This paper extends the existing results on joint input and state estimation to systems with arbitrary fault and unknown inputs. The objective is to derive an optimal filter in the general case where not only fault affect both the systems state and the output, but also the direct feedthrough matrix has arbitrary rank. The paper extends both the results of Bessaoudi and Ben Hmida (2013). [State a...

In this paper, a dual estimation methodology is developed for both time-varying parameters and states of a nonlinear stochastic system based on the Particle Filtering (PF) scheme. Our developed methodology is based on a concurrent implementation of state and parameter estimation filters as opposed to using a single filter for simultaneously estimating the augmented states and parameters. The co...

This paper presents a novel approach to fault detection, faulty phase(s) identification, faulty section estimation, and fault location determination for a multi-ended transmission line in Egypt based on artificial neural networks. In order to perform this approach, the protection task is subdivided into different neural network modules for fault detection, fault classification as well as fault ...

An accurate fault detection, classification and direction estimation of double end fed transmission lines based on application of artificial neural networks is presented in this paper. The proposed method uses the phase voltage and current available at only the local end of line. This method is adaptive to the variation of fault inception angle, fault location and high fault resistance. The alg...

Autonomous fault detection is prerequisite for autonomous system repair which is of great value for spacecraft where human intervention is expensive, slow, unreliable, and occasionally impossible. We present a method which successfully achieves autonomous fault detection in simulation. The approach uses a nonparametric estimate of the system state updated based upon sensor measurements. Our sys...

This paper presents a method to increase the reliability of UAV sensor fault detection in a multiUAV context. The method uses additional position estimations that augment individual UAV fault detection system. These additional estimations are obtained using images from the same planar scene taken from two different UAVs. Since accuracy and noise level of the estimation depends on several factor...

In this paper, a class of single output nonlinear systems with an uncertain parameter is considered. A diffeomorphism is first introduced to simplify the system structure, then, by employing an adaptive approach to identify the unknown parameter, a sliding mode observer is developed to estimate the system state variables. Based on the observer, a fault estimation scheme is proposed based on the...

This paper presents a method of fault prognostics and reliability estimation for DC motor using time series modeling procedure based on DC motor performance degradation data. DC motor performance degradation data are treated as a time series data and stochastic process are utilized to describe the degradation process for predicting long-term trend. A degradation test is processed for DC motor u...

Abstract: This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean squa...