Gear fault diagnosis based on CS-improved variational mode decomposition and probabilistic neural network

• Published in: Measurement : journal of the International Measurement Confederation 2022-03, Vol.192, p.110913, Article 110913
• Main Authors: Lin, Ying, Xiao, Maohua, Liu, Huijia, Li, Zhuolong, Zhou, Shuang, Xu, Xiaomei, Wang, Dicheng
• Format: Article
• Language: English
• Subjects: Cuckoo search algorithm; Decomposition; Fault detection; Fault diagnosis; Fault tolerance; Feature extraction; Gear; Gearboxes; Neural networks; Optimization; Parameter identification; Probabilistic neural network; Signal processing; Training; Variational mode decomposition
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.110913

•A diagnosis model is established based on the combination of the variational mode decomposition (VMD) and the probabilistic neural network (PNN) for gear fault.•The key factors of VMD can be selected adaptively without manual input, optimized by the cuckoo algorithm (CS).•The diagnostic accuracy of the fault diagnosis system in this paper is significantly higher than other traditional methods after training.•The shortcomings of the existing advanced signal processing and neural network methods are improved. Increasing the rate of gear fault diagnosis is crucial to research on gear fault diagnosis methods. The existing signal processing methods have modal aliasing phenomena and poor adaptability. Moreover, the neural network method has serious problems, such as complex training and low accuracy. Based on these problems, this study aims to improve the shortcomings of existing gear fault diagnosis methods, thereby enhancing the adaptability and accuracy of gear fault diagnosis systems. This study proposes a method based on the combination of the parameter optimisation of variational mode decomposition (VMD) with cuckoo search (CS) and the probabilistic neural network (PNN) for intelligent identification of gearbox faults. Firstly, the energy parameters of each mode of the signals are extracted by the method of CS-improved VMD, and a feature matrix is constructed. Then, the optimal training parameters of the PNN are selected and the PNN is trained, and the performance is evaluated by the parameters RMSEC and RMSEP. Use the data set from Southeast University of China and the experimental data, and compare with the diagnosis classification effect of four other fault diagnosis models. The diagnostic results of the experimental data show that the fault diagnosis accuracy of the method proposed in this paper can reach an average of 98.5%, proving the advancement and effectiveness of this method over existing fault diagnosis technologies.