Predictive Maintenance Strategies for Safety-critical Mechanical Systems

Year : 2025 | Volume : 12 | Issue : 01 | Page : 12 17
    By

    Umesh Alane,

  • Khan Zulqarnain khan khalilullah,

  • Pathan Liyakhat Khan Majid Khan,

  • Sagar Bhagat,

  • Mohammad Faizan Mohammed Gafoor,

  • Shaikh Javeed Ahmed Gambeer Miyan,

Abstract

Ensuring the reliability and safety of industrial systems is essential, especially in high-risk sectors such as aerospace, manufacturing, and energy. Predictive maintenance (PdM) has become a crucial approach for minimizing operational failures and improving maintenance efficiency. This research introduces an advanced PdM framework that enhances industrial safety by integrating Internet of Things (IoT) technology, machine learning (ML), and big data analytics. By enabling real-time monitoring and predictive fault detection, this approach aims to reduce unplanned downtime, mitigate safety risks, and improve operational efficiency. The proposed approach utilizes IoT-enabled sensors to continuously track essential parameters, including vibration, temperature, pressure, and load, in industrial machinery. The collected data is processed using advanced ML techniques to detect anomalies, estimate the remaining useful life (RUL) of components, and issue early warning alerts for potential failures. Predictive models, including time-series analysis, regression algorithms, and deep learning, are utilized to enhance the accuracy of fault prediction. Additionally, a risk-based maintenance approach is incorporated to prioritize interventions based on failure severity, ensuring compliance with industrial safety regulations and reducing the likelihood of accidents. Case studies from high-risk industries validate the effectiveness of PdM in improving equipment reliability and workplace safety. Results demonstrate that predictive maintenance not only minimizes operational disruptions and maintenance costs but also plays a vital role in accident prevention and risk mitigation. The study also addresses key implementation challenges, such as data security, system integration, and workforce training, offering recommendations for effective adoption in industrial safety engineering. Future research will focus on integrating predictive maintenance with digital twins and augmented reality to further enhance safety monitoring and decision-making capabilities in high-risk industrial environments.

Keywords: Predictive Maintenance (PdM), safety-critical systems, mechanical systems, internet of things (IoT), machine learning (ML), big data analytics, real-time monitoring, remaining useful life (RUL), risk assessment, anomaly detection, vibration analysis, temperature monitoring, deep learning algorithms, failure prediction, digital twin technology

[This article belongs to Journal of Industrial Safety Engineering ]

How to cite this article:
Umesh Alane, Khan Zulqarnain khan khalilullah, Pathan Liyakhat Khan Majid Khan, Sagar Bhagat, Mohammad Faizan Mohammed Gafoor, Shaikh Javeed Ahmed Gambeer Miyan. Predictive Maintenance Strategies for Safety-critical Mechanical Systems. Journal of Industrial Safety Engineering. 2025; 12(01):12-17.
How to cite this URL:
Umesh Alane, Khan Zulqarnain khan khalilullah, Pathan Liyakhat Khan Majid Khan, Sagar Bhagat, Mohammad Faizan Mohammed Gafoor, Shaikh Javeed Ahmed Gambeer Miyan. Predictive Maintenance Strategies for Safety-critical Mechanical Systems. Journal of Industrial Safety Engineering. 2025; 12(01):12-17. Available from: https://journals.stmjournals.com/joise/article=2025/view=232941


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Regular Issue Subscription Original Research
Volume 12
Issue 01
Received 06/02/2025
Accepted 28/02/2025
Published 08/03/2025
Publication Time 30 Days
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