Sensor Fault Estimation in a Probabilistic Framework for Industrial Processes and its Applications

In this article, a new sensor fault estimation algorithm is proposed for industrial processes described by linear discrete-time systems, where the fault dynamics are modeled as a stochastic process. By performing the variational Bayesian inference, the potential sensor fault, as well as the system states, is estimated simultaneously in a probabilistic framework. It is shown that the target fault signal can be satisfactorily estimated through the proposed method, without knowing the statistics of measurement noise and fault coefficient matrix. The efficiency and superiority of the proposed method are demonstrated through numerical simulations and experimental tests performed on a hybrid tank system.

Automated summary

A new sensor fault estimation algorithm is proposed for industrial processes described by linear discrete-time systems in this article. By performing variational Bayesian inference, the potential sensor fault and system states are estimated simultaneously in a probabilistic framework. It is demonstrated through numerical simulations and experimental tests on a hybrid tank system that the proposed method is efficient and superior.