Blind Source Separation and Dynamic Fuzzy Neural Network for Fault Diagnosis in Machines

11th International Conference on Damage Assessment of Structures

物理学;材料科学

Huang, Haifeng^1 ; Ouyang, Huajiang^1,2 ; Gao, Hongli^1

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China^1

School of Engineering, University of Liverpool, Liverpool, United Kingdom^2

关键词: Damage assessments;    Dynamic fuzzy neural networks(D-FNN);    Fault detection and diagnosis;    Fault development;    Low-dimensional manifolds;    Mechanical fault diagnosis;    Online computations;    Sensitive features;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/628/1/012070/pdf
DOI  :  10.1088/1742-6596/628/1/012070

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Many assessment and detection methods are used to diagnose faults in machines. High accuracy in fault detection and diagnosis can be achieved by using numerical methods with noise-resistant properties. However, to some extent, noise always exists in measured data on real machines, which affects the identification results, especially in the diagnosis of early- stage faults. In view of this situation, a damage assessment method based on blind source separation and dynamic fuzzy neural network (DFNN) is presented to diagnose the early-stage machinery faults in this paper. In the processing of measurement signals, blind source separation is adopted to reduce noise. Then sensitive features of these faults are obtained by extracting low dimensional manifold characteristics from the signals. The model for fault diagnosis is established based on DFNN. Furthermore, on-line computation is accelerated by means of compressed sensing. Numerical vibration signals of ball screw fault modes are processed on the model for mechanical fault diagnosis and the results are in good agreement with the actual condition even at the early stage of fault development. This detection method is very useful in practice and feasible for early-stage fault diagnosis.