On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Fault prognostics using dynamic wavelet neural networks
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
New sequential Monte Carlo methods for nonlinear dynamic systems
Statistics and Computing
On time series model selection involving many candidate ARMA models
Computational Statistics & Data Analysis
Fatigue crack growth estimation by relevance vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Machine condition prognosis is an important part of the decision-making in condition-based maintenance. By predicting the degradation of working conditions of machinery, it can organize a predictive maintenance program and prevent production loss. For complex systems, the trending data of the performance degradation is nonlinear over time known as a time series. This paper proposes a prognosis algorithm applied in a real dynamic system. Sequential Monte Carlo method, also known as a particle filter, can be used in nonlinear systems without any assumption of linearity. It is based on the sequential important sampling and resampling algorithm, which represents the posterior probability density function by a set of randomly drawn samples (called particles) and their associated weights. The prediction estimations are computed based on those samples and their weights. The real trending data of low methane compressors acquired from condition monitoring routines is employed for evaluating the proposed method. The results show that the proposed method offers a potential to predict the trending data in real systems of machine condition prognosis.