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

Improving Distributed Diagnosis Through Structural Model Decomposition

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches

Sistemas cualitativos y diagnosis: ARCA

Este artículo se ha escrito con una doble intención: presentar el estado actual de las investigaciones de diversos grupos españoles sobre los sistemas cualitativos y también sobre la diagnosis dentro de nuestro país. Hay además un estudio sobre la aplicación de estas investigaciones al campo de los sistemas socioeconómicos. Recientemente se han celebrado en Vilanova i la Geltrú una reunión de carácter nacional de investigadores pertenecientes al colectivo ARCA (www.lsi.us.es/arca). Los objetivos de estas jornadas han sido los sistemas cualitativos y la diagnosis. El objetivo de este artículo es presentar de manera general el objetivo de esta reunión y además, de manera resumida, los trabajos que allí se presentaron

Integrating PCA and structural model decomposition to improve fault monitoring and diagnosis with varying operation points

Producción CientíficaFast and efficient fault monitoring and diagnostics methods are essential for fault diagnosis and prognosis tasks in Health Monitoring Systems. These tasks are even more complicated when facing dynamic systems with multiple operation points. This article introduces a symbiotic solution for fault detection and isolation, based on the integration of two complementary techniques: Possible Conflicts (PCs), a model-based diagnosis technique from the Artificial Intelligence (AI) community, and Principal Component Analysis (PCA), a Multivariate Statistical Process Control (MSPC) technique. Our proposal improves the PCA-based fault detection in systems with multiple operation points and transient states and provides a straightforward fault isolation stage for PCA. At the same time, the proposal increases the robustness for fault detection using PCs through the application of PCA to the residual signals. PCA has the ability to filter out residual deviations caused by model uncertainties that can lead to a high number of false positives. The proposed method has been successfully tested in a real-world plant with accurate fault detection results. The plant has noisy sensors and a system model without the same accuracy at each operation point and transient states.Ministerio de Ciencia e Innovación (PID2021-126659OB-I00

Diagnosing Business Processes

Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-

Constraint-Driven Fault Diagnosis

Constraint-Driven Fault Diagnosis (CDD) is based on the concept of constraint suspension [6], which was proposed as an approach to fault detection and diagnosis. In this chapter, its capabilities are demonstrated by describing how it might be applied to hardware systems. With this idea, a model-based fault diagnosis problem may be considered as a Constraint Satisfaction Problem (CSP) in order to detect any unexpected behavior and Constraint Satisfaction Optimization Problem (COP) constraint optimization problem in order to identify the reason for any unexpected behavior because the parsimony principle is taken into accountMinisterio de Ciencia y Tecnología TIN2015-63502-C3-2-

Model-Based Software Debugging

The complexity and size of software systems have rapidly increased in recent years, with software engineers facing ever-growing challenges in building and maintaining such systems. In particular, testing and debugging, that is, finding, isolating, and eliminating defects in software systems still constitute a major challenge in practiceMinisterio de Ciencia y Tecnología TIN2015-63502-C3-2-RFundacao para a Ciencia e a Tecnologia (FCT) UID/EEA/50014/2013European Regional Development Fund (ERDF) POCI-01-0145-FEDER-006961 (COMPETE 2020

Basic Tasks for Knowledge Based Supervision in Process Control

A new tasks taxonomy for knowledge-based global supervision (GS) of continuous industrial processes is introduced in this work. Possible required tasks are specified together with the analysis of their dimensions, which should be useful in the selection of the final capabilities of supervision. Moreover, these dimensions would help end-users and designers when comparing different systems. Several methodologies based on concepts such as generic task, generic operation or heuristic classification have been proposed to transform knowledge-based system (KBS) development in a systematic knowledge engineering activity. These approaches have been quite successful in domains such as medicine or mineral prospecting, identifying a large number of tasks that experts in the domain articulate to solve the problem. However, this was not the case in the process control area. The selection of tasks and their capabilities is the first step to be taken, even before choosing a KBS analysis and design methodology. Authors found a lack of facilities to do this selection in the aforementioned approaches when they tried to develop a global supervision tool in a beet sugar factory in Spain. Hence, this article describes an attempt to fill this gap. Moreover, it shows how this taxonomy supported the analysis and design stages of a supervision tool in the mentioned industrial application.Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil; ArgentinaFil: Alonso Gonzalez, Carlos. Universidad de Valladolid; EspañaFil: Pulido, Belarmino. Universidad de Valladolid; Españ