Vision-based extraction of industrial information from legacy Programmable Logic Controllers

Abstract

Technological advancements in manufacturing are increasingly driven by connectivity and information that can be collected about manufacturing processes. Programmable Logic Controllers (PLCs) are a valuable source of process information which can help inform operations. However, many factories use legacy PLCs with restricted connection and data extraction capabilities. This paper presents a novel vision-based PLC monitoring method for extracting the input and output (I/O) states of a PLC in real time. Four case studies in industry and laboratory settings are presented; in each case study, vision-based PLC monitoring was used to extract I/O data successfully and provide data for applications such as operation monitoring, process monitoring, production counting and fault detection. Vision-based monitoring is evaluated and compared to other PLC monitoring methods using a set of key requirements. The vision-based monitoring method showed several improvements over existing PLC data extraction methods; these include no PLC control system interference, minimal disruption during installation, system security, and cost-effective design. This new vision-based PLC monitoring method has the potential to provide manufacturers with a method to retrofit PLCs to access new valuable sources of information that can be used to improve their operation or create a smart factory at a lower cost