Weakly Supervised Multilayer Perceptron for Industrial Fault Classification With Inaccurate and Incomplete Labels

Abstract

For fault classification in industrial processes, both inaccurate and incomplete supervised information commonly exist in practice, which raise a big challenge to the research field. In this article, a weakly supervised form of the multilayer perceptron (MLP) model is proposed, with considerations of both inaccurate and incomplete labels in fault classification. First, a label probability transition matrix is used to describe the relationship between the inaccurate labels and unknown true labels of process data. This transition matrix is estimated through a Gaussian mixture model, and then used to correct the loss function of the MLP model. Based on the framework of the developed weakly supervised MLP (WS-MLP) model, the information of incomplete labels in the training data set is incorporated simultaneously with the inaccurate label information. The performance of the proposed model WS-MLP is evaluated through two industrial benchmark data sets, results of which indicate its effectiveness under different application cases.