Monitoring and Predictive Maintenance of Assets in Manufacturing Using LSTM-Autoencoders and Transformer EncodersSensors10.3390/s2410321524:10(3215)Online publication date: 18-May-2024
https://doi.org/10.3390/s24103215
Macktoobian MShu ZZhao Q(2023)Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant ApproachIEEE Transactions on Signal and Information Processing over Networks10.1109/TSIPN.2023.33282759(786-799)Online publication date: 2023
https://doi.org/10.1109/TSIPN.2023.3328275
Chen ZO’Neill ZWen JPradhan OYang TLu XLin GMiyata SLee SShen CChiosa RPiscitelli MCapozzoli AHengel FKührer APritoni MLiu WClauß JChen YHerr T(2023)A review of data-driven fault detection and diagnostics for building HVAC systemsApplied Energy10.1016/j.apenergy.2023.121030339(121030)Online publication date: Jun-2023
https://doi.org/10.1016/j.apenergy.2023.121030
Cho ACarrasco RRuz G(2022)A RUL Estimation System from Clustered Run-to-Failure Degradation SignalsSensors10.3390/s2214532322:14(5323)Online publication date: 16-Jul-2022
https://doi.org/10.3390/s22145323
Aditiyawarman TKaban ASoedarsono J(2022)A Recent Review of Risk-Based Inspection Development to Support Service Excellence in the Oil and Gas Industry: An Artificial Intelligence PerspectiveASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering10.1115/1.40545589:1Online publication date: 7-Jun-2022
https://doi.org/10.1115/1.4054558
Chaudhuri APatil RGhosh S(2022)Predictive Maintenance of Vehicle Fleets Using LSTM Autoencoders for Industrial IoT DatasetsBig Data Privacy and Security in Smart Cities10.1007/978-3-031-04424-3_6(103-118)Online publication date: 9-Sep-2022
https://doi.org/10.1007/978-3-031-04424-3_6
Bampoula XSiaterlis GNikolakis NAlexopoulos K(2021)A Deep Learning Model for Predictive Maintenance in Cyber-Physical Production Systems Using LSTM AutoencodersSensors10.3390/s2103097221:3(972)Online publication date: 1-Feb-2021
https://doi.org/10.3390/s21030972
Vollert STheissler A(2021)Challenges of machine learning-based RUL prognosis: A review on NASA's C-MAPSS data set2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )10.1109/ETFA45728.2021.9613682(1-8)Online publication date: 7-Sep-2021
https://doi.org/10.1109/ETFA45728.2021.9613682
Armentia UBarrio IDel Ser J(2021)Performance and Explainability of Reservoir Computing Models for Industrial Prognosis16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021)10.1007/978-3-030-87869-6_3(24-36)Online publication date: 23-Sep-2021
https://doi.org/10.1007/978-3-030-87869-6_3
Lyu JYing RLu NZhang B(2020)Remaining useful life estimation with multiple local similaritiesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2020.10384995(103849)Online publication date: Oct-2020
https://doi.org/10.1016/j.engappai.2020.103849

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