A self-growth convolution network for thermal and mechanical fault detection with very limited engine data  

作　　者：Gou Xin Zhu Xiaolong Wang Xinwei Wang Hui Zhang Junhong Lin Jiewei 

机构地区：[1]State Key Laboratory of Engines,Tianjin University,300350,China [2]School of Mechanical Engineering,Renai College,Tianjin,30163,China [3]Weichai Power Co.Ltd.,Weifang,261043,China

出　　处：《Energy and AI》2024年第4期431-444,共14页能源与人工智能(英文)

基　　金：support of the National Key R&D Program of China(Grant No.2021YFD2000303);the Weichai Power Co.,Ltd(WCDL-GH-2023-0147).

摘　　要：Severe faults occur infrequently but are critical for the prognostics and health management(PHM)of power machinery.Due to the scarcity of fault data,diagnostic models are always facing a very limited data problem.Basic convolutional neural networks require a large number of samples to train,and widely used data augmentation methods are influenced by data quality,which can exacerbate overfitting.To address this issue,a self-growth convolution network(SGNet)is proposed to make the deep learning process a self-growing scheme in both depth and width dimensions.The direct similarity measurement is utilized to supervise the depth-growth in the layer-by-layer training process.The feature redundancy metric is employed to control the width expansion.The self-growth scheme is proposed to disrupt the coadaptation between layers and that between kernels in order to mitigate the overfitting issue of small-sample cases.The SGNet is verified and implemented in the PHM of a heavy-duty diesel engine.It exhibits remarkable diagnostic capabilities in extremely sample-limited scenarios.With only three training samples per faulty type,the recognition rates of SGNet for the misfire fault and the gear tooth fracture fault are 88.44%and 98.11%,respectively.Further,the feature contrast,the information trans-mission,the noise resistance,and the frequency domain activation heat of SGNet are discussed by the ablation experiment in detail.The results indicate a novel path to solve the data-limitation problem in the PHM of important power machinery.

关 键 词：Diesel engine Small-sample fault diagnosis Deep learning Self-growth convolution networks Prognostic health management 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]