James Kuria Kimotho

PEM fuel cell prognostics using particle filter with model parameter adaptation

Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.

Machinery Prognostic Method Based on Multi-Class Support Vector Machines and Hybrid Differential Evolution -- Particle Swarm Optimization

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Controlling the Remaining Useful Lifetime using Self- Optimization

Self-optimizing mechatronic systems offer possibilities well beyond those of traditional mechatronic systems. Among these is the adaptation of the system behavior to the current situation. To do so, they are able to choose from different working points, which are pre-calculated using multiobjective optimization and are thus Pareto-optimal with regard to the chosen objective functions. In this contribution, a method is presented that allows to continuously control the system degradation by adapting the behavior of a selfoptimizing system throughout its complete lifetime. The current remaining useful lifetime is estimated and then related to the spent lifetime and the desired useful lifetime. Using this information, a reliability-related objective is prioritized using a closed-loop control, which in turn is used to determine the working point of the self-optimizing system. This way, the desired useful lifetime can be achieved. To exemplify the setup of the controller structure and to demonstrate the adaptation of the system behavior, a dynamic model of a clutch system is used. It can be seen that the closed loop controller is able to correct for external perturbations, such as changed requirements, as well as changed system parameters. This way, the modeled system is able to achieve the desired lifetime reliably.