Abstract
Prognosis and remaining useful life (RUL) estimation of components and systems (C&S) are vital for intelligent asset-integrity management. The implementation of the traditional multi-level particle filter (TRMPF) has improved prognosis when compared with the one-step traditional particle filter that depended on the first-order state equation. However, despite this improvement, the need to enhance the accuracy of fault prognosis, diagnosis and detection cannot be overemphasized. To this end, an optimal multi-level particle filter (OPMPF) algorithm that combines genetic algorithm (GA) optimization and multi-level particle filter (MPF) techniques is used to predict the RUL of the C&S in order to enhance the accuracy of the estimation at different forms of deterioration in operation. A 9-fold cross-validation ensemble MPF that utilized lithium-ion (Li+) batteries’ charge capacity decay to test the developed OPMPF algorithm showed an improvement of over 200% in the estimated RUL when compared with the TRMPF estimation.
Highlights
Since the components and systems (C&S) of microelectronics and other complex electromechanical systems degrade over the period of operation, it is important that the trend of the deterioration is understood to enhance maintenance management, forestall downtime and optimize operating cost
The objective of this study is to develop a technique for prognostics health monitoring and estimation of the remaining useful life (RUL) of C&S, and test the developed algorithm with Li+ batteries’ charge capacity fade over the cycle time
To better estimate the retained life of C&S, an algorithm that combines multi-level particle filter (MPF) and genetic algorithm (GA) optimization was used in an optimal multi-level particle filter (OPMPF) to determine the RUL
Summary
Since the components and systems (C&S) of microelectronics and other complex electromechanical systems degrade over the period of operation, it is important that the trend of the deterioration is understood to enhance maintenance management, forestall downtime and optimize operating cost. Using MPSM to extract the decay pattern of the Li+ batteries’ charge capacities will help to show the uniqueness in the GA estimated charge capacities at each generation This gives more credence to the optimal multi-level particle filter (OPMPF) estimation results given that the MPF will be implemented with distinct charge decay information that depicts the original experimental results. This procedure will be in sharp contrast to the technique of using similar parametric values to determine the OPMPF by other researchers [22] who embarked on a similar study. The new algorithm for OPMPF was compared with the TRMPF in Section 4 while results and discussion are discussed in Sections 5 and 6 was used for concluding the findings of the study
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