| Summary: | 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.
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