A new approach for diagnosability analysis of Petri nets using Verifier Nets

In this paper, we analyze the diagnosability properties of labeled Petri nets. We consider the standard notion of diagnosability of languages, requiring that every occurrence of an unobservable fault event be eventually detected, as well as the stronger notion of diagnosability in K steps, where the detection must occur within a fixed bound of K event occurrences after the fault. We give necessary and sufficient conditions for these two notions of diagnosability for both bounded and unbounded Petri nets and then present an algorithmic technique for testing the conditions based on linear programming. Our approach is novel and based on the analysis of the reachability/coverability graph of a special Petri net, called Verifier Net, that is built from the Petri net model of the given system. In the case of systems that are diagnosable in K steps, we give a procedure to compute the bound K. To the best of our knowledge, this is the first time that necessary and sufficient conditions for diagnosability and diagnosability in K steps of labeled unbounded Petri nets are presented

Sistema en lazo cerrado para el diagnóstico de fallos de sistemas de eventos discretos utilizando redes de Petri interpretadas

Este trabajo presenta la implementación de un diagnosticador de fallos en línea, para la detección de fallos operacionales presentes en la planta piloto de procesamiento de aguas, en el marco del proyecto REAGRITECH de la cátedra Unesco de sostenibilidad, modelada como un sistema de eventos discretos (SED), haciendo uso de redes de Petri interpretadas (IPN). Se realiza un análisis de funcionamiento del sistema real, del cual se identifican algunas escenas de funcionamiento, que permiten la construcción de un controlador encargado de representar, de forma simulada, el comportamiento real de la planta. A partir de este, se obtiene una matriz de información de entradas-salidas (E/S), donde las entradas corresponden a las señales de mando de control, y las salidas son las señales de sensores. Estos datos se ingresan como parámetros a un algoritmo de identificación, que entrega como resultado el modelo de la IPN identificado a partir de los datos, es decir, información de una IPN: matriz de incidencia A, función de entrada (Fe) con las etiquetas asociadas a las transiciones, la función de salida (Fs) y una matriz de salida φ que relaciona los sensores con cada plaza del sistema. Con base en el modelo identificado se realiza un análisis de detectabilidad para saber si el sistema es detectable o no por eventos, y se construye un diagnosticador a partir de fallos predefinidos. Se concluye que el diagnosticador implementado es capaz de detectar fallos presentes en la planta piloto de procesamiento de agua, en el marco del proyecto REAGRITECH, haciendo un análisis del marcado en los lugares de diagnóstico y en los lugares Post de riesgos del sistema

Petri Nets at Modelling and Control of Discrete-Event Systems with Nondeterminism - Part 2

Discrete-Event Systems (DES) are discrete in nature. Petri Nets (PN) are one of the most widespread tools for DES modelling, analyzing and control. Different kinds of PN can be used for such purposes. Some of them were described in [3], being the first part of this paper. Here, the applicability of Labelled PN (LbPN) and Interpreted PN (IPN) for modelling and control of nondeterministic DES, especially with uncontrollable and/or unobservable transitions in the models, will be pointed out. Moreover, another kinds of nondeterminism in DES (errors, failures) will be modelled, and the possibilities of the error recovery of failed system will be presented

On the Equivalence of Observation Structures for Petri Net Generators

Observation structures considered for Petri net generators usually assume that the firing of transitions may be observed through a static mask and that the marking of some places may be measurable. These observation structures, however, are rather limited, namely they do not cover all cases of practical interest where complex observations are possible. We consider in this paper more general ones, by correspondingly defining two new classes of Petri net generators: labeled Petri nets with outputs (LPNOs) and adaptive labeled Petri nets (ALPNs). To compare the modeling power of different Petri net generators, the notion of observation equivalence is proposed. ALPNs are shown to be the class of bounded generators possessing the highest modeling power. Looking for bridges between the different formalisms, we first present a general procedure to convert a bounded LPNO into an equivalent ALPN or even into an equivalent labeled Petri net (if any exists). Finally, we discuss the possibility of converting an unbounded LPNO into an equivalent ALPN

A Hierarchical Architecture for Cooperative Actuator Fault Estimation and Accommodation of Formation Flying Satellites in Deep Space

A new cooperative fault accommodation algorithm based on a multi-level hierarchical architecture is proposed for satellite formation flying missions. This framework introduces a high-level (HL) supervisor and two recovery modules, namely a low-level fault recovery (LLFR) module and a formation-level fault recovery (FLFR) module. At the LLFR module, a new hybrid and switching framework is proposed for cooperative actuator fault estimation of formation flying satellites in deep space. The formation states are distributed among local detection and estimation filters. Each system mode represents a certain cooperative estimation scheme and communication topology among local estimation filters. The mode transitions represent the reconfiguration of the estimation schemes, where the transitions are governed by information that is provided by the detection filters. It is shown that our proposed hybrid and switching framework confines the effects of unmodeled dynamics, disturbances, and uncertainties to local parameter estimators, thereby preventing the propagation of inaccurate information to other estimation filters. Moreover, at the LLFR module a conventional recovery controller is implemented by using estimates of the fault severities. Due to an imprecise fault estimate and an ineffective recovery controller, the HL supervisor detects violation of the mission error specifications. The FLFR module is then activated to compensate for the performance degradations of the faulty satellite by requiring that the healthy satellites allocate additional resources to remedy the problem. Consequently, fault is cooperatively recovered by our proposed architecture, and the formation flying mission specifications are satisfied. Simulation results confirm the validity and effectiveness of our developed and proposed analytical work

Efficient Detection on Stochastic Faults in PLC Based Automated Assembly Systems With Novel Sensor Deployment and Diagnoser Design

In this dissertation, we proposed solutions on novel sensor deployment and diagnoser design to efficiently detect stochastic faults in PLC based automated systems First, a fuzzy quantitative graph based sensor deployment was called upon to model cause-effect relationship between faults and sensors. Analytic hierarchy process (AHP) was used to aggregate the heterogeneous properties between sensors and faults into single edge values in fuzzy graph, thus quantitatively determining the fault detectability. An appropriate multiple objective model was set up to minimize fault unobservability and cost while achieving required detectability performance. Lexicographical mixed integer linear programming and greedy search were respectively used to optimize the model, thus assigning the sensors to faults. Second, a diagnoser based on real time fuzzy Petri net (RTFPN) was proposed to detect faults in discrete manufacturing systems. It used the real time PN to model the manufacturing plant while using fuzzy PN to isolate the faults. It has the capability of handling uncertainties and including industry knowledge to diagnose faults. The proposed approach was implemented using Visual Basic, and tested as well as validated on a dual robot arm. Finally, the proposed sensor deployment approach and diagnoser were comprehensively evaluated based on design of experiment techniques. Two-stage statistical analysis including analysis of variance (ANOVA) and least significance difference (LSD) were conducted to evaluate the diagnosis performance including positive detection rate, false alarm, accuracy and detect delay. It illustrated the proposed approaches have better performance on those evaluation metrics. The major contributions of this research include the following aspects: (1) a novel fuzzy quantitative graph based sensor deployment approach handling sensor heterogeneity, and optimizing multiple objectives based on lexicographical integer linear programming and greedy algorithm, respectively. A case study on a five tank system showed that system detectability was improved from the approach of signed directed graph's 0.62 to the proposed approach's 0.70. The other case study on a dual robot arm also show improvement on system's detectability improved from the approach of signed directed graph's 0.61 to the proposed approach's 0.65. (2) A novel real time fuzzy Petri net diagnoser was used to remedy nonsynchronization and integrate useful but incomplete knowledge for diagnosis purpose. The third case study on a dual robot arm shows that the diagnoser can achieve a high detection accuracy of 93% and maximum detection delay of eight steps. (3) The comprehensive evaluation approach can be referenced by other diagnosis systems' design, optimization and evaluation

Verification and Anomaly Detection for Event-Based Control of Manufacturing Systems.

Many important systems can be described as discrete event systems, including a manufacturing cell and patient flow in a clinic. Faults often occur in these systems and addressing these faults is important to ensure proper functioning. There are two main ways to address faults. Faults can be prevented from ever occurring, or they can be detected at the time at which they occur. This work develops methods to address faults in event-based systems for which there is no formal, pre-existing model. A primary application is manufacturing systems, where reducing downtime is especially important and pre-existing formal models are not commonly available. There are three main contributions. The first contribution is formalizing input order robustness - inputs occurring in different orders and yielding the same final state and set of outputs - and creating a method for its verification for logic controllers and networks of controllers. Theory is developed for a class of networks of controllers to be verified modularly, reducing the computational complexity. Input order robustness guarantees determinism of the closed-loop system. The second contribution is an anomaly detection solution for event-based systems without a pre-existing formal model. This solution involves model generation, performance assessment, and anomaly detection itself. A new variation of Petri nets was created to model the systems in this solution that incorporates resources in a less restrictive way. The solution detects anomalies and provides information about when the anomaly was first observed to help with debugging. The third contribution is the identification and resolution of five inconsistencies found between typical academic assumptions and industry practice when applying the anomaly detection solution to an industrial system. Resolutions to the inconsistencies included working with industry collaborators to change logic, and developing new algorithms to incorporate into the anomaly detection solution. Through these resolutions, the anomaly detection solution was improved to make it easier to apply to industrial systems. These three contributions for handling faults will help reduce down-time in manufacturing systems, and hence increase productivity and decrease costs.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78897/1/lzallen_1.pd