Fault Diagnosis under Variable Operating Conditions for Rotating Machinery Based on MKPCA and Domain Adaptive DBN

Under varying operating conditions, the operating state of rotating equipment is influenced by multiple factors, presenting the characteristics of complex and changeable data distribution and high nonlinearity. Deep belief networks (DBNs) possess powerful feature learning capabilities, making them suitable for fault diagnosis under such complex conditions. However, traditional DBNS have limitations, such as structural design relying on experience and being prone to getting stuck in local optima. To cope with these issues, a fault diagnosis method based on improved DBN is presented. First, a multiple kernel principal component analysis is proposed. The radial basis function kernel and the polynomial function kernel are combined, and the weights of the kernel functions are dynamically adjusted according to different characteristics to improve the complex data processing ability. Second, the particle swarm optimization algorithm is introduced to improve the network structure and parameters of the DBN. Then, the shortcomings of traditional DBN structure design that relies on experience and is prone to fall into local optimum is overcame. In addition, this article combines semi-supervised domain adaptation with DBN and introduces the multiple kernel maximum mean difference, forcing DBN to learn general features, reducing the distribution differences between domains, and transferring the knowledge learned in the source domain to the target domain, thus solving the problem of insufficient model generalization ability caused by changes in working conditions. Finally, through experimental verification, the results indicate that the proposed method significantly enhances the accuracy and generalization ability of fault diagnosis of rotating equipment under varying operating conditions.