2019· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
A structured methodology for ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models is proposed, offering valuable insights for developing efficient and scalable PdM solutions.
Abstract
Predictive Maintenance (PdM) is a key component of Industry 4.0, enabling intelligent and data-driven management of industrial assets. Traditional maintenance strategies are no longer sufficient for complex cyber-physical systems, where reliability and efficiency are critical. With the rise of Industrial IoT (IIoT), large volumes of data can be analyzed using machine learning (ML) techniques to predict equipment failures, estimate remaining useful life (RUL), and optimize maintenance schedules. This paper provides a comprehensive study of ML-based PdM frameworks, covering data-driven, physics-based, and hybrid approaches, including supervised, unsupervised, and deep learning models. A structured methodology is proposed, involving data acquisition, preprocessing, feature engineering, model development, and deployment. Key aspects such as degradation modeling, anomaly detection, and performance evaluation are discussed. Challenges including data imbalance, interpretability, scalability, cybersecurity, and real-time implementation are also analyzed. The paper concludes with future directions such as explainable AI, digital twins, federated learning, and autonomous maintenance systems, offering valuable insights for developing efficient and scalable PdM solutions.
The findings reveal that ML algorithms significantly improve fault detection, Remaining Useful Life (RUL) estimation, and maintenance decision-making, but challenges related to data quality, model interpretability, cybersecurity, and integration with legacy systems continue to affect implementation effectiveness.
P. Siva, Sankar Shunmuga, Sundaram et al.· Stanzaleaf International Jou...· 0 citations
The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning, and explainability techniques such as SHAP, LIME, and rule extraction.
Narendra Karmarkar· International Journal of Mod...· 0 citations
Artificial intelligence-driven predictive maintenance represents a critical enabler of operational excellence, resilient manufacturing systems, and sustainable industrial transformation in the era of Industry 4.0.
Banoth Samya, V. Ramesh, A. Vathsala et al.· Journal of Intelligent Decis...· 0 citations
Experimental results demonstrate that the proposed framework achieves high fault prediction accuracy, enhances system reliability, reduces maintenance costs, and supports data-driven decision-making in industrial environments.
Govind D. More, Shreyas Hon, Piyush Kotkar et al.· International Journal of Cre...· 0 citations
Timely asset maintenance remains a critical challenge in Industry 4.0 environments. Predictive Maintenance aims to anticipate failures and estimate Remaining Useful Life (RUL), enabling cost reduction and minimizing production downtime. However, real-world industrial scenarios are often characterized by noisy telemetry data, incomplete information about operating conditions, and weak degradation signals, which limit the effectiveness of conventional data-driven approaches. This paper proposes a methodology for RUL prediction under such challenging conditions, leveraging raw sensor telemetry without requiring detailed knowledge of machine operating characteristics. The approach introduces a novel Degradation Index, combined with a Health Index, to better represent degradation patterns. Additionally, signal preprocessing techniques, including Savitzky–Golay and Kalman filters, are applied to mitigate noise and improve data quality. The methodology integrates statistical analysis, similarity-based pattern extraction, and machine learning techniques, including Convolutional Neural Networks and Long Short-Term Memory models, for feature selection and prediction. Experiments conducted on real-world industrial datasets demonstrate that the proposed approach significantly improves prediction performance, achieving high accuracy and enabling failure anticipation up to five days in advance. The results highlight the importance of feature engineering and signal processing in PdM applications, showing that combining degradation modeling with deep learning yields robust, generalizable RUL predictions, even in noisy, partially observed environments.
Tiago Zonta, C. D. da Costa, F. Zeiser et al.· Scientific Reports· 0 citations
The rapid digital transformation of industrial manufacturing has introduced digital twin (DT) technology as a cornerstone for intelligent maintenance systems within Industry 4.0 and Industry 5.0 environments. This study presents an empirical investigation of digital twin–based predictive maintenance (PdM) using machine learning algorithms on real-world industrial sensor data. The research evaluates the performance of Random Forest, Gradient Boosting, Support Vector Machine, and Artificial Neural Networks in predicting equipment failures and optimizing maintenance strategies. A dataset of over 10,000 machine operation records, including temperature, vibration, pressure, and operational cycles, was analyzed to assess predictive accuracy and operational impact. Results indicate that Random Forest achieved the highest predictive accuracy (92.4%), while digital twin integration reduced unplanned machine downtime by approximately 28% compared to reactive maintenance approaches. The study highlights vibration and temperature as the most critical indicators of machine failure, demonstrating the importance of sensor-driven monitoring in predictive maintenance. Findings further show that digital twin–enabled predictive maintenance supports proactive maintenance planning, human-centered decision-making, and operational efficiency, bridging the gap between Industry 4.0 automation and Industry 5.0 human–AI collaboration.
Ifebadiofu Matthew Okoro, Perseverance Omoh Agbi· International journal of res...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
