Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network

Abstract

Purpose

The purpose of this study is to analyze the intelligent semisupervised fault diagnosis method of aeroengine.

Design/methodology/approach

A semisupervised fault diagnosis method based on denoising autoencoder (DAE) and deep belief network (DBN) is proposed for aeroengine. Multiple state parameters of aeroengine with long time series are processed to form high-dimensional fault samples and corresponding fault types are taken as sample labels. DAE is applied for unsupervised learning of fault samples, so as to achieve denoised dimension-reduction features. Subsequently, the extracted features and sample labels are put into DBN for supervised learning. Thus, the semisupervised fault diagnosis of aeroengine can be achieved by the combination of unsupervised learning and supervised learning.

Findings

The JT9D aeroengine data set and simulated aeroengine data set are applied to test the effectiveness of the proposed method. The result shows that the semisupervised fault diagnosis method of aeroengine based on DAE and DBN has great robustness and can maintain high accuracy of fault diagnosis under noise interference. Compared with other traditional models and separate deep learning model, the proposed method also has lower error and higher accuracy of fault diagnosis.

Originality/value

Multiple state parameters with long time series are processed to form high-dimensional fault samples. As a typical unsupervised learning, DAE is used to denoise the fault samples and extract dimension-reduction features for future deep learning. Based on supervised learning, DBN is applied to process the extracted features and fault diagnosis of aeroengine with multiple state parameters can be achieved through the pretraining and reverse fine-tuning of restricted Boltzmann machines.

Citation

Lv, D., Wang, H. and Che, C. (2022), "Semisupervised fault diagnosis of aeroengine based on denoising autoencoder and deep belief network", Aircraft Engineering and Aerospace Technology, Vol. 94 No. 10, pp. 1772-1779. https://doi.org/10.1108/AEAT-10-2020-0234