Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology (Deemed to be University), Avadi, Chennai, Tamil Nadu, India.
Predictive maintenance (PdM) in mechatronic systems demands high-precision failure prediction and interpretability for real-time operational decisions. This study presents a hybrid expert system integrating symbolic reasoning and Deep Neural Networks (DNNs) to enhance predictive accuracy and semantic traceability. The symbolic layer consists of 42 fuzzy inference rules, enabling domain expert interpretability, while the neural network layer comprises a 4-layer feedforward architecture with 128-64-32-1 units using ReLU and sigmoid activations. Experiments were conducted on a real-world dataset, and the hybrid model achieved an accuracy of 96.8%, a precision of 94.22%, and a recall of 97.31%, outperforming conventional Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) models, and rule-based systems by margins of 3.2–7.8%. The proposed method reduced false positives by 21.4% and improved time-to-failure prediction by 18.7% compared to standalone models. Maintenance scheduling optimized using the proposed model yielded a 14.5% reduction in unplanned downtime. The hybrid inference strategy not only improved prediction granularity but also supported rule-based diagnostics. This framework significantly advances predictive intelligence in safety-critical mechatronic domains.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Venkatesh S, Chandravadhana S, Rajesh R, Sagar Imambi S, Arivazhagan D and Vedaraj M;
Writing- Original Draft Preparation: Venkatesh S, Chandravadhana S, Rajesh R, Sagar Imambi S, Arivazhagan D and Vedaraj M;
Visualization: Venkatesh S, Chandravadhana S and Rajesh R;
Investigation: Sagar Imambi S, Arivazhagan D and Vedaraj M;
Supervision: Venkatesh S, Chandravadhana S and Rajesh R;
Validation: Sagar Imambi S, Arivazhagan D and Vedaraj M;
Writing- Reviewing and Editing: Venkatesh S, Chandravadhana S, Rajesh R, Sagar Imambi S, Arivazhagan D and Vedaraj M; All authors reviewed the results and approved the final version of the manuscript.
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Venkatesh S
Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India.
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Cite this article
Venkatesh S, Chandravadhana S, Rajesh R, Sagar Imambi S, Arivazhagan D and Vedaraj M, “A Hybrid Expert System Using Symbolic Reasoning and Neural Networks for Predictive Maintenance in Mechatronic Systems”, Journal of Machine and Computing, vol.5, no.4, pp. 2603-2614, October 2025, doi: 10.53759/7669/jmc202505200.
