Probabilistic monitoring of sensors in state-space with variational Bayesian inference
Measurements quality is important for process systems engineering. In this paper, an
estimation scheme is proposed in the state-space form to monitor the degree of accuracy of …
estimation scheme is proposed in the state-space form to monitor the degree of accuracy of …
Probabilistic monitoring of correlated sensors for nonlinear processes in state space
To optimize control and/or state estimation of industrial processes, information about
measurement quality provided by sensors is required. In this paper, a probabilistic scheme …
measurement quality provided by sensors is required. In this paper, a probabilistic scheme …
Tuning-free Bayesian estimation algorithms for faulty sensor signals in state-space
Sensors provide insights into the industrial processes, while misleading sensor outputs may
result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian …
result in inappropriate decisions or even catastrophic accidents. In this article, the Bayesian …
Variational Bayesian State Space Model for dynamic process fault detection
Industrial processes are subject to various noise disturbances that lead to the stochastic
nature of the modeled system and the uncertainty of the model parameters. In this paper, a …
nature of the modeled system and the uncertainty of the model parameters. In this paper, a …
Variational Bayesian probabilistic modeling framework for data-driven distributed process monitoring
J Jiang, Q Jiang - Control Engineering Practice, 2021 - Elsevier
Data-driven process monitoring has gained increasing attention because of the increasing
demand in process safety and the rapid advancement of data gathering techniques. When …
demand in process safety and the rapid advancement of data gathering techniques. When …
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 …
described by linear discrete-time systems, where the fault dynamics are modeled as a …
Prediction of Condition Monitoring Signals Using Scalable Pairwise Gaussian Processes and Bayesian Model Averaging
Predicting condition monitoring signals has become a critical task for health status
assessment and monitoring of industrial systems. It is crucial to incorporate correlated …
assessment and monitoring of industrial systems. It is crucial to incorporate correlated …
Robust filter design for asymmetric measurement noise using variational Bayesian inference
To obtain an effective state estimator for industrial processes, estimator needs to be
designed to match the characteristics of noise. In this study, a new filter is proposed focusing …
designed to match the characteristics of noise. In this study, a new filter is proposed focusing …
Centralized and distributed robust state estimation over sensor networks using elliptical distribution
We consider the robust state estimation over sensor networks with non-Gaussian noise,
which is often encountered in many applications. Motivated by the fact that the elliptical …
which is often encountered in many applications. Motivated by the fact that the elliptical …
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 …
described by nonlinear state-space models. The potential sensor fault is modeled as an …