Determination of timed transitions in identified discrete-event models for fault detection

Schneider, Stefan; Litz, Lothar; Lesage, Jean-Jacques

doi:10.1109/cdc.2012.6426695

Cited by 5 publications

(4 citation statements)

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“…highest) timing value observed during identification for that transition. In [31], the guards are determined using a method called Skewness Adaption, which always results in single intervals. In [2], a single timing interval is assigned to each transition of a time Petri net.…”

Section: Identification Proceduresmentioning

confidence: 99%

Fault detection of Discrete-Event Systems based on an identified timed model

Souza

Moreira

Lesage

2020

Control Engineering Practice

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In this paper, a method for fault detection of Discrete-Event Systems (DES) based on a timed model called Timed Automaton with Outputs and Conditional Transitions (TAOCT), obtained by identification, is presented. The TAOCT is an extension of a recent untimed model proposed in the literature, called Deterministic Automaton with Outputs and Conditional Transitions (DAOCT). Differently from the DAOCT, where only the logical behavior of the DES is considered, the TAOCT takes into account information about the time that the events are observed, and, for this reason, it can be used for the detection of faults that cannot be detected by using untimed models, such as faults that lead the fault detector to deadlocks. The TAOCT represents the fault-free system behavior, and a fault is detected when the observed behavior is different from the one predicted by the model, considering both logical and timing information. A practical example is used to illustrate the results of the paper.

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Section: Identification Proceduresmentioning

confidence: 99%

Fault detection of Discrete-Event Systems based on an identified timed model

Souza

Moreira

Lesage

2020

Control Engineering Practice

Self Cite

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“…all states that have been observed) is called the alphabet of the system. Furthermore, adding the timing information for each event results in a timed alphabet containing pairs of symbols and corresponding timing attributes (see [39] for further details).…”

Section: Learning Knowledge Modelsmentioning

confidence: 99%

“…it contradicts the timed alphabet of the system behavior observed to generate the models), an anomaly has been detected cf. [39]. The following anomalies can be detected with MSPNs:…”

Section: Detecting Changesmentioning

confidence: 99%

Automated Determining of Manufacturing Properties and Their Evolutionary Changes from Event Traces

2016

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Production plants are usually kept in operation for several decades. During this long operational phase operation requirements and other production conditions change frequently. Accordingly, the plants have to be adjusted in behavior and/or structure by adapting software and physics of the plant to avoid degeneration. Unfortunately, in industrial practice, changes, especially smaller ones, are often performed ad-hoc without appropriate adaptation of formal models or documentation. As a consequence, knowledge about the process is only implicitly available and an evaluation of performed changes is often omitted, resulting in sub-optimal production performance. Present research approaches to overcome these deficiencies usually concentrate on (a) manual modelling with manual or automatic analysis on a high level of abstraction; or (b) on automatic model generation from observations without lifting gathered knowledge to easy interpretable indicators. The approach presented in this paper combines both methods (a) and (b) by learning models from observation of input / output signals of the production plant's control system. Semantics are added by using a priori information modelling which is less tedious compared to modelling the process itself. The learned models are used to automatically detect changes by continuously comparing their behavior with real plant behavior during operation as well as to evaluate performed changes. An analysis of the models results in high-level property values such B Jan Ladiges

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“…More recently, construction by identification of models in the form of automata or Petri nets (PN) has been addressed by several authors. Roth et al (2010) and Schneider et al (2012), for instance, propose methods to build automata that are used later on for diagnosis purposes. Meda-Campaña and López-Mellado (2005), Giua and Seatzu (2005), Dotoli et al (2011), Estrada-Vargas et al (2015), Saives et al (2015), to name a few, focus on identification based on Petri nets.…”

Section: Introductionmentioning

confidence: 99%

A relative identification method for reactive systems

2018

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This paper proposes a method to build an identified model from an observed sequence of input/output vectors and a model of knowledge on the system to identify. This method comprises two steps. The first one starts by checking whether the knowledge model is compatible with the observed sequence; when this is the case, the identified model is built automatically. From this result, the observed and unobserved behaviors of the knowledge model are then explicitly determined, in a second step.

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Determination of timed transitions in identified discrete-event models for fault detection

Cited by 5 publications

References 10 publications

Fault detection of Discrete-Event Systems based on an identified timed model

Fault detection of Discrete-Event Systems based on an identified timed model

Automated Determining of Manufacturing Properties and Their Evolutionary Changes from Event Traces

A relative identification method for reactive systems

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