A federated learning based intelligent fault diagnosis framework for manufacturing processes with intraclass and interclass imbalance

Abstract Data based fault diagnosis technologies are important measures to improve the operation safety, stability, and reliability of manufacturing processes, which are key entry points and innovation powers to promote the intelligent manufacturing as well as improve the operation efficiency. Class-balanced datasets are often used for modeling and fault diagnosis by the traditional data based methods. However, in practical engineering applications, manufacturing processes often produce multiple classifications of imbalance data, bringing great challenges to the promotions and applications of the classical data based methods. To this end, an intelligent fault diagnosis framework is proposed for manufacturing processes, of which the issues on the intraclass and interclass imbalance have been specially focused. Specifically, considering the non-independently identically distribution characteristics among different fault data and the low recognition rates of minority fault samples, a new cost sensitive convolutional neural network is constructed as a base classifier by coordinating the cross entropy loss function with a specific cost sensitive index. Subsequently, a federated learning based aggregation algorithm is designed to optimize the participation weights of local classifiers with the purpose of cooperating with multiple classifiers to improve the model generalization performance. Finally, the validity of the proposed framework is demonstrated by a typical hot rolling process with different forms of imbalance fault data. The simulation results show that superior diagnosis performance can be achieved compared with some comparative algorithms in each scenario.