Research on the mechanical fault diagnosis method based on sound signal and IEMD-DDCNN

作     者：Haoning Pu Zhan Wen Xiulan Sun Lemei Han Yanhe Na Hantao Liu Wenzao Li 

作者机构：Chengdu University of Information TechnologyChengduChina Education Department of Sichuan ProvinceEducation Informatization and Big Data CenterChengduChina Network and Data Security Key Laboratory of Sichuan ProvinceUESTCChengduChina 

出 版 物：《International Journal of Intelligent Computing and Cybernetics》 (智能计算与控制论国际期刊（英文）)

年 卷 期：2023年第16卷第3期

页      面：629-646页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：At the same time,the authors also appreciate the support by the fund from the Network and Data Security Key Laboratory of Sichuan Province,UESTC(NO.NDS2021-7) Sichuan Province General Education Scientific Research(NO.2019514). 

主　　题：Fault identification Neural nets DDCNN IEMD MFCC 

摘      要：Purpose–The purpose of this paper is to provide a shorter time cost,high-accuracy fault diagnosis method for water pumps.Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention.Considering the time-consuming empirical mode decomposition(EMD)method and the more efficient classification provided by the convolutional neural network(CNN)method,a novel classification method based on incomplete empirical mode decomposition(IEMD)and dual-input dual-channel convolutional neural network(DDCNN)composite data is proposed and applied to the fault diagnosis of water pumps.Design/methodology/approach–This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient(MFCC)and a neural network model of DDCNN.First,the sound signal is decomposed by IEMD to get numerous intrinsic mode functions(IMFs)and a residual(RES).Several IMFs and one RES are then extracted by MFCC features.Ultimately,the obtained features are split into two channels(IMFs one channel;RES one channel)and input into DDCNN.Findings–The Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection(MIMII dataset)is used to verify the practicability of the method.Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis.Compared with EMD,51.52% of data preprocessing time,67.25% of network training time and 63.7%of test time are saved and also improve accuracy.Research limitations/implications–This method can achieve higher accuracy in fault diagnosis with a shorter time cost.Therefore,the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.Originality/value–This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.