Failure diagnosis using discrete event models

Abstract

Abstruct-Detection and isolation of failures in large, complex systems is a crucial and challenging task. The increasingly stringent requirements on performance and reliability of complex technological systems have necessitated the development of sophisticated and systematic methods for the timely and accurate diagnosis of system failures. We propose a discrete-event systems (DES) approach to the failure diagnosis problem. This approach is applicable to systems that fall naturally in the class of DES; moreover, for the purpose of diagnosis, continuous-variable dynamic systems can often be viewed as DES at a higher level of abstraction. We present a methodology for modeling physical systems in a DES framework and illustrate this method with examples. We discuss the notion of diagnosability, the construction procedure of the diagnoser, and necessary and sufficient conditions for diagnosability. Finally, we illustrate our approach using realistic models of two different heating, ventilation, and air conditioning (HVAC) systems, one diagnosable and the other not diagnosable. While the modeling methodology presented here has been developed for the purpose of failure diagnosis, its scope is not restricted to this problem; it can also be used to develop DES models for other purposes such as control. A detailed treatment of the theory underlying our approach can be found in a companion paper [27].

Modeling Methods Based on Discrete Algebraic Systems

“…Each process's start and processing times are assigned to internal states and system parameters, respectively. Similar examples include diagnosis and fault detection for batch-processing lines (Sampath et al, 1996;Schullerus & Krebs, 2001). In such systems, the input times correspond to start times for injection of a substance or solvent, and the output times are equal to completion times for the outflow of the resulting substance.…”

Fault diagnosis of continuous systems using discrete-event methods

“…2. It consists of only the single event corresponding to the fault signature 1 . Also, each relative measurement ordering, m i ≺ f m j , with associated signatures σ f,mi and σ f,mj , can be represented as an LTS, shown to the right of Fig.…”

“…Although these methods have produced many practical diagnosis applications [1], [3]- [5], they are not suitable for systems with complex continuous dynamics. Quantizing the continuous behavior using a finite set of states and events results in large, nondeterministic models [6], [7] that degrade the performance and increase the computational requirements of the diagnosis algorithms.…”

“…Existing DES diagnosers [1], [2] are designed as extended observers that estimate the system state under nominal and faulty conditions. Although these methods have produced many practical diagnosis applications [1], [3]- [5], they are not suitable for systems with complex continuous dynamics.…”

Diagnosis and Threat Detection Capabilities of the SERENITY Monitoring Framework

“…The assessment of the genuineness of the explanation effects and the validity of explanations is based on the computation of beliefs using functions that we have defined for this purpose. These functions have been defined using the axiomatic framework of the Dempster Shafer theory of evidence [18]. The diagnostic mechanism of EVEREST has been tested successfully using some of the industrial scenarios of the project.…”