Reliable composite fault diagnosis of hydraulic systems based on linear discriminant analysis and multi-output hybrid kernel extreme learning machine

Abstract

With increasingly stringent in requirements on the reliability and safety of hydraulic systems, data-driven fault diagnosis has emerged as a popular area of research. Hydraulic systems may have multiple failure modes, and accurately diagnosing compound failures in multi-component systems is a daunting task. In this paper, a method of multi-output classification by combining linear discriminant analysis (LDA) with the hybrid kernel extreme learning machine (HKELM) is proposed to diagnose compound faults in hydraulic systems. Data selection based on LDA is used in place of expert knowledge to screen out sensitive channels of each component from multi-channel signals. The multi-output strategy is embedded into the HKELM, which can simultaneously output the fault status of multiple components to diagnose the health of the system. An improved Hamming loss is also proposed to evaluate the total error in the multi-output classification because it has greater applicative relevance than classification accuracy. The results of experiments show that the proposed method can diagnose composite faults in multi-component systems with an accuracy higher than 99.5% and an error of only 0.20% on a dataset of hydraulic systems. As a shallow feed-forward network model, it can be used for real-time fault diagnosis due to its efficiency.