Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model's effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Center for Information Analytical and Technical Support of Nuclear Power Facilities Monitoring National Academy of Sciences of Ukraine Akademika Palladina Avenue 34 A Kyiv Ukraine

College of Engineering University of Business and Technology Jeddah 21448 Saudi Arabia

Department of Electrical Engineering Graphic Era Dehradun 248002 India

Department of Mechanical Engineering University of Chlef Ouled Fares Algeria

Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment Institute of Electrodynamics National Academy of Sciences of Ukraine Beresteyskiy 56 Kyiv 57 03680 Ukraine

Hourani Center for Applied Scientific Research Al Ahliyya Amman University Amman Jordan

LGEM Laboratory Faculty of Technology University of Batna 2 Batna Algeria

LGMM Laboratory Faculty of Technology University of 20 August 1955 Skikda Algeria

SIME Laboratory University of Tunis ENSIT Tunis Tunisia

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