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.
Highlights
The engine is the key component of an aircraft, and maintaining its health is a key issue in ensuring civil aviation safety
In order to test the advisability of the application of the proposed model for the wear fault diagnosis of aircraft engines, we used the random forest (RF) algorithm for comparison, in order to illustrate the desirability and superiority of the bagging-broad learning system (BLS) algorithm
Random forest is a classical ensemble learning algorithm [40], where decision trees are used as sublearners to obtain better accuracy
Summary
The engine is the key component of an aircraft, and maintaining its health is a key issue in ensuring civil aviation safety. A wear fault usually results in different types of wear particles, which can provide a basis for the monitoring and analysis of fatigue failures [7,8]. The basis of ferrography analysis is to separate abraded iron from an oil sample by using a magnetic method, which is observed under a microscope, and a qualitative and quantitative analysis is conducted on it This method can provide detailed information about the abraded particles, such as the type and quantity, but can Energies 2019, 12, 4750; doi:10.3390/en12244750 www.mdpi.com/journal/energies. A new method for wear fault diagnosis—a broad learning system (BLS) based on ensemble learning (bagging-BLS)—is proposed to deal with the oil analysis data. The results show that the proposed method can extract the knowledge rules of wear fault diagnosis in aeroengines well and has high recognition accuracy.
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