E. Quiles

Intermittent Failure Dynamics Characterization

Diagnosis of intermittent failures is relevant for productive processes which sustain a high level of maintenance caused by failures. Systems including electrical contacts suffer a considerable amount of intermittent failures which are usually not diagnosed. A characterization of the failure dynamics may save unnecessary repairs, and enable a better planning component substitution. This paper presents a new concept in failure diagnosis, the modeling of intermittent failure dynamics. It develops two methodologies for modeling intermittent failure dynamics, and generating information for maintenance scheduling.

Centralized modular diagnosis and the phenomenon of coupling

This paper studies modular decomposition as an approach for failure diagnosis based on discrete event systems. This paper also analyses the problem of coupling produced in the implementation of centralized modular diagnosers, as coupled diagnosers cannot carry out their own diagnosis task when there is a failure in another subsystem sharing a common energy or material flow. In addition, we propose a method to avoid diagnoser coupling, by means of decoupling functions using nonlocal information with respect to the coupled diagnoser and generated in the diagnoser where the failure has been isolated.

Intermittent failure diagnosis in industrial processes

Intermittent failure diagnosis based on discrete event system (DES) in industrial processes is studied. Intermittent failures are defined as failures that can automatically recover once they have occurred. In this paper this sort of failures are modeled and some characteristic parameters are defined. The original approach to diagnose permanent failures is revisited and extended in order to include new diagnosis skills in the diagnosers. The developed methodology is applied to a classical industrial subprocess system composed by a pump and a valve. The developed diagnoser and this example are simulated in Matlab, employing Simulink for the continuous model and stateflow for the DES diagnoser.

Modular Fault Diagnosis Based on Discrete Event Systems

An awareness of failure type and location is an indispensable requirement for the establishment of adequate recovery strategies and the maintenance of Factory Automation and Process Control systems. The failure diagnosis methodology presented in this paper is based on Discrete Event Systems models and in the diagnoser concept, which enable the off-line and on-line analysis of systems failures. We present an approach for models and associated diagnosers based on a modular decomposition of the global system, with the aim of avoiding problems of exponential explosion in the number of states and computational complexity of the diagnosis process.

Latent Nestling Method: A new fault diagnosis methodology for complex systems

This paper presents a new methodology for permanent and intermittent fault diagnosis, named faults latent nestling method (FLNM), using coloured Petri nets (CPNs). CPNs and FLNM method allow for an enhanced capability for synthesis and modelling of complex systems in contrast to the classical phenomena of combinational state explosion when using finite state machine based methods.

Application of latent nestling method using Coloured Petri Nets for the Fault Diagnosis in the wind turbine subsets

This paper presents an application example using the lating nestling method for the fault diagnosis based in the use of coloured Petri nets, to a lubrication and cooling system in the wind turbinepsilas gearbox with a critical subsystem as far as failure probability. It demonstrate the synthesis capacity of the method for any model of diagnosis and isolation, giving as opposed to know the contributed advantages other methodologies, as those based in finite state machine.

Failure diagnosis of a cement kiln using expert systems

The supervisory control and the fault diagnosis of a cement kiln is presented in this paper. The fault diagnosis system has been developed using state space and sensor space mapping, following the methodology developed by the authors. In this case, the mentioned methodology has lead to the use of an expert system implemented as a fuzzy logic fault diagnoser. Practical results of the implementation of the proposed supervisory control and fault diagnosis system in a cement kiln are included in the paper.

Fault diagnosis with Coloured Petri Nets using Latent Nestling Method

This paper presents a new methodology for permanent and intermittent fault diagnosis, named Faults Latent Nestling Method (FLNM), using Coloured Petri Nets (CPNs). CPNs and FLNM method allow for an enhanced capability for synthesis and modelling in contrast to the classical phenomena of combinational state explosion when using Finite State Machine based methods.

Diagnosis of intermittent fault dynamics

Diagnosis of intermittent faults is relevant for productive processes with a high level of maintenance caused by such failures. A characterization of diagnosed faults may save unnecessary repairs and enable better maintenance planning. This paper introduces the concept of diagnosis of intermittent fault dynamics. Moreover, a methodology for modelling intermittent faults and generating information for maintenance planning in discrete event systems is thoroughly covered.

Intermittent failure diagnosis based on discrete event models

Abstract This paper develops a methodology for diagnosing intermittent failures in industrial processes. Failure diagnosis methodologies based on discrete event systems are extended in order to diagnose this type of failures. A new structure for the supervision layer is designed, so information related to on-line diagnosis can be fused with failure historic patterns. Then, preventive maintaining can be assisted by the diagnosis system. A survey on the evolution of intermittent failure diagnosis over the past years is also included.