Cavitation recognition of axial piston pumps in noisy environment based on Grad-CAM visualization technique  

作　　者：Qun Chao Xiaoliang Wei Jianfeng Tao Chengliang Liu Yuanhang Wang 

机构地区：[1]State Key Laboratory of Mechanical System and Vibration,Shanghai Jiao Tong University,Shanghai,China [2]State Key Laboratory of Fluid Power and Mechatronic Systems,Zhejiang University,Hangzhou,China [3]MoE Key Laboratory of Artificial Intelligence,AI Institute,Shanghai Jiao Tong University,Shanghai,China [4]China Electronic Product Reliability and Environmental Testing Research Institute,Guangzhou,China [5]Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology,Guangzhou,China

出　　处：《CAAI Transactions on Intelligence Technology》2023年第1期206-218,共13页智能技术学报（英文）

基　　金：National Key R&D Program of China,Grant/Award Number:2018YFB1702503;Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems,Grant/Award Number:GZKF-202108;Open Foundation of the Guangdong Provincial Key Laboratory of Electronic Information Products Reliability Technology;China National Postdoctoral Program for Innovative Talents,Grant/Award Number:BX20200210;China Postdoctoral Science Foundation,Grant/Award Number:2019M660086。

摘　　要：The cavitation in axial piston pumps threatens the reliability and safety of the overall hydraulic system.Vibration signal can reflect the cavitation conditions in axial piston pumps and it has been combined with machine learning to detect the pump cavitation.However,the vibration signal usually contains noise in real working conditions,which raises concerns about accurate recognition of cavitation in noisy environment.This paper presents an intelligent method to recognise the cavitation in axial piston pumps in noisy environment.First,we train a convolutional neural network(CNN)using the spectrogram images transformed from raw vibration data under different cavitation conditions.Second,we employ the technique of gradient-weighted class activation mapping(Grad-CAM)to visualise class-discriminative regions in the spectrogram image.Finally,we propose a novel image processing method based on Grad-CAM heatmap to automatically remove entrained noise and enhance class features in the spectrogram image.The experimental results show that the proposed method greatly improves the diagnostic performance of the CNN model in noisy environments.The classification accuracy of cavitation conditions increases from 0.50 to 0.89 and from 0.80 to 0.92 at signal-to-noise ratios of 4 and 6 dB,respectively.

关 键 词：axial piston pump cavitation recognition CNN Grad-CAM spectrogram image 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]