Online Probabilistic Estimation of Sensor Faulty Signal in Industrial Processes and Its Applications

In this article, an online estimator for faulty sensor signal is proposed for industrial processes described by nonlinear state-space models. The potential sensor fault is modeled as an unknown additive Gaussian signal, whose distribution is estimated together with the probability density function (pdf) of the state by using the variational Bayesian inference. To solve the problem that transition dynamics of faults are unavailable, a heuristic model with probabilistic features is discussed together with other commonly-used descriptions. For the computation issue caused by nonlinearities, a set of weighted particles is employed to estimate the pdf of state empirically, while the counterpart of the faulty signal is still calculated analytically. The effectiveness of the proposed algorithm is verified by a robot localization example and an experiment conducted on a flexible rotary joint. It shows that the proposed algorithm yields improvements over the existing algorithms, including the Bayesian estimator and the modified multiple-model-based method, and can satisfactorily estimate the magnitude of fault as well as its location in an online manner.

Automated summary

This article proposes an online estimator for faulty sensor signals in industrial processes by using variational Bayesian inference to estimate the probability density function of the fault signal and state. It discusses a heuristic model with probabilistic features to address the issue of unavailable fault transition dynamics and employs a set of weighted particles for empirical estimation of the state pdf. The proposed algorithm demonstrates improvements over existing methods in accurately estimating fault magnitude and location in real-time.