Abstract—This study proposes a hybrid anomaly detection framework based on Independent Component Analysis (ICA) and Bayesian Long Short-Term Memory (LSTM) networks for early fault diagnosis in rolling element bearings. The model is trained exclusively on normal condition data from the Case Western Reserve University (CWRU) Bearing Dataset, enabling unsupervised deployment suitable for realworld industrial environments. Vibration signals are preprocessed using ICA to extract statistically independent features, followed by a Bayesian LSTM that captures temporal dynamics and provides uncertainty-quantified predictions. A dynamic thresholding mechanism based on Interquartile Range (IQR) autonomously distinguishes between normal and anomalous behavior without manual calibration. Experimental results demonstrate exceptional performance with 99.1% accuracy, perfect recall (100%) on fault conditions, and zero false negatives, ensuring no faults are missed. The composite anomaly score effectively tracks degradation progression, thereby rendering the system highly reliable and practical for predictive maintenance applications. This approach combines statistical signal processing with probabilistic deep learning to deliver a robust, explainable, and adaptive solution for bearing health monitoring.

Keywords—bearing fault diagnosis, anomaly detection, Independent Component Analysis (ICA), Bayesian Long Short-Term Memory (LSTM), predictive maintenance, uncertainty quantification, Case Western Reserve University (CWRU) dataset, vibration analysis

Cite: Primawati, Ferra Yanuar, Dodi Devianto, Remon Lapisa, Dwiprima Elvanny Myori, and Fazrol Rozi, "A Hybrid Bayesian ICA-LSTM Framework for Unsupervised-Like Anomaly Detection in Rolling Element Bearings," International Journal of Mechanical Engineering and Robotics Research, Vol. 15, No. 2, pp. 114-122, 2026. doi: 10.18178/ijmerr.15.2.114-122

Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).