Research On Equipment Fault Diagnosis Algorithm Based On Depth Domain Adaptation

With the acceleration of industrialization and the improvement of intelligent level,the reliabilit y and stability of equipment have become important factors for intelligent manufacturing enterprises to improve production efficiency and reduce costs.Traditional equipment fault diagnosis methods a re usually based on manual experience and professional knowledge,which have low diagnostic effic iency and are prone to missed and wrong diagnosis,making it difficult to meet the needs of equipme nt fault diagnosis in the era of big data.Data-driven intelligent fault diagnosis methods can complete the diagnosis process in a short time,thus improving the reliability of industrial production equipm ent and the efficiency of industrial production.Therefore,this thesis focuses on the research of datadriven intelligent fault diagnosis methods,and the main research contents are as follows:1.Conditionally adversarial domain adaptation-based fault diagnosis algorithm: In response to the issue of the decrease in the generalization and adaptation abilities of deep learning-based fault di agnosis models under varying operating conditions,this thesis proposes an intelligent fault diagnosi s model CDATN based on conditionally adversarial training and domain adaptation.Adversarial trai ning is used for fault diagnosis models and improved by using a domain discriminator to distinguish the data features of source and target domains,thereby aligning the feature space distributions of so urce and target domains.Through comprehensive comparative experiments,it is demonstrated that t he proposed CDATN diagnostic model exhibits significant improvements in feature extraction abilit y under varying operating conditions compared to mainstream diagnostic models.2.Feature fusion-based domain adaptation fault diagnosis algorithm: To address the issue of li mited recognition accuracy of diagnostic models under noisy backgrounds and weak correlation bet ween signal preprocessing and intelligent models,this thesis proposes an anti-noise intelligent fault diagnosis model VMD-ECA-DTN based on feature fusion and domain adaptation.Firstly,for the no ise problem,VMD is used to effectively separate and frequency-domain partition non-stationary equ ipment fault signals,obtaining relatively stable sub-signals.Then,based on signal decomposition,E CA is used to reweight and learn to fuse decomposed signal channel features more effectively.Finall y,combined with the joint maximum average distance metric,the input features and predicted labels are aligned in the joint distribution of source and target domains.Under input with added interferin g noise,the algorithm achieves the highest average diagnostic accuracy of 99.03% on the CWRU da tabase.3.Intelligent Fault Diagnosis System for Battery Production Equipment: To address the challen ge of implementing intelligent fault diagnosis models in practical scenarios,this study designed and implemented an industrial intelligent fault diagnosis system based on the proposed CDATN and VM D-ECA-DTN fault diagnosis models for battery production equipment.Finally,a fault diagnosis dat aset for coating machine battery production equipment was built,and the robustness of the diagnosis algorithm and the feasibility of the intelligent fault diagnosis system for lithium-ion battery product ion equipment were verified through data collection,data preprocessing,intelligent fault diagnosis t esting,and system visualization testing.This study provides a new reference case for the implement ation of intelligent fault diagnosis algorithms.