Petri Nets Research Articles

Software patterns and data structures for the runtime coordination of robots, with a focus on real-time execution performance.

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

A model of knowledge chain integration for complex product R&D projects based on DSM and TCPN

AbstractComplex product research and development projects usually are large scale with high cost and complex technologies and with large number of group members involved. From the perspective of complex system management, this paper adopts design structure matrix and timing constraint Petri nets (TCPN) to analyse knowledge behaviour and time constraints of team members in complex product R&D projects. In specific, this paper first develops a knowledge‐activity design structure matrix and a time‐resource design structure matrix model. Then a TCPN model that integrates resource constraints in knowledge chains is proposed based on the dependence relationship of team members' knowledge activities. Schedulabilities of the model are calculated, analysed and corrected to integrate team members' knowledge. Finally, a case study is conducted to test the proposed model.

Modeling of Planning Processes Based on Temporal Neuro-Fuzzy Petri Nets

Modeling of Planning Processes Based on Temporal Neuro-Fuzzy Petri Nets

Automated generation of digital models for manufacturing systems: The event-centric process mining approach

Digital and simulation models support the design and management of complex systems. However, system modelling is a time-demanding and knowledge-intensive activity. Moreover, modern manufacturing systems are subjected to frequent changes in production plans and subsequent reconfigurations. Therefore, the quick regeneration of the digital models is necessary to align digital twins and cyber-physical systems. This paper proposes a novel event-centric process mining paradigm, a process discovery algorithm, and a set of Key Performance Indicators for the fast and automated generation of digital models and their benchmarking. The discovery algorithm is based on the Event Relationship Graph of the conceptual model of the physical line. The algorithm is tested in four realistic systems of increasing complexity to verify the accuracy in modelling multi-product systems with re-entrant flows and random reworks in the presence of the assembly, disassembly, and split processes beyond the processing operations, and multi-operation workstations. The Event Relationship Graphs of the four systems are presented through the equivalent Petri nets models. The proposed approach is suitable for systems where the sensor positions are known and meaningful, like manufacturing systems, and it is effective for the quick automated generation of digital models for the activities of production planning and control as it requires a few seconds of computation time and a few hours of system observation.

An asset management modelling framework for wind turbine blades considering monitoring system reliability

By incorporating information about asset condition from a monitoring system, engineers can utilize asset management models to manage maintenance activities on wind turbine blades throughout their lifespan. This can lower operating and maintenance costs and increase the life of the blades. The asset management model relies on the monitoring system as a source of information, however, commonly the reliability of the monitoring system is not considered. This paper presents a wind turbine blade asset management Petri net (PN) model that covers the blade asset management process, including degradation, inspection, condition monitoring (CM), and maintenance processes. The paper proposes two contributions. Firstly, while taking into account detailed industry guidelines, the developed model can forecast the future blade condition for a given asset management strategy. Secondly, it investigates the impact of the reliability of the monitoring system on the asset management modelling results. With the aid of the developed model, the number of repair actions and probability distributions of blade condition discovery time are obtained. In addition, the PN gives an indication of how misreporting (underestimation and overestimation) occurs and the extent of the misreporting. The simulation results illustrate the degree of uncertainty introduced into the monitoring results by the reliability of the monitoring system and, consequently, the extent to which this factor influences the maintenance strategies. The proposed model can be used to support asset management decisions when monitoring system performance degrades.

Path detectability verification for time-dependent systems with application to flexible manufacturing systems

This paper addresses the path detectability verification problem for time-dependent systems modeled by time labeled Petri nets (TLPNs). To capture the information precisely, it may not be sufficient to estimate the current state by resorting to the partial system observation, and it is usually crucial to decide the path of a system to reach the current state. Path detectability characterizes a time-dependent system whose current state and the corresponding path can be uniquely determined after a real-time observation (RTO). Revised state class graphs (RSCGs) are proposed to capture the time information for the evolution of the RTO in a TLPN system. We demonstrate the time information overlap problem in the RSCG, i.e., several paths are associated with the same observable events and the same time instants, which leads to such paths that cannot be distinguished. The nodes required to be computed in the proposed RSCGs are always less or equal to those of the modified state class graphs reported in the literature, since the enumeration of all the states is avoided. Based on the RSCG, an RSCG observer is formulated to address the time information overlap problem and capture the number of such paths in the TLPN system. The efficiency analysis of this verification method is provided. In this paper, the results are applied to a real production system, exposing the practical value of the reported method.

Process-commutative distributed objects: From cryptocurrencies to Byzantine-Fault-Tolerant CRDTs

This paper explores the territory that lies between best-effort Byzantine-Fault-Tolerant Conflict-free Replicated Data Types (BFT CRDTs) and totally ordered distributed ledgers, such as those implemented by Blockchains. It formally characterizes a novel class of distributed objects that only requires a First In First Out (FIFO) order on the object operations from each process (taken individually). The formalization leverages Mazurkiewicz traces to define legal sequences of operations and ensure both Strong Eventual Consistency (SEC) and Pipleline Consistency (PC). The paper presents a generic algorithm that implements this novel class of distributed objects both in a crash and Byzantine setting. It also illustrates the practical interest of the proposed approach using four instances of this class of objects, namely money transfer, Petri nets, multi-sets, and concurrent work stealing dequeues.

Mobility‐aware modeling and evaluation of IoT systems using stochastic reward nets

SummaryThe frequent geographical changes of mobile nodes in Internet of Things (IoT) systems affect communication, activities, and behaviors. In such scenarios, it is crucial to establish a system model capable of evaluating quality of service (QoS) measures. However, the existing formal modeling techniques pose complexities in modeling mobility. To deal with these challenges, this study aims to propose a model that simplifies the process of modeling mobility within IoT systems. This paper presents a method for modeling mobility within IoT systems by leveraging a widely recognized extension of stochastic Petri nets known as stochastic reward nets (SRNs). The proposed method enhances the SRN model by incorporating the location concept, resulting in a novel extension called mobile SRN (MSRN). In this work, a case study utilizes the MSRN to evaluate the suggested features, examining various scenarios and investigating the impact of factors such as environmental conditions, sensor sampling rate, and the permissible distance of the node from the sink.

Performance evaluation of a video surveillance system using stochastic petri nets for license plate detection on highways

Performance evaluation of a video surveillance system using stochastic petri nets for license plate detection on highways

Process mining embeddings: Learning vector representations for Petri nets

Process Mining offers a powerful framework for uncovering, analyzing, and optimizing real-world business processes. Petri nets provide a versatile means of modeling process behavior. However, traditional methods often struggle to effectively compare complex Petri nets, hindering their potential for process enhancement. To address this challenge, we introduce PetriNet2Vec, an unsupervised methodology inspired by Doc2Vec. This approach converts Petri nets into embedding vectors, facilitating the comparison, clustering, and classification of process models. We validated our approach using the PDC Dataset, comprising 96 diverse Petri net models. The results demonstrate that PetriNet2Vec effectively captures the structural properties of process models, enabling accurate process classification and efficient process retrieval. Specifically, our findings highlight the utility of the learned embeddings in two key downstream tasks: process classification and process retrieval. In process classification, the embeddings allowed for accurate categorization of process models based on their structural properties. In process retrieval, the embeddings enabled efficient retrieval of similar process models using cosine distance. These results demonstrate the potential of PetriNet2Vec to significantly enhance process mining capabilities.

Использование алгоритмов Process Mining для выявления паттернов поведения студентов

This work is devoted to studying the possibilities of using process analytics methods (Process Mining) to analyze student activity based on digital traces that students leave in learning management systems (LMS). This work examines the specifics of process mining algorithms that can be used to analyze educational processes, namely, heuristic and inductive algorithms are considered as the most effective for building models and suitable for use for the purpose of analyzing educational data. The work involved creating a way to use process analytics algorithms to identify clusters of students with similar behavior patterns. The development of a process analysis algorithm was carried out on the basis of the event log of the distance learning system of Kostroma State University. As a result of the work, models of student behavior were built and visualized, including the identification and clustering of students with similar behavior, the construction of heuristic networks, Petri nets, a direct sequence graph, a BPMN model and a decision tree. An analysis of the resulting models was carried out, which showed that the developed method makes it possible to study the behavioral patterns of students. The proposed method of using intellectual analysis of educational processes can be used to solve issues of increasing the productivity of the educational process, early detection of problems, especially in the context of changing student behavior in the system, as well as the development and optimization of educational programs. In addition, the limitations of this system have been identified, which may hinder its implementation and application in the educational environment of universities.

Analysis and optimization of a Stochastic Petri Net for air-rail intermodal transportation.

Air-rail intermodal transportation (ARIT) plays a crucial role in China's intermodal transportation system. This study aims to model and optimize issues such as inefficiency and complexity in China's ARIT freight transportation using Business Process Reengineering and Stochastic Petri Nets theories. The Petri Net (PN) model for incoming freight transport in ARIT is based on actual operations, employing a new method involving Stochastic Petri Nets and isomorphic Markov Chains theory for performance analysis. Performance analysis results help intuitively identify areas needing optimization. Based on optimization principles, elements such as railway container packaging are improved, resulting in an optimized PN model for ARIT. Finally, data analysis shows that the optimized ARIT model reduces total delay by 7.7% compared to the original. This demonstrates that the new method, combining Markov Chain performance analysis and optimization principles, is feasible and effective for ARIT optimization.

MSCFS-RP: A Colored-Petri-Net-Based Analysis Model for Master–Slave Cloud File Systems with Replication Pipelining

As a typical information system, a cloud file system enables the storage, retrieval, and management of data on remote servers or server clusters. The reliable design of such systems is critical to ensure the security of data and availability of services. However, designing correct-by-construction systems is challenging due to the complexity of and concurrency inherent in cloud file systems. Further, existing works on cloud file system analysis often focus on specific systems or lack formal modeling and verification, leading to potential design flaws and security vulnerabilities. To address these issues, we propose MSCFS-RP, which is a formal analysis model based on colored Petri nets. Leveraging the strengths of colored Petri nets in representing diverse information types with colored tokens and defining explicit rules for concurrent interactions, our model captures the writing and reading processes of clients, meta servers, and clusters. With strong formalism and support for verification using CPN Tools, we rigorously evaluate key properties such as replication consistency under various scenarios. The results demonstrate that MSCFS-RP satisfies these properties, validating its effectiveness and trustworthiness in managing information within cloud storage systems.

Mapping Petri Nets onto a Calculus of Context-Aware Ambients

Petri nets are a graphical notation for describing a class of discrete event dynamic systems whose behaviours are characterised by concurrency, synchronisation, mutual exclusion and conflict. They have been used over the years for the modelling of various distributed systems applications. With the advent of pervasive systems and the Internet of Things, the Calculus of Context-aware Ambients (CCA) has emerged as a suitable formal notation for analysing the behaviours of these systems. In this paper, we are interested in comparing the expressive power of Petri nets to that of CCA. That is, can the class of systems represented by Petri nets be modelled in CCA? To answer this question, an algorithm is proposed that maps any Petri net onto a CCA process. We prove that a Petri net and its corresponding CCA process are behavioural equivalent. It follows that CCA is at least as expressive as Petri nets, i.e., any system that can be specified in Petri nets can also be specified in CCA. Moreover, tools developed for CCA can also be used to analyse the behaviours of Petri nets.

Study and Analysis of Methods and Techniques for Control-command Applications

After an introduction to systemic analysis and the OOPP (oriented objectives project planning) method, we present a systems analysis methodology based on the OOPP method applied to various modeling methods and techniques: we particularly cite Petri Nets (RdP), Logic Fuzzy and the UML object-oriented modeling language This methodology made it possible to describe the exchanges of information between the different components of a system and to define the different parameters involved in the constitution of the models.

Assuring Autonomy of UAVs in Mission-critical Scenarios by Performability Modeling and Analysis

Uncrewed Aerial Vehicles (UAVs) have been used in mission-critical scenarios such as Search and Rescue (SAR) missions. In such a mission-critical scenario, flight autonomy is a key performance metric that quantifies how long the UAV can continue the flight with a given battery charge. In a UAV running multiple software applications, flight autonomy can also be impacted by faulty application processes that excessively consume energy. In this article, we propose Flight Autonomy Assurance as a framework to assure the autonomy of a UAV considering faulty application processes through performability modeling and analysis. The framework employs hierarchically configured stochastic Petri nets, evaluates the performability-related metrics, and guides the design of mitigation strategies to improve autonomy. We consider a SAR mission as a case study and evaluate the feasibility of the framework through extensive numerical experiments. The numerical results quantitatively show how autonomy is enhanced by offloading and restarting faulty application processes.

Simulation of the Effect of N2O on DNA Damage by Ionizing Radiation

Damage to the DNA molecule by ionizing radiation can be influenced by the presence of certain chemicals in the cell during irradiation. These substances can be both radioprotective and radiosensitive. In this paper, we will discuss the effect of N2O, widely used in medicine, on the chemical stage of the radiobiological mechanism. N2O in the cell during irradiation with ionizing radiation results in more significant damage to the DNA molecule because N2O reacts with hydrated electrons 𝑒𝑎𝑞 − to form aggressive OH radicals. A mathematical simulation model developed using hybrid Petri nets is used to analyze this dynamic process. Hybrid Petri nets allow us to quickly create a mathematical simulation model and explore the system under study to obtain detailed information for practical applications.

Aspects of Symmetry in Petri Nets

Symmetry is a fundamental mathematical property applicable to the description of various shapes both geometrical and representational. Symmetry is central to understanding the nature of various objects. It can be used as a simplifying principle when structures are created. Petri nets are widely covered formalisms, useful for modeling different types of computer systems or computer configurations. Different forms of Petri nets exist along with several forms of representation. Petri nets are useful for i) deterministic and ii) non-deterministic modeling. The aspect of symmetry in Petri nets requires in-depth treatment that is often overlooked. Symmetry is a fundamental property found in Petri nets. This work tries to briefly touch on these properties and explain them with simple examples. Hopefully, readers will be inspired to carry out more work in this direction.

Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper.

Distributed Petri nets ROS

AbstractVerifying industrial robotic systems is a complex task because those systems are distributed and solely defined by their implementation instead of models of the system to be verified. Some technologies mitigate parts of this problem, e.g., robotic middleware such as the Robotic Operating System (ROS) or concrete solutions such as automata-based specification of robot behavior. However, they all lack the required modeling depth to describe the structure, behavior, and communication of the system. We introduce an improved version of our previous model-driven approach based on Petri nets, integrating these three aspects of ROS-based systems. Using a formal modeling language enables verification of the described system and the generation of complete system parts in the form of ROS nodes. This reduces testing effort because the specification of component workflows and interfaces remains formally proven, while only changed implementations have to be revalidated. We extended our previous approach with novel model transformations, which considerably improved our approach’s performance and memory requirements. We evaluate our approach in a case study involving multiple industrial robotic arms and show that the structure of and communication between ROS nodes can be described and verified.