Advanced Prognostic Models for Predictive Maintenance: A Comparative Analysis of Machine Learning and Deep Learning Approaches

Predictive maintenance is an essential technique used by organizations to reduce unexpected equipment failures and improve operational effectiveness. Estimating the Remaining Useful Life (RUL) of machinery is an essential aspect of this process, which helps industries like manufacturing, water treatment, and aerospace schedule maintenance in advance and avoid costly breakdowns. This study looks at a number of methods for estimating Remaining Useful Life (RUL), such as hybrid approaches, statistical methods, and machine learning models. Convolutional neural networks (CNNs) and long short-term memory (LSTM) networks are examples of deep learning techniques that make it easier to analyze sensor data, while physics-based models improve accuracy by taking consideration of actual conditions. Planning maintenance for complex systems is improved when these approaches are included into hybrid models.Some issues still exist without technological developments, such as high processing power requirements, challenges handling real-time data, and a limited ability to understand predictions produced by AI. While accuracy has increased with advanced techniques like Bayesian neural networks, particle filtering, and Automated Machine Learning (AutoML), more research is required for real-time implementation. This study highlights the use of Remaining Useful Life (RUL) prediction in water purification systems, in which artificial intelligence establishes the best time to replace filters, maintaining water purity while decreasing costs.