Filter Based Fault Diagnosis Research Articles

Nonlinear predictive filter based fault diagnosis of oxygen generation system by using electrolytic water in space station

The oxygen generation system using electrolytic water is one of the important systems in space station which supplies the oxygen for the astronauts from the condensate water, i.e., Sabatier reduction reaction water and urine treated water. During the operation of the space station, the normal operation of the system directly affects the safety of astronauts and the operating costs of the space station, so the fault diagnosis of the system has become a research hotspot. This paper systematically describes the fault diagnosis method of the oxygen generation system using electrolytic water, including the establishment of the fault mode and effects analysis, mathematical model of the key components and the estimation algorithm of unmeasured parameters. For the two direct discriminant indicators of the oxygen production and hydrogen production, because they are unmeasurable and their dynamic models are difficult to be established, this paper proposes a cascade algorithm of nonlinear predictive filtering and low-pass filter to solve the problem. Firstly, the oxygen and hydrogen production are estimated by the predictive filter. And then, the high frequency noise introduced by measurement error is eliminated when the oxygen and hydrogen production signals entered in a low-pass filter. Through the simulation analysis of the normal and fault mode, the correctness and effectiveness of the estimation algorithm are illustrated.

Generalized Correntropy Filter-Based Fault Diagnosis and Tolerant Control for Non-Gaussian Stochastic Systems Subject to Sensor Faults

In this paper, a generalized correntropy filter-based fault diagnosis (FD) and fault tolerant control (FTC) strategy is proposed for stochastic systems with heavy-tailed distributed noises. In order to deal with the non-Gaussian noises involved in the stochastic systems, a generalized correntropy criterion (GCC) is reviewed first. Then, based on this criterion, an output feedback controller is designed for the healthy stochastic system to make the system output track the set-point as closely as possible. Considering the sensor fault, a GCC filter-based FD method is established, where residuals are used to detect and isolate the sensor fault. Moreover, when the sensor fault is diagnosed, normal signals are used to reconstruct the system so that the system can maintain run normally. Finally, the wind energy conversion system with three types of sensor faults is used to verify the feasibility and efficiency of the proposed FD and FTC method.

Extended Kalman filter development in Lebesgue sampling framework with an application to Li-ion battery diagnosis and prognosis

Extended Kalman filter in Riemann sampling framework (RS-EKF) has been widely used in diagnosis and prognosis, navigation systems, and GPS for its advantage of simplicity and reasonable solution for nonlinear systems. New particle filter based fault diagnosis and prognosis algorithms in Lebesgue sampling framework have been developed to enable the implementation on systems with limited computational sources, such as embedded systems. In this Lebesgue sampling-based approach, Lebesgue states are defined on the fault dimension axis and algorithm is executed only when the measurement causes a transition from one Lebesgue state to another, or an event happens. This is a need-based fault diagnosis and prognosis (FDP) philosophy in which the algorithm is executed only when necessary, thus less computational resources are required. In order to make algorithms more efficient, EKF algorithm is developed in Lebesgue samplingframework (LS-EKF). With the philosophy of “execution only when necessary”, the proposed approach is able to eliminate unnecessary computations, especially in the scenario that the fault grows slowly. The prediction horizon defined by Lebesgue states on the fault dimension axis is usually small and, therefore, LS-EKF naturally benefits the uncertainty management by reducing the uncertainty accumulation. One feature of diagnosis and prognosis in Lebesgue sampling is that it requires two models, one for diagnosis and one for prognosis. The diagnostic model describes the dynamics of fault and is used to estimate the fault state. Prognostic model for LS-EKF describes the time for fault state reaching each defined Lebesgue state. The new algorithms is verified with an application to the diagnosis and prognosis of the state of health of Li-ion battery. The results show that LSEKF and RS-EKF have comparable performance in diagnosis Wuzhao Yan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. but LS-EKF has much less computation. Moreover, LS-EKF is more accurate and time-efficient on long term prognosis than RS-EKF algorithms, which makes it a promising solution for FDP in distributed applications.

Kalman filter based fault diagnosis of networked control system with white noise

The networked control system NCS is regarded as a sampled control system with output time-variant delay. White noise is considered in the model construction of NCS. By using the Kalman filter theory to compute the filter parameters, a Kaiman filter is constructed for this NCS. By comparing the output of the filter and the practical system, a residual is generated to diagnose the sensor faults and the actuator faults. Finally, an example is given to show the feasibility of the approach.