Jul 2026· 2026 6th International Conference on Electrical, Computer and Energy Technologies (ICECET)· pp. 1-6· 0 citations· 23 references
Abstract
Ensuring reliable equipment operations are critical for production efficiency and safety compliance in manufacturing industries. Unexpected machine breakdowns not only disrupt operations but also increase maintenance costs and safety risk. Traditional approaches — whether reactive or preventive — often fail to incorporate real-time equipment data and often overlook early fault indicators. Predictive maintenance strategies based on artificial intelligence models, address these shortcomings by monitoring machine conditions in real time, detect anomalies and forecast failures. Thus, this study proposes a refined deep neural network to classify machine health and estimate failure probability, thereby emphasizing Remaining Useful Life (RUL) prediction as a strategy for optimizing maintenance scheduling. In this study, the AI4I 2020 Predictive Maintenance dataset, which contains 10,000 records of machine operating conditions, including air and process temperatures, rotational speed, torque, tool wear, product type, and failure status was used for the evaluation of the proposed model. To improve the proposed model's performance, detailed preprocessing steps were deployed on the dataset. These steps include some preliminary categorical encoding and feature standardization on the dataset while class weighting, SMOTE and focal loss were deployed for handling class imbalance issues. According to the results achieved, the proposed model performed better across all metrics considered in comparison with similar models including baseline models. By integrating data-driven AI techniques with predictive maintenance strategies, this research study demonstrates how manufacturing plants can reduce downtime, extend machine lifespan, and minimize unnecessary maintenance interventions.
Industry 4.0 has transformed manufacturing through the integration of Industrial IoT (IIoT), cyber-physical systems, cloud computing, and artificial intelligence, making predictive maintenance (PdM) a key strategy for improving equipment reliability. Unlike traditional maintenance, AI-driven PdM analyzes real-time sensor data to predict equipment failures before they occur. However, many AI models operate as black boxes, limiting trust and interpretability. The proposed Explainable AI-Based Predictive Maintenance Framework (XAI-PMF) addresses this challenge by integrating IIoT sensing, intelligent feature engineering, hybrid machine learning (Random Forest, Gradient Boosting, LSTM, and Transformers), and explainability techniques such as SHAP, LIME, and rule extraction. These methods provide transparent fault predictions and maintenance recommendations by highlighting the factors influencing equipment degradation. Continuous learning further enables adaptive model updates as new operational data become available. Overall, the framework improves prediction accuracy, reduces downtime and false alarms, enhances maintenance scheduling, and supports trustworthy, intelligent asset management for next-generation smart factories.
Narendra Karmarkar· International Journal of Mod...· 0 citations
Predictive maintenance has emerged as a transformative strategy within Industry 4.0, enabling organizations to transition from reactive and preventive maintenance approaches toward intelligent, data-driven asset management. The convergence of artificial intelligence, Industrial Internet of Things (IIoT), cyber-physical systems, cloud computing, digital twins, and edge computing facilitates continuous monitoring of equipment health, early fault diagnosis, and accurate prediction of component failures. These capabilities significantly reduce unexpected machine downtime, optimize maintenance scheduling, extend equipment lifespan, minimize operational costs, and improve production quality. Artificial intelligence techniques, including machine learning, deep learning, reinforcement learning, and hybrid predictive analytics, enhance the ability to process large-scale industrial data and generate reliable maintenance decisions in real time. Furthermore, predictive maintenance supports sustainability objectives through improved resource utilization, energy efficiency, and reduced material waste while strengthening organizational competitiveness. Despite implementation challenges related to data quality, interoperability, cybersecurity, and model interpretability, continuous technological advancements are accelerating industrial adoption across manufacturing, energy, transportation, healthcare, and process industries. Consequently, 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
The increasing adoption of smart manufacturing technologies has intensified the need for reliable predictive maintenance solutions to reduce unexpected equipment failures and production downtime. Rotating machinery, including bearings, electric motors, gearboxes, pumps, and turbines, represents a critical class of industrial assets whose degradation directly affects manufacturing productivity, operational safety, and maintenance costs. This study presents a trustworthy AI-driven framework for condition monitoring and Remaining Useful Life (RUL) prediction of rotating machinery in smart manufacturing environments. The proposed framework integrates multi-sensor condition monitoring using vibration, temperature, acoustic emission, pressure, and motor current measurements with machine learning and deep learning models for intelligent fault diagnosis and prognostics. To improve transparency and industrial trust, the framework incorporates Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, to identify the operational factors influencing prediction outcomes. In addition, Digital Twin technology and Physics-Informed Artificial Intelligence are integrated to enhance prediction reliability and maintain consistency with engineering knowledge. The framework is evaluated using benchmark datasets and standard classification and regression metrics, including Accuracy, Precision, Recall, F1-Score, MAE, RMSE, and RUL prediction error. The results demonstrate that the integration of multi-sensor monitoring, explainable AI, and Digital Twin-assisted analysis improves diagnostic reliability, prediction consistency, and maintenance decision support. The proposed approach offers practical benefits for Industry 4.0 applications by reducing unplanned downtime, optimizing maintenance scheduling, improving equipment availability, and enhancing the trustworthiness of AI-based predictive maintenance systems for critical rotating machinery.
Umme Habiba Aesha, Reyan Hridoy Bhuiyan, Yeasir Arafat Ayan· International Journal of Eng...· 0 citations
Predictive maintenance has become an important strategy in the aviation industry for improving aircraft reliability, safety, and operational efficiency. With the increasing availability of sensor data from aircraft engines, Machine Learning (ML) techniques can be used to detect degradation patterns and predict failures before they occur. However, many previous studies focus on isolated tasks such as fault detection or Remaining Useful Life (RUL) prediction and often rely on complex Deep Learning (DL) models without efficient feature selection, leading to high computational cost and limited interpretability. The study proposes a hybrid predictive maintenance framework that combines Temporal Convolutional Networks (TCN) for temporal feature extraction with Genetic Algorithms (GA) to select optimal features and Variational Autoencoders (VAE) for detecting anomalies and Transformer-based models to classify failures and predict RUL. The model is evaluated using the NASA CMAPSS turbofan engine dataset. Experimental results indicate that the proposed method is able to achieve a high classification accuracy of 98.71% and reduce costs associated with maintenance by 46.25%, compared to traditional methods of maintaining an aircraft. The results of this study indicate that the proposed hybrid method has the potential to improve maintenance-related decision-making, improve aircraft reliability, and reduce operational cost. Additionally, these results show that there are a significant number of opportunities to deploy intelligent predictive maintenance systems to aviation operations across the globe.
P. M. Shimpi, Ajay Sawarkar· Discover Vehicles· 0 citations
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.
Sithik Shah· International Journal of App...· 0 citations
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industry 4.0 objectives.
Gajula Prasad Gajula Prasad, Bolloju Divya Sri Bolloju Divya Sri, Dr B Ramprasad Dr B Ramprasad· International Journal of Sci...· 0 citations
