Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm

Authors

Farzin Piltan^1, , Jong-Myon Kim^{2, *,}

¹Department of Electrical, Electronics and Computer Engineering, University of Ulsan, Ulsan, 680-479, Korea

²School of IT Convergence, University of Ulsan, Ulsan, 680-479, Korea

^*Corresponding author. Email: jongmyon.kim@gmail.com

DOI

10.2991/ijcis.d.201228.002How to use a DOI?

Keywords

Lyapunov-based observer; Fuzzy algorithm; Adaptive technique; Autoregressive learning signal modeling; Support vector regression; Support vector machine; Fault diagnosis

Abstract

Bearings are complex components with nonlinear behavior that are used to reduce the effect of inertia. They are used in applications such as induction motors and rotating components. Condition monitoring and effective data analysis are important aspects of fault detection and classification in bearings. Thus, an effective and robust hybrid technique for fault detection and identification is presented in this study. The proposed scheme has four main steps. First, a mathematical approach is combined with an autoregressive learning technique to approximate the vibration signal under normal conditions and extract the state-space equation. In the next step, an intelligence-based observer is designed using a combination of the robust Lyapunov-based method, autoregressive learning scheme, fuzzy technique, and adaptive algorithm. The intelligence-based observer is the main part of the algorithm that determines the fault estimation in the bearing. After estimating the signals, in the third step, the residual signals are generated, resampled, and the root mean square (RMS) is extracted from the resampled residual signals. Then, in the final step, the classification, detection, and identification of the signal is performed by the support vector machine algorithm. The effectiveness of the proposed learning control algorithm is analyzed using the Case Western Reverse University (CWRU) bearing vibration dataset. The proposed method is compared to two state-of-the-art techniques: an autoregressive learning Lyapunov-based observer and a Lyapunov-based observer. The proposed algorithm improved the average fault identification accuracy by 3.9% and 5.2% compared to the autoregressive learning Lyapunov-based approach and the Lyapunov-based technique, respectively.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Farzin Piltan
AU  - Jong-Myon Kim
PY  - 2021
DA  - 2021/01/04
TI  - Fault Diagnosis of Bearings Using an Intelligence-Based Autoregressive Learning Lyapunov Algorithm
JO  - International Journal of Computational Intelligence Systems
SP  - 537
EP  - 549
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201228.002
DO  - 10.2991/ijcis.d.201228.002
ID  - Piltan2021
ER  -