Machine Learning Applications and Techniques for Predictive Maintenance in Industrial Operations: A Review

Abstract

The rapid development of Industry 4.0 has transformed industrial systems because it has allowed the use of data-driven solutions to monitor equipment and prevent faults. Predictive maintenance (Pd.M.), which utilizes advanced analytics and uses artificial intelligence to predict when a breakdown is likely to happen, is becoming more and more prevalent in the industry, in addition to more traditional methods like reactive and preventative maintenance.  Pd.M. can utilize event logs, control systems, and real-time sensor data streams to enhance equipment availability, minimize downtime, and allocate resources as efficiently as possible.  Strong anomaly detectors, defect classifiers, and Remaining Useful Life (RUL) predictions may be obtained using machine learning (ML) and deep learning (DL) models, which are regarded as crucial tools in Pd.M. Across a range of industrial situations, Random Forest (RF) methods, Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) may be applied with great predictive flexibility. Additionally, scalability, efficiency, and sustainability are improved in contemporary operations through integration with digital twins, the Industrial Internet of Things (IIoT), and quantum-enhanced techniques. These developments notwithstanding, there are still challenges, including low data quality, heavy computing requirements, and barriers to adoption by organizations. However, with the further implementation of smart PdM systems, operational efficiency can be enhanced, safety can increase, and sustainable industrial growth can be achieved, marking a crucial step toward smarter and healthier industrial ecosystems.