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Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results

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European Workshop on Structural Health Monitoring (EWSHM 2020)

Part of the book series: Lecture Notes in Civil Engineering ((LNCE,volume 127))

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Abstract

Bearings are the most common mechanical components in machines. Once a bearing fails (or components in it), other adjacent components or the machine itself are effected up to failure. Therefore, bearing health condition is of great interest in practice. Several benchmark datasets are developed to evaluate development in bearings health state (diagnosis) and remaining useful lifetime (prognosis). Among these datasets, Case Western Reserve University (CWRU) dataset is one of the most cited ones used to validate the performance of different diagnostic approaches. Over recent years, a significant amount of research approaches are developed using CWRU data. Most approaches are focused on specific performance parameters like detection rate or accuracy etc. The main problems in connection with CWRU dataset use are: no overview about latest results is available. Furthermore several results published are not complete, for example published accuracies rate without false alarm rates.

In this contribution an overview about the development change over the last years, the approaches applied, and specifically the results obtained will be given. Additionally, the new approaches emerging in recent years like deep learning (DL) also in combination with fusion methods and related performance will be given in comparison with conventional machine learning (ML) methods. Special care will be given to the completeness of published results also in combination with shown robustness. As outcome of this contribution the newest and best results are noted, furthermore a recommendation how to complete research work using benchmark dataset will be given. Although most approaches using CWRU dataset as benchmark get high accuracy, for further bearing fault diagnosis research, more and more suitable measures as well as other datasets are needed for increased performance evaluation.

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Authors and Affiliations

  1. Chair of Dynamics and Control, University of Duisburg Essen, Duisburg, Germany

    Xiao Wei & Dirk Söffker

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  1. Xiao Wei
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  2. Dirk Söffker
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Correspondence to Xiao Wei .

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Editors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Pittsburgh, Pittsburgh, PA, USA

    Piervincenzo Rizzo

  2. Department of Engineering, Università degli Studi di Palermo, Palermo, Italy

    Alberto Milazzo

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Wei, X., Söffker, D. (2021). Comparison of CWRU Dataset-Based Diagnosis Approaches: Review of Best Approaches and Results. In: Rizzo, P., Milazzo, A. (eds) European Workshop on Structural Health Monitoring. EWSHM 2020. Lecture Notes in Civil Engineering, vol 127. Springer, Cham. https://doi.org/10.1007/978-3-030-64594-6_51

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  • DOI: https://doi.org/10.1007/978-3-030-64594-6_51

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64593-9

  • Online ISBN: 978-3-030-64594-6

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