Wear Fault Diagnosis of Aeroengines Based on Broad Learning System and Ensemble Learning

Mengmeng Wang, Quanbo Ge, Haoyu Jiang and Gang Yao
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Mengmeng Wang: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China
Quanbo Ge: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Haoyu Jiang: Institute of Automation, Southeast University, Nanjing 211189, China
Gang Yao: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

Energies, 2019, vol. 12, issue 24, 1-16

Abstract: An aircraft engine (aeroengine) operates in an extremely harsh environment, causing the working state of the engine to constantly change. As a result, the engine is prone to various kinds of wear faults. This paper proposes a new intelligent method for the diagnosis of aeroengine wear faults based on oil analysis, in which broad learning system (BLS) and ensemble learning models are introduced and integrated into the bagging-BLS model, in which 100 sub-BLS models are established, which are further optimized by ensemble learning. Experiments are conducted to verify the proposed method, based on the analysis of oil data, in which the random forest and single BLS algorithms are used for comparison. The results show that the output accuracy of the proposed method is stable (at 0.988), showing that the bagging-BLS model can improve the accuracy and reliability of engine wear fault diagnosis, reflecting the development trend of fault diagnosis in implementing intelligent technology.

Keywords: aircraft engine; fault diagnosis; broad learning system; ensemble learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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