A Novel Bayesian Framework With Enhanced Principal Component Analysis for Chemical Fault Diagnosis

Fault diagnosis is of great importance to enhance the reliability and security of the complex chemical processes. However, many fault diagnosis methods cannot extract correlation information among attributes in the feature extraction procedure, resulting in the degradation of diagnosis performance. In this article, a novel framework is proposed for fault diagnosis of the chemical processes. First, in the framework, an enhanced kernel principal component analysis (eKPCA) is proposed to obtain the key features of the fault based on Hotelling's T-2 and squared prediction error (SPE) statistics. Second, to learn the correlation information of the features, an enhanced naive Bayesian model (eNBM) is constructed with a multivariate Gaussian kernel function. Third, the dragonfly algorithm (DA) is employed to find the optimal smoothing parameter to improve the performance of eNBM. The framework is applied to make a fault diagnosis of Tennessee Eastman (TE) process. The experimental results show that the proposed framework is more effective compared with the already existed traditional approaches, such as deep learning.

Automated summary

Fault diagnosis is crucial for enhancing the reliability and security of complex chemical processes. A novel framework utilizing eKPCA, eNBM, and the DA algorithm is proposed in this article, showing superior performance compared to traditional methods such as deep learning.