Probabilistic Information-Theoretic Discriminant Analysis for Industrial Label-Noise Fault Diagnosis

Abstract

Fault diagnosis, which aims to identify the root cause of the observed abnormality, is essential for the control and optimization of industrial processes. Many existing data-driven fault diagnosis methods require all the training samples to be correctly labeled. However, label noise is ubiquitous in practical industrial data, and the performance of these methods may be severely affected. In this article, we address the fault diagnosis issue in the presence of label noise. A probabilistic information-theoretic discriminant analysis (PITDA) algorithm is proposed, which consists of two iterative steps. First, a probabilistic feature extractor based on information theory is presented to extract discriminative features from industrial data. Second, a robust mixture discriminant analysis method is applied to build label noise-tolerant classifier in the feature space and produce the probability used in the first step. Iteration of these two steps gives the PITDA algorithm, which is able to perform fault diagnosis for complex and high-dimensional industrial data in the presence of label noise. Experimental results on synthetic data, Tennessee Eastman benchmark process, and a real-world air compressor working process demonstrate the effectiveness and advantages of the proposed algorithm.