Explanation-based learning for diagnosis

Diagnostic expert systems constructed using traditional knowledge-engineering techniques identify malfunctioning components using rules that associate symptoms with diagnoses. Model-based diagnosis (MBD) systems use models of devices to find faults given observations of abnormal behavior. These approaches to diagnosis are complementary. We consider hybrid diagnosis systems that include both associational and model-based diagnostic components. We present results on explanation-based learning (EBL) methods aimed at improving the performance of hybrid diagnostic problem solvers. We describe two architectures called EBL_IA and EBL(p). EBL_IA is a form fo "learning in advance" that pre-compiles models into associations. At run-time the diagnostic system is purely associational. In EBL(p), the run-time diagnosis system contains associational, MBD, and EBL components. Learned associational rules are preferred but when they are incomplete they may produce too many incorrect diagnoses. When errors cause performance to dip below a give threshold p, EBL(p) activates MBD and explanation-based "learning while doing". We present results of empirical studies comparing MBD without learning versus EBL_IA and EBL(p). The main conclusions are as follows. EBL_IA is superior when it is feasible but it is not feasible for large devices. EBL(p) can speed-up MBD and scale-up to larger devices in situations where perfect accuracy is not required

An Efficient Model-based Diagnosis Engine for Hybrid Systems Using Structural Model Decomposition

Complex hybrid systems are present in a large range of engineering applications, like mechanical systems, electrical circuits, or embedded computation systems. The behavior of these systems is made up of continuous and discrete event dynamics that increase the difficulties for accurate and timely online fault diagnosis. The Hybrid Diagnosis Engine (HyDE) offers flexibility to the diagnosis application designer to choose the modeling paradigm and the reasoning algorithms. The HyDE architecture supports the use of multiple modeling paradigms at the component and system level. However, HyDE faces some problems regarding performance in terms of complexity and time. Our focus in this paper is on developing efficient model-based methodologies for online fault diagnosis in complex hybrid systems. To do this, we propose a diagnosis framework where structural model decomposition is integrated within the HyDE diagnosis framework to reduce the computational complexity associated with the fault diagnosis of hybrid systems. As a case study, we apply our approach to a diagnostic testbed, the Advanced Diagnostics and Prognostics Testbed (ADAPT), using real data

Model based fault diagnosis for hybrid systems : application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

Fault Diagnosis for Polynomial Hybrid Systems

Safety requirements of technological processes trigger an increased demand for elaborate fault diagnosis tools. However, abrupt changes in system behavior are hard to formulate with continuous models but easier to represent in terms of hybrid systems. Therefore, we propose a set-based approach for complete fault diagnosis of hybrid polynomial systems formulated as a feasibility problem. We employ mixed-integer linear program relaxation of this formulation to exploit the presence of discrete variables. We improve the relaxation with additional constraints for the discrete variables. The efficiency of the method is illustrated with a simple two-tank example subject to multiple faults

Using Hybrid Automata for Diagnosis of Hybrid Dynamical Systems

Physical systems can fail. For this reason the problem of identifying and reacting to faults has received a large attention in the control and computer science communities. In this paper we study the fault diagnosis problem and modeling of Hybrid Dynamical Systems (HDS). Generally speaking, HDS is a system mixing continuous and discrete behaviors that cannot be faithfully modeled neither by using formalism with continuous dynamics only nor by a formalism including only discrete dynamics. We use the well known framework of hybrid automata for modeling hybrid systems, because they combine the continous and discretes parts on the same structure. Hybrid automaton is a states-transitions graph, whose dynamic evolution is represented by discretes and continous steps alternations, also, continous evolution happens in the automaton apexes, while discrete evolution is realized by transitions crossing (arcs) of the graph. Their simulation presents many problems mainly the synchronisation between the two models. Stateflow, used to describe the discrete model, is co-ordinated with Matlab, used to describe the continuous model. This article is a description of a case study, which is a two tanks system

A Structural Model Decomposition Framework for Hybrid Systems Diagnosis

Nowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study

Fault Diagnosis of Hybrid Systems with Dynamic Bayesian Networks and Hybrid Possible Conficts

Hybrid systems are very important in our society, we can find them in many engineering fields. They can develop a task by themselves or they can interact with people so they have to work in a nominal and safe state. Model-based Diagnosis (MBD) is a diagnosis branch that bases its decisions in models. This dissertation is placed in the MBD framework with Artificial Intelligence techniques, which is known as DX community. The kind of hybrid systems we focus on have a continuous behaviour commanded by discrete events. There are several works already done in the diagnosis of hybrid systems field. Most of them need to pre-enumerate all the possible modes in the system even if they are never visited during the process. To solve that problem, some authors have presented the Hybrid Bond Graph (HBG) modeling technique, that is an extension of Bond Graphs. HBGs do not need to enumerate all the system modes, they are built as the system visits them at run time. Regarding the faults that can appear in a hybrid system, they can be divided in two main groups: (1) Discrete faults, and (2) parametric or continuous faults. The discrete faults are related to the hybrid nature of the systems while the parametric or continuous faults appear as faults in the system parameters or in the sensors. Both types af faults have not been considered in a unified diagnosis architecture for hybrid systems. The diagnosis process can be divided in three main stages: Fault Detection, Fault Isolation and Fault Identification. Computing the set of Possible Conflicts (PCs) is a compilation technique used in MBD of continuous systems. They provide a decomposition of a system in subsystems with minimal analytical redundancy that makes the isolation process more efficient. They can be used for fault detection and isolation tasks by means of the Fault Signature Matrix (FSM). The FSM is a matrix that relates the different parameters (fault candidates) in a system and the PCs where they are used

Set-membership parity space hybrid system diagnosis

In this paper, diagnosis for hybrid systems using a parity space approach that considers model uncertainty is proposed. The hybrid diagnoser is composed of modules which carry out the mode recognition and diagnosis tasks interacting each other, since the diagnosis module adapts accordingly to the current hybrid system mode. Moreover, the methodology takes into account the unknown but bounded uncertainty in parameters and additive errors (including noise and discretisation errors) using a passive robust strategy based on the set-membership approach. An adaptive threshold that bounds the effect of model uncertainty in residuals is generated for residual evaluation using zonotopes, and the parity space approach is used to design a set of residuals for each mode. The proposed fault diagnosis approach for hybrid systems is illustrated on a piece of the Barcelona sewer network.This work has been funded by the Spanish Ministry of Science and Technology through the CICYT project WATMAN [grant number DPI2009-13744]; the Spanish Ministry of Economy and Competitiveness through the CICYT project SHERECS [grant number DPI2011-26243]; EFFINET [grant number FP7-ICT-2012-318556] of the European Commission.Peer Reviewe