Fault estimation and prediction for nonlinear stochastic system with intermittent observations

Asian Journal of Control

2022

Self Cite

This article investigates the fault estimation and fault tolerant control (FTC) problems for linear stochastic uncertain systems. By introducing the fictitious noise, the fault is augmented as part of the systems state, and then a robust estimator is proposed to simultaneously obtain the state and fault estimation. Based on the estimated information, the active FTC is presented to eliminate the impact of the fault. Finally, a simulation example is conducted to demonstrate the effectiveness of our main method.

“…Additionally, the additive fault f k is assumed to be an incipient fault [26,27] and can be modeled as follows:…”

Section: Problem Formulationmentioning

confidence: 99%

Fault estimation and fault tolerant control for linear stochastic uncertain systems

Ding

Asian Journal of Control

2022

Self Cite

“…It is also necessary to predict the trend of faults and provide more information for the management and maintenance of the system. In References 31‐33, based on the Kalman and particle filtering principles, not only the detection of incipient faults but also their subsequent trends were predicted. However, there are drawbacks such as computational complexity and difficulty in finding the optimal number of particles, which prompted us to propose more effective fault prediction algorithms.…”

Section: Introductionmentioning

confidence: 99%

Incipient fault prediction for nonlinear stochastic distribution systems

Intl J Robust & Nonlinear

Yao

2022

In order to avoid the occurrence of major accidents in the industrial production process, incipient fault prediction that can predict fault in advance has attracted more and more attentions. In this article, a generalized correntropy filtering‐based incipient fault prediction strategy is proposed, which is suitable for nonlinear stochastic distribution systems with simultaneous actuator and sensor faults and non‐Gaussian noise. Three filters are designed to predict the incipient fault of nonlinear stochastic distribution systems: the first one is used to predict the time when the incipient fault occurs, and the other two are used to estimate the evolution rate of sensor fault and actuator fault, respectively. When the residual signal exceeds the predetermined threshold, the fault is detected and the time of occurrence can be also estimated. Once the fault is detected, the designed filter can be used to estimate the evolution rate of the unknown fault. After estimating the time of fault appearance and its evolution rate, the change process of incipient fault can be predicted. Finally, an example of a chemical reaction process is given to illustrate the validity of the prediction scheme.

“…In recent years, KF has encountered renewed interest, due to an increasing range of applications. [4][5][6][7][8] Precise knowledge of system parameters and states is crucial for successful realization of many control techniques. Many modern engineering applications such as autonomous vehicles, 9 microphone sensing, 10 strain prediction for fatigue, 11 maintaining security of cyber-physical systems, 12 or robotic manipulation tasks 13 require real-time Kalman filtering framework with linear models.…”

Section: Introductionmentioning

confidence: 99%

State and parameter joint estimation of linear stochastic systems in presence of faults and non‐Gaussian noises

Stojanović

Intl J Robust & Nonlinear

2020

111

Joint estimation of states and time-varying parameters of linear stochastic systems is of practical importance for fault diagnosis and fault tolerant control. The known fact is that measurements have outliers. They can significantly degrade the properties of linearly recursive algorithms, which are designed to work in presence of Gaussian noises. This article proposes two kinds of strategies for joint parameter-state robust estimation of linear stochastic models in presence of all possible faults and non-Gaussian noises. In the form of Theorem, joint robust algorithm for systems with sensor and component faults, as well as the algorithm for systems with parameter faults are proposed. Because of their good features in robust filtering, Masreliez-Martin filter represents a cornerstone for realization of the proposed robust algorithms for joint state-parameter estimation. The good features of proposed robust estimation algorithms, in relation to algorithms based on other widely-used filters, are illustrated by simulation results. On the other side, intensive research in the field of mathematical modeling of pneumatic servo drives has shown that their mathematical models are nonlinear in which a lot of important details cannot be included in the model. Also, it has been well known that the nonlinear model can be approximated by a linear model with time-varying parameters. Due to the abovementioned reasons, it can be assumed that the pneumatic cylinder model is a linear stochastic model with variable parameters. The good practical values of the proposed robust joint algorithm to identification of the pneumatic cylinder are illustrated by experimental results.