In today's industrial landscape, the pursuit of operational excellence has driven organizations to seek innovative approaches to ensure the uninterrupted functionality of machinery and equipment. Predictive maintenance (PM) provides a pivotal strategy to achieve this goal by detecting faults earlier and predicting maintenance before the system enters a critical state. This thesis proposed a fault detection and diagnosis (FDD) method for predictive maintenance using particle filter resampling and a particle tracking technique. To develop this FDD method, particle filter and hidden Markov model efficiency in the forecasting system state variables are studied on a hydraulic wind power transfer system with different noise levels and system faults. Furthermore, a particle tracker is developed to analyze the particle filter's resampling process and study the particle selection process. After that, the proposed FDD method was developed and validated through three simulation tests employing system degradation models. Furthermore, the system's remaining useful life (RUL) is estimated for those simulation tests.