# Process Mining of Programmable Logic Controllers: Input/Output Event Logs

## TL;DR

This paper introduces a novel method combining process mining, Petri nets, and neural networks to model and approximate the logic of unknown PLC programs from input/output logs, validated through simulated case studies.

## Contribution

It presents a new approach to model PLC logic by converting input/output logs into event logs and applying a hybrid Petri net and neural network method for logic approximation.

## Key findings

- Successfully modeled PLC logic from input/output logs
- Demonstrated approach on three simulated scenarios
- Validated the effectiveness of the hybrid method

## Abstract

This paper presents an approach to model an unknown Ladder Logic based Programmable Logic Controller (PLC) program consisting of Boolean logic and counters using Process Mining techniques. First, we tap the inputs and outputs of a PLC to create a data flow log. Second, we propose a method to translate the obtained data flow log to an event log suitable for Process Mining. In a third step, we propose a hybrid Petri net (PN) and neural network approach to approximate the logic of the actual underlying PLC program. We demonstrate the applicability of our proposed approach on a case study with three simulated scenarios.