Advancing Predictive Maintenance: Median-Based Particle Filtering in MOSFET Prognostics

• Main Authors: Hadi E.F., Baharuddin M.Z.B., Zuhdi A.W.M.
• Other Authors: 59508922600
• Format: Article
• Published: International Information and Engineering Technology Association 2025

The burgeoning demand for effective predictive maintenance in industrial systems necessitates accurate prognostics of equipment's Remaining Useful Life (RUL). In particular, the prediction of RUL plays a crucial role in MOSFET devices for many applications to prevent failures and maintenance schedule optimization. Motivated by the potential requirement for more than what traditional models can provide, this study sought to improve upon these estimates. This research aimed to solve the problem of accurately estimating RUL for MOSFET devices, since these systems are characterized by substantial uncertainties and non-linear processes. To address this issue, we introduced a high-performance prognostic model that promises to take advantage of both the Adaptive Particle Filter (APF) and Gaussian Process Regression (GPR), which is called as PF-GPR method. The model used a genetic algorithm to control when and how many particles were adaptively resampled applying different weighted strategies (mean or median) in response of the stochastic system deterioration. We designed and conducted a series of experiments based on the PF-GPR approach with two objectives: i) to benchmark different systematic resampling schemes; ii) demonstrate that even in case of small number (i.e. FloatTensor section {newString 10}0/75k). Systematic comparisons with standard particle filtering techniques showed the model performed well in tracking RUL as it degraded through wear degrees and estimated prediction errors were obtained. The results showed that the PF-GPR model significantly outperformed traditional methods, in particular their adpative resampling version based on the median. The PF-GPR median approach performed consistently better with respect to true RUL approximation and had the lowest RMSE for all time points. These results highlight the improved strength of prediction power for equipment RULs using our provided approach, thus reinforcing that real-world predictive maintenance applications are possible by said model. ?2024 The authors.