Abstract
In this paper, an adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method is developed for an electric scooter system with intermittent faults. Firstly, bond-graph-based fault detection and isolation is carried out to find possible faulty components in the electric scooter system. Secondly, submodels are decomposed from the global model using structural model decomposition, followed by adaptive Cuckoo search (ACS)-based distributed fault estimation with less computational burden. Then, as the intermittent fault gradually deteriorates in magnitude, and possesses the characteristics of discontinuity and stochasticity, a set of fault features that can describe the intermittent fault’s evolutionary trend are captured with the aid of tumbling window. With the obtained dataset, which represents the fault features, the ACS-ELM is developed to model the intermittent fault degradation trend and predict the remaining useful life of the intermittently faulty component when the physical degradation model is unavailable. In the ACS-ELM, the ACS is employed to optimize the input weights and hidden layer biases of an extreme learning machine, to improve the algorithm performance. Finally, the proposed methodologies are validated by a series of simulation and experiment results based on the electric scooter system.
Highlights
Mechatronic systems, which involve the synergistic integration of mechanical and electrical structures, are essential parts of modern industrial systems [1,2,3,4,5]
An adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method for an electric scooter with intermittent faults is proposed in this paper
On the other hand, considering the fact that the physical degradation models are usually unknown in practice, the data-driven prognosis method is developed to predict the remaining useful life (RUL) of intermittently faulty components
Summary
Mechatronic systems, which involve the synergistic integration of mechanical and electrical structures, are essential parts of modern industrial systems [1,2,3,4,5]. Without the exact degradation model, predicting the RUL of the intermittently faulty component based on established intermittent fault degradation features is challenging. An adaptive Cuckoo search extreme learning machine (ACS-ELM)-based prognosis method for an electric scooter with intermittent faults is proposed in this paper. On the other hand, considering the fact that the physical degradation models are usually unknown in practice, the data-driven prognosis method is developed to predict the RULs of intermittently faulty components. The ACS-ELM is proposed to model the intermittent fault feature evolutionary trend, as well as the RUL prediction of the intermittently faulty component, where ACSELM is developed by introducing adaptive Cuckoo search (ACS) into the ELM to optimize input weights and hidden layer biases.
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