Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees

Stefan Windmann

doi:10.1515/auto-2021-0101

Published by De Gruyter (O) February 5, 2022

Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees

Lernen von Petri-Netzen aus den Sensordaten industrieller Transportsysteme durch das Mergen von Prefix- und Postfix-Trees

Stefan Windmann

Stefan Windmann received the Dipl.-Ing. and Dipl.-Inf. degrees in electrical engineering and technical computer sciences from University of Paderborn, Germany, in 2004, where he received the Ph.D. degree in electrical engineering in 2008. He is currently employed as senior scientist at Fraunhofer IOSB-INA in Lemgo, Germany. His current research interests include machine learning algorithms and methods for diagnosis and optimization of automated production systems.

From the journal at - Automatisierungstechnik

https://doi.org/10.1515/auto-2021-0101

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Abstract

Petri nets are a common modeling approach for parallel processes such as transport operations in conveying systems. In industrial applications, the Petri net models are usually created manually, which involves a lot of effort, especially if the modeled systems change frequently. This paper introduces a new learning method to automatically generate Petri nets from sensor data acquired in conveying systems. The underlying approach is to create prefix and postfix trees of possible event sequences and to merge them into a compact graph, which can be transformed into a deterministic Petri net model of the conveying system. Experimental results show that the proposed method produces realistic Petri net models even for conveying systems with ambiguous events.

Zusammenfassung

Petri-Netze sind ein gängiger Modellierungs-Ansatz für parallele Prozesse wie z. B. Transportvorgänge in Fördersystemen. In industriellen Anwendungen werden die verwendeten Petri-Netz-Modelle in der Regel manuell erstellt, was mit hohem Engineering-Aufwand verbunden ist, insbesondere wenn sich die modellierten Systeme häufig ändern. In diesem Beitrag wird eine neue Lernmethode zur automatischen Erzeugung von Petri-Netzen aus Sensordaten vorgestellt. In dem vorgeschlagenen Ansatz werden Prefix- und Postfix-Trees möglicher Ereignisfolgen generiert und zu einem kompakten Graphen verschmolzen, welcher in ein deterministisches Petri-Netz-Modell des Transportsystems transformiert werden kann. Experimentelle Ergebnisse zeigen, dass die vorgeschlagene Methode auch für Fördersysteme mit mehrdeutigen Ereignissen realitätsnahe Petri-Netz-Modelle erzeugt.

Keywords: conveying systems; Petri net; model learning

Schlagwörter: industrielle Transportsysteme; Petri Netze; Modell-Lernen

About the author

Stefan Windmann

Stefan Windmann received the Dipl.-Ing. and Dipl.-Inf. degrees in electrical engineering and technical computer sciences from University of Paderborn, Germany, in 2004, where he received the Ph.D. degree in electrical engineering in 2008. He is currently employed as senior scientist at Fraunhofer IOSB-INA in Lemgo, Germany. His current research interests include machine learning algorithms and methods for diagnosis and optimization of automated production systems.

AppendixMerge operations

Fig. 9 illustrates the effects of merge operations.

Before merging two nodes v 1 and v 2 of a graph G, event sequences independently pass through these nodes, which are denoted by

(20) E = { ( e 1 − , e 1 + ) | e 1 − ∈ E 1 − , e 1 + ∈ E 1 + } ∪ { ( e 2 − , e 2 + ) | e 2 − ∈ E 2 − , e 2 + ∈ E 2 + } ,

where E i ( − ) , i ∈ { 1 , 2 } is the set of event sequences e i − that lead from the root node v 0 of G to the node v i , and E i + , i ∈ { 1 , 2 } is the set of event sequences e i + that lead from the node v i to a final node v f ∈ { V f }. After merging v 1 and v 2 , the event sequences

(21) E ′ = { ( e 1 − , e 1 + ) | e 1 − ∈ E 1 − , e 1 + ∈ E 1 + } ∪ { ( e 2 − , e 2 + ) | e 2 − ∈ E 2 − , e 2 + ∈ E 2 + } ∪ { ( e 1 − , e 2 + ) | e 1 − ∈ E 1 − , e 2 + ∈ E 2 + } ∪ { ( e 2 − , e 1 + ) | e 2 − ∈ E 2 − , e 1 + ∈ E 1 + }

are contained in the graph, which include the new combinations in line 3 and 4 of (21). Generally, the condition E ⊆ E ′ fulfilled, i. e., with each merge operation the event sequences represented by the graph either remain unchanged or new event sequences are added. For E 1 − = E 2 − or E 1 + = E 2 + , the equality E = E ′ holds, i. e., the merging does not result in new event sequences. This is exploited in the learning procedures for the prefix tree and the postfix tree, where only nodes with E 1 − = E 2 − resp. E 1 + = E 2 + are merged.

$Figure 9 Nodes v 1 {v_{1}} and v 2 {v_{2}} before the merging operation.$

Figure 9

Nodes v 1 and v 2 before the merging operation.

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Received: 2021-07-14

Accepted: 2021-11-03

Published Online: 2022-02-05

Published in Print: 2022-02-23

Learning Petri net models from sensor data of conveying systems based on the merging of prefix and postfix trees

Abstract

Zusammenfassung

About the author

AppendixMerge operations

References

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