Luis J. de Miguel

Fuzzy logic-based decision-making for fault diagnosis in a DC motor

The uncertainty of system models, the presence of noise and the stochastic behaviour of several variables reduce the reliability and robustness of the fault diagnosis methods. To tackle these kinds of problems, this paper presents a decision-making module based on fuzzy logic for model-based fault diagnosis applications. Fuzzy rules use the concept of fault possibility and knowledge of the sensitivities of the residual equations. A fault detection and isolation system, based on the input-output linear model parity equations approach, and including this decision-making module, has been successfully applied in laboratory equipment, resulting in a reduction of the uncertainty due to disturbances and modelling errors. Furthermore, the experimental sensitivity values of the residual equations allow the fault size to be estimated with sufficient accuracy.

Fault-diagnostic system using analytical fuzzy redundancy

Abstract This paper describes a fault-diagnostic system method using analytical redundancy and fuzzy identification. The identification mechanism is a neuro-fuzzy adaptive model. Using this method, a Sugeno fuzzy model of a plant is constructed. Finally, this model is used for building a model-based fault-diagnostic system, using parity equations. This hybrid method has been applied to a laboratory installation.

Fuzzy Identification of Systems and Its Applications to Fault Diagnosis Systems

Abstract This paper describes a method to build a fuzzy model of a Fault Diagnosis System using fuzzy implications and reasoning. The identification mechanism is a neurofuzzy adaptive model. We use this method to construct a Sugeno fuzzy model of a plant Finally we use this model for building a decision system for model-based fault diagnosis, using parity equations fault diagnosis method. A simple simulated case has been used to prove its performance.

Isolation of Multiplicative Faults in the Industrial Actuator Benchmark

Abstract This paper presents an approach to the problem of fault isolation applied to the actuator benchmark test based on multiplicative fault isolation with parity equations. One of the problems of this benchmark is relative to the isolation of a multiplicative fault from additive disturbances. The fact that a fault can be modelled as multiplicative is used to isolate it from additive disturbances. An optimization procedure gets the most suitable residuals for multiplicative fault isolation.

Decision-Making Approaches for a Model-Based FDI Method

Abstract This paper describes three ways of building a decision system for quantitative model-based fault diagnosis. Some clues are given to apply the method to qualitative model-based FDI. The aim is the management of uncertain and redundant information provided by a residual generator. Although any residual generation method may be considered, the input-output parity equation approach has been used. The key point is the fault sensitivity estimations of the residuals. which lead to define the decision rules. In the case of sensor and actuator faults, sensitivity estimates are easily obtainable from the parity equations. Three ways to solve the decision problem are described: fuzzy logic-based, direct weighting of symptoms and directional properties. The proposed methods have been tested, first on simulation and then on two laboratory control equipments: 3-tanks system and d.c motor system.

Controller Reconfiguration System Using Parity Equations and Fuzzy Logic

Abstract This paper describes the reconfiguration of a fuzzy controller based on a Fault Detection and Isolation system that uses input-output parity equations as a residual generator. The FDI system uses fuzzy logic in the residual evaluation and in the diagnosis step. An estimation of the size of the fault is made using the value of the residuals and their sensitivity respect the different faults. The result is the failure isolation, with the estimation of its possibility and its size. The accommodation is made using these results and information in the fuzzy rules of the controller. First the FDI system is described and then the method of accommodation is applied to a laboratory plant DC motor that presents sensor and actuator faults.

Neuro-fuzzy identification applied to fault detection in nonlinear systems

This article describes a fault detection method, based on the parity equations approach, to be applied to nonlinear systems. The input–output nonlinear model of the plant, used in the method, has been obtained by a neural fuzzy inference architecture and its learning algorithm. The proposed method is able to detect small abrupt faults, even in systems with unknown nonlinearities. This method has been applied to a real industrial pilot plant, and good performance has been obtained for the experimental case of fault detection in the level sensor of a level control process in the said industrial pilot plant.

Additive fault detection in nonlinear dynamic systems with saturation

This paper describes the effects of input saturation on the performance of a model-based fault detection method based on the input-output parity equation approach. For this purpose, the level control of a chemical reactor has been chosen as the control process to be analyzed, where the saturation of the dynamic process is due to the inflow control valve, and only additive faults have been considered. This study has been carried out in two ways: first by simulation techniques and second on a real industrial system. In the simulated case, the decrease in the fault detectability due to the saturation effects is shown, and some ways of achieving higher fault detectability are explored. The results obtained in the industrial case complement those obtained in the simulated case, and also reveal the existence of a relation between the control strategy used in the process and additive fault detectability, in the sense that increases in fault detectability are obtained due to the use of faster control strategies.

Traffic sign recognition application based on image processing techniques

Abstract This paper describes a software application for traffic sign recognition (TSR). The application works in four stages. First, an image preprocessing step and the detection of regions of interest (ROIs), which involves a series of steps that include transforming the image to grayscale and applying edge detection by the Laplacian of Gaussian (LOG) filter. Secondly, the potential traffic signs detection, where the ROIs are compared with each shape pattern. Thirdly, a recognition stage using a cross-correlation algorithm, where each potential traffic sign, if validated, is classified according to the data-base of traffic signs. Finally, the previous stages can be managed and controlled by a graphical user interface, which has been specially designed for this purpose. The results obtained show a good performance of the developed application, taking into account acceptable conditions of size and contrast of the input image.

Fault Detection in Laser Welding

Abstract Laser welding of zinc-coated steel is an attractive process for the automotive industry. Specific faults can occur where apparently good welds have been produced. The light that is emitted from plasma and spatter in laser welding was detected by photomultiplier. It has been found that the light intensity depends on the conditions in which the weld is made. The correlation between each fault and the characteristics of the light must be analysed in order to diagnose the process. Fault detection methods have been used to fulfil this aim.