Open Access

ARTICLE

Leveraging Safe and Secure AI for Predictive Maintenance of Mechanical Devices Using Incremental Learning and Drift Detection

Prashanth B. S^1,*, Manoj Kumar M. V.^2,*, Nasser Almuraqab³, Puneetha B. H⁴

1 Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Visvesvaraya Technological University, Belagavi, 590018, India
2 Department of Information Science and Engineering, Nitte Meenakshi Institute of Technology, Bangalore, 560064, India
3 Dubai Business School, University of Dubai, Academic City, Dubai, P.O. Box 14143, United Arab Emirates
4 Department of Computer Science and Business Systems, Bapuji Institute of Engineering and Technology, Davangere, 577004, India

* Corresponding Authors: Prashanth B. S. Email: ; Manoj Kumar M. V.. Email:

(This article belongs to the Special Issue: Safe and Secure Artificial Intelligence)

Computers, Materials & Continua 2025, 83(3), 4979-4998. https://doi.org/10.32604/cmc.2025.060881

Received 12 November 2024; Accepted 08 February 2025; Issue published 19 May 2025

Abstract

Ever since the research in machine learning gained traction in recent years, it has been employed to address challenges in a wide variety of domains, including mechanical devices. Most of the machine learning models are built on the assumption of a static learning environment, but in practical situations, the data generated by the process is dynamic. This evolution of the data is termed concept drift. This research paper presents an approach for predicting mechanical failure in real-time using incremental learning based on the statistically calculated parameters of mechanical equipment. The method proposed here is applicable to all mechanical devices that are susceptible to failure or operational degradation. The proposed method in this paper is equipped with the capacity to detect the drift in data generation and adaptation. The proposed approach evaluates the machine learning and deep learning models for their efficacy in handling the errors related to industrial machines due to their dynamic nature. It is observed that, in the settings without concept drift in the data, methods like SVM and Random Forest performed better compared to deep neural networks. However, this resulted in poor sensitivity for the smallest drift in the machine data reported as a drift. In this perspective, DNN generated the stable drift detection method; it reported an accuracy of and an AUC of while detecting only a single drift point, indicating the stability to perform better in detecting and adapting to new data in the drifting environments under industrial measurement settings.

---

Keywords

---