An intelligent diagnostic model for industrial equipment with privacy protection

Abstract

Intelligent diagnostic modeling of industrial equipment (IDMIE) addresses various industrial challenges, yet concerns about data privacy security have been raised by many organizations. However, the reliance on third-party trust and the stringent privacy requirements pose obstacles to ensuring privacy. To tackle these issues, this study proposes a generative model based on the framework of differential privacy and one-dimensional operational generative adversarial networks (DP1D-OpGAN), in which, in order to reduce the privacy budget and ensure the privacy of the generative model, a method involving training the learning parameters with perturbed gradient vectors is proposed. Additionally, the classification model of discrete multi-wavelet transforms convolutional neural network (DMWA-CNN) is integrated to enhance the diagnostic performance of the model. The model's safety, high performance, and generalizability are validated through multiple comprehensive experiments.