ÐThis paper addresses the problem of estimating fault tolerance coverage through statistical processing of observations collected in fault-injection experiments. In an earlier paper, various estimators based on simple sampling in the complete fault/activity input space and stratified sampling in a partitioned space were studied; frequentist confidence limits were derived based on a normal appro...

This paper presents two novel hybrid schemes for component fault diagnosis in a general nonlinear system. Unlike most fault diagnosis techniques, the proposed solution detects, isolates, and identifies the severity of faults in the system within a single integrated framework. The proposed technique is based on a bank of adaptive neural parameter estimators (NPE) and a set of parameterized fault...

Supervision of the performance of an industrial gas turbine is important since it gives valuable information of the process health and makes efficient determination of compressor wash intervals possible. Slowly varying sensor faults can easily be misinterpreted as performance degradations and result in an unnecessary compressor wash. Here, a diagnostic algorithm is carefully combined with non-l...

This paper presents a new robust fault and state estimation based on recursive least square filter for linear stochastic systems with unknown disturbances. The novel elements of the algorithm are : a simple, easily implementable, square root method which is shown to solve the numerical problems affecting the unknown input filter algorithm and related information filter and smoothing algorithms;...

The generalized Shiryayev sequential probability ratio test (SSPRT) is applied to linear dynamic systems for single fault isolation and estimation. The algorithm turns out to be the multiple model (MM) algorithm considering all the possible model trajectories. In real application, this algorithm must be approximated due to its increasing computation complexity and the unknown parameters of the ...

The AI model-based diagnosis community has developed qualitative reasoning mechanisms for fault isolation in dynamic systems. Their emphasis has been on the fault isolation algorithms, and little attention has been paid to robust online detection and symbol generation that are essential components of a complete diagnostic solution. This paper discusses a robust diagnosis methodology for hybrid ...

In this article, a novel and accurate scheme for fault detection, classification and fault distance estimation for a fixed series compensated transmission line is proposed. The proposed scheme is based on artificial neural network (ANN) and metal oxide varistor (MOV) energy, employing Levenberg-Marquardt training algorithm. The novelty of this scheme is the use of MOV energy signals of fixed se...

This paper proposes a fault detection method for hysteretic base-isolation systems. One of the key contr butions of this work is a Lyapunov-based restoring force observer that leads to the design of a robust fault detection scheme. The different fault types considered are stiffness and damping variations in the system. The proposed fault estimation method provides a direct estimate of the size ...

This paper considers a practical approach to model-based fault diagnostics. The fault estimation is performed by a delayed FIR lter that is designed as a noncausal Wiener lter. The approach is demonstrated using a case study of a process upset in a separation column in a petrochemical plant. The designed fault estimator demonstrates early and reliable detection of the fault. It presents a concl...