LAUSR

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. Note to Practitioners—Due to manual labeling and other reasons, it is difficult to ensure that the labels of industrial data samples are completely accurate. At present, a useful method is to use a label probability transition matrix to correct the model loss function, so that the model can learn the inaccurate labeled samples robustly. Based on the different distribution of feature representation in the model between accurate category samples and inaccurate category samples, we introduce the Gaussian mixture model to carry out the feature representation and estimate the label probability transition matrix. In addition, a large number of unlabeled samples are available in the industry. In this article, the inaccurate label data samples and incomplete label data samples are modeled using WS-MLP model. The performances of the estimated label probability transition matrix and WS-MLP model are validated by two industrial examples.