| Title | Generalized Reconstruction-Based Contribution for Multiple Faults Diagnosis with Bayesian Decision |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Zhou, Wei, Yang, Weidong, Wang, Yan, Zhang, Hong |
| Conference Name | 2018 IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS) |
| Keywords | Bayes methods, bayesian decision, belief networks, Covariance matrices, cyber physical systems, data-driven fault diagnosis method, fault detection, fault diagnosis, generalized reconstruction-based contribution, human factors, Indexes, industrial process, Metrics, Monitoring, Multi-dimensional reconstruction based contribution, multiple fault diagnosis, multiple faults diagnosis, numerical analysis, numerical simulation, PCA, principal component analysis, process monitoring, pubcrawl, Resiliency, smearing effect |
| Abstract | In fault diagnosis of industrial process, there are usually more than one variable that are faulty. When multiple faults occur, the generalized reconstruction-based contribution can be helpful while traditional RBC may make mistakes. Due to the correlation between the variables, these faults usually propagate to other normal variables, which is called smearing effect. Thus, it is helpful to consider the pervious fault diagnosis results. In this paper, a data-driven fault diagnosis method which is based on generalized RBC and bayesian decision is presented. This method combines multi-dimensional RBC and bayesian decision. The proposed method improves the diagnosis capability of multiple and minor faults with greater noise. A numerical simulation example is given to show the effectiveness and superiority of the proposed method. |
| DOI | 10.1109/DDCLS.2018.8516010 |
| Citation Key | zhou_generalized_2018 |
Generalized Reconstruction-Based Contribution for Multiple Faults Diagnosis with Bayesian Decision