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A Subspace Clustering Chart Using a Reference Model for Featureless Bearing Performance Degradation Assessment
State Key Laboratory of Mechanical Transmission, Shazheng Street 174, Shapingba District, Chongqing, 400044, China.
State Key Laboratory of Mechanical Transmission, Shazheng Street 174, Shapingba District, Chongqing, 400044, China.
Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui, 230026, China.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
2018 (English)In: MFPT 2018 - Intelligent Technologies for Equipment and Human Performance Monitoring, Proceedings, Society for Machinery Failure Prevention Technology , 2018, p. 35-49Conference paper, Published paper (Other academic)
Abstract [en]

The health index (HI) of machine condition must be sensitive and robust in complex working conditions. A systematic HI will assess machine performance automatically, reliably, and in a timely manner without intervention. This paper proposes a subspace clustering HI in a model using reference data on component health. Unlike the conventional HIs empirically learned from raw feature sets, a subspace clustering HI aims to automatically describe the migration and variation of the condition clustering distribution in a series of two-class subspace models derived from the raw data. First, in a featureless process, a covariance-driven Hankel matrix is directly constructed from the raw time-domain signal, and principal component analysis is used to separate the feature subspace and noise null-space. Second, in the index construction process, the reference health subspace data (from healthy data) and the monitored subspace data (from monitored data) are combined to construct a referenced model. Thus, a new spatial clustering HI with kernel operation is implemented to assess the current bearing performance and reveal discriminative features. The effectiveness of the proposed subspace clustering HI for the detection of abnormal condition is evaluated experimentally on bearing test-beds, using a mobile mapping mode. A novel subspace clustering chart, CUSUM-based spatial clustering HI, is developed to depict the real bearing performance degradation. Compared to the regular HI (e.g., root mean square), the proposed approach provides a more accurate and reliable degradation assessment profile with an early fault occurrence alarm. The experimental results show the potential of the proposed spatial clustering analysis to assess bearing degradation.

Place, publisher, year, edition, pages
Society for Machinery Failure Prevention Technology , 2018. p. 35-49
Keywords [en]
Bearing health monitoring, Featureless process, Performance degradation assessment, Spatial clustering analysis, Subspace clustering chart
National Category
Reliability and Maintenance
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-86254Scopus ID: 2-s2.0-85069450934OAI: oai:DiVA.org:ltu-86254DiVA, id: diva2:1577224
Conference
Intelligent Technologies for Equipment and Human Performance Monitoring (MFPT 2018), Virginia Beach, USA, May 15-17, 2018
Note

Finansiär: National Natural Science Foundation of China (51475053); Foundation of SKLMT-KFKT-201704 8; Postdoctoral Science Foundation of China (2017M622960)

Available from: 2021-07-02 Created: 2021-07-02 Last updated: 2021-07-02Bibliographically approved

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