Abstract
Since the operating environment of a turboshaft engine is complex and harsh, gas path components are easily damaged. It is quite important of gas path fault diagnosis for turboshaft engine from the huge accumulated data. The paper presents the data-driven ways of learning machine algorithms for gas path fault diagnosis of the engine and the performance comparisons. Various Extreme Learning Machine (ELM), including basic ELM, Kernel ELM (KELM) and Multiple Layer KELM (MLKELM), are involved. Besides, the Deep Belief Network (DBN) as one of the deep learning machines is also employed to recognize fault patterns. The simulation is carried out on a turboshaft engine with typical gas path fault modes. The results show that the MLKELM and DBN provide better classification accuracy than the ELM and KELM, and the DBN consumes less training time compared to the MLKELM.
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