A robust fault diagnosis model with interval distribution analysis for industrial processes with data uncertainties

In industrial processes, sensor aging and harsh field environments often introduce uncertainties into process data. These uncertainties obscure fault symptoms and undermine the precision of fault diagnosis. To address this challenge, this paper proposes a robust fault diagnosis model with interval distribution analysis for abnormal recognition under data uncertainties in complex industrial settings. Specifically, this research first transforms uncertain data collected from complex industrial sites into interval-valued data, which can globally capture the internal structural characteristics of data objects and effectively represent the uncertainty inherent in the single-valued data. Subsequently, a complete information principal component analysis (CIPCA)-based dimensionality reduction model is constructed to exploit the distribution information within the interval and extract interval fault features. Finally, an interval radial basis function neural network (IRBFNN) is developed to handle the interval upper and lower bound matrices through subtractive clustering algorithm, facilitating fault prediction and diagnosis in industrial processes contaminated by uncertainties. The key to discriminate the proposed method from many well-established fault diagnosis methods is its ability to cluster the interval fault features from uncertain data with embedded interval distribution analysis. The superiority of the proposed fault diagnosis model is validated by the Tennessee Eastman process (TEP).