A Flexible Probabilistic Framework With Concurrent Analysis of Continuous and Categorical Data for Industrial Fault Detection and Diagnosis

Operational data from industrial processes typically consist of continuous and categorical variables. They reveal different aspects of the operational conditions, and both are useful for comprehensive fault detection and diagnosis (FDD). However, due to multiple production modes, the variables usually do not follow Gaussian or Bernoulli distributions. Furthermore, their correlations can be different across normal and various faulty classes. Thus, the main challenge is how to fuse the complementary information in the two types of variables and accurately characterize the complicated distribution for each class. This article proposes a flexible probabilistic framework that can concurrently analyze continuous and categorical variables for FDD. Our framework specifies a finite mixture model for each class. Thus, it can handle non-Gaussian and non-Bernoulli variables and capture their correlations under the conditional independence assumption. We then introduce the variational inference for parameter estimation, which makes our framework adaptive to the different distributions of various classes. Furthermore, a unified statistical index is designed, which gives our method extra capability to distinguish unknown faults. Finally, the effectiveness of our method is validated on the Tennessee Eastman (TE) process and a practical industrial plant process. The average
F1
score of our method is improved by 3.4/3.9 percentage points in the single-mode/multimode situation on the TE process compared with traditional mixture discriminant analysis. In the industrial plant process, when unknown faults are added, the average
F1
score of our method is 91.1% and only 1.2 percentage points lower than that on the test set without unknown faults.