Showing papers by "Belarmino Pulido published in 2014"

A Common Framework for Compilation Techniques Applied to Diagnosis of Linear Dynamic Systems

Abstract: The systems dynamics and control engineering (FDI) and the artificial intelligence diagnosis (DX) communities have developed complementary approaches that exploit structural relations in the system model to find efficient solutions for the residual generation and residual evaluation steps in fault detection and isolation in dynamic systems. This paper compares three different structural fault diagnosis techniques, two from the DX community and one from the FDI community. To simplify our comparison, we start with bond graphs as the common system modeling language and develop a graph-based framework using temporal causal graphs as the basis for analyzing the three fault isolation approaches. This framework allows for systematic comparison of the diagnosability properties of the three algorithms. The three-tank system is used as a running example to illustrate our concepts and algorithms.

Integration of Simulation and State Observers for Online Fault Detection of Nonlinear Continuous Systems

Abstract: The development of efficient and reliable fault detection approaches is necessary to improve performance, safety, and reliability in engineering systems. Moreover, these approaches have to be simple enough to provide quick diagnosis results and to reduce development and maintenance costs. Consistency-based diagnosis using possible conflicts (PCs) relies upon the simulation of numerical models to provide a simple and efficient fault diagnosis approach. However, simulation approaches need to know the initial state, and this assumption is not easily fulfilled in real systems, even in the presence of measurements related to state variables due to noise and parameter uncertainties. In this paper, we develop an approach where PCs are used to automatically compute structural models which can be implemented as simulation and state observer models. Using these models, we propose a framework which integrates those state observers to estimate the initial states for simulation within the consistency-based diagnosis framework. Then, both the simulation models and the state observers are used to provide quick detection decisions without increasing the complexity of the diagnoser. Our integration proposal is open to different kinds of state observers, except for the structural model, and different fault detection configurations. The proposal has been tested on a thermohydraulic reconfigurable laboratory plant using real data with satisfactory results.