Abstract
This paper presents an approach to detect and classify the faults in complex systems with small amounts of available data history. The methodology is based on the model fusion for fault detection and classification. Moreover, the database is enriched with additional samples if they are correctly classified. For the fault detection, the kernel principal component analysis (KPCA), kernel independent component analysis (KICA) and support vector domain description (SVDD) were used and combined with a fusion operator. For the classification, extreme learning machine (ELM) was used with different activation functions combined with an average fusion function. The performance of the methodology was evaluated with a set of experimental vibration data collected from a test-to-failure bearing test rig. The results show the effectiveness of the proposed approach compared to conventional methods. The fault detection was achieved with a false alarm rate of 2.29% and a null missing alarm rate. The data is also successfully classified with a rate of 99.17%.
Highlights
Because of higher requirements in terms of effectiveness and economic performances, modern industrial systems have become more complex [1]
The processes are more complex as they involve different physical phenomenon and highly targets
The processes are more complex as they involve different physical phenomenon nonlinear behaviors
Summary
Because of higher requirements in terms of effectiveness and economic performances, modern industrial systems have become more complex [1]. When the distribution is more complex the usual fault detection methods have lower performances. Learning Lachine (ELM) and Support Vector Machine (SVM) have good performances with a nonlinear process, with no assumptions on the variables. Vapnik et al proposed the Support Vector Machine (SVM) to address the classification of small sample data [32]. SVM is another application of the kernel method, which has better generalization performance and nonlinear processing capability [33]. Despite that it can extract nonlinear features and effectively detect the faults, it requires a lot of time in the training stage and testing stage.
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