Petri net

About: Petri net is a research topic. Over the lifetime, 25039 publications have been published within this topic receiving 406994 citations.

Adaptive design of Petri net controllers for error recovery in automated manufacturing systems

Abstract: The concept of Petri net controllers is extended to include automatic error recovery and adaptive design. In the Petri net controller considered, a place that represents an operation or a state of a machine is attached to two functions and a constant so that it can represent a system working with both normal states and abnormal states. In addition, it is possible to detect an error with the controller using watchdog timers. Four basic Petri net augmentation methods for error recovery are investigated: input conditioning, alternate path, feedback error recovery, and forward error recovery. The authors demonstrate that when these methods are used to augment the Petri net controller, some important properties of the controller are guaranteed to be preserved. These properties include boundedness or safety, liveness, reversibility and the essentially decision-free property. An example of augmentations for error recovery for a piston insertion cell with two robots is given. >

An Efficient Approach for Online Diagnosis of Discrete Event Systems

Abstract: A novel approach to fault diagnosis of discrete event systems is presented in this paper. The standard approach is based on the offline computation of the set of fault events that may have occurred at each reachable state, providing a fast online diagnosis at a price of excessive memory requirements. A different approach is here adopted, which is based on the online computation of the set of possible fault events required to explain the last observed event. This is efficiently achieved by modelling the plant by Petri nets, since their mathematical representation permits to formulate the fault diagnosis problems in terms of mathematical programming, which is a standard tool. Moreover, the graphical representation of the net allows the diagnoser agent to compute off-line reduced portions of the net in order to improve the efficiency of the online computation, without a big increase in terms of memory requirement.

A generalized fuzzy Petri net model

Abstract: The paper proposes a new model of Petri nets based on the use of logic based neurons. In contrast to the existing generalizations, this approach is aimed at neural-type modeling of the entire concept with a full exploitation of the learning capabilities of the processing units being used there. The places and transitions of the net are represented by OR and AND-type and DOMINANCE neurons, respectively. A correspondence between this model and the previous two-valued counterpart is also revealed. The learning aspects associated with the nets are investigated. >

Comprehensible predictive models for business processes

Abstract: Predictive modeling approaches in business process management provide a way to streamline operational business processes. For instance, they can warn decision makers about undesirable events that are likely to happen in the future, giving the decision maker an opportunity to intervene. The topic is gaining momentum in process mining, a field of research that has traditionally developed tools to discover business process models from data sets of past process behavior. Predictive modeling techniques are built on top of process-discovery algorithms. As these algorithms describe business process behavior using models of formal languages (e.g., Petri nets), strong language biases are necessary in order to generate models with the limited amounts of data included in the data set. Naturally, corresponding predictive modeling techniques reflect these biases. Based on theory from grammatical inference, a field of research that is concerned with inducing language models, we design a new predictive modeling technique based on weaker biases. Fitting a probabilistic model to a data set of past behavior makes it possible to predict how currently running process instances will behave in the future. To clarify how this technique works and to facilitate its adoption, we also design a way to visualize the probabilistic models. We assess the effectiveness of the technique in an experimental evaluation with synthetic and real-world data.

Partial (set) 2-structures. Part II: state spaces of concurrent systems

Abstract: The problem of characterizing state spaces of basic classes of Petri nets, and the problem of synthesizing state spaces of basic classes of Petri nets from their «abstract» descriptions in the form of directed edge-labeled graphs are investigated