ForeSight

Date: 2022/2023
Programs: Beckhoff TwinCat3 | Siemens TIA Portal | EPLAN
Technologies: Beckhoff IPC | Siemens PLC | Soft-PLC | ProfiNET

About the Project
The „ForeSight“ project was carried out as part of the bachelor's thesis. The aim was to develop a software prototype for condition monitoring of electromechanical components in specialpurpose machines. The focus was on the efficient use of existing sensors and actuators without the need for costly hardware retrofits. The project was carried out in collaboration with INDAT GmbH, which ensured a practical approach. The integration of the prototype from the start of operation is intended to enable early fault detection in order to avoid unplanned downtime. The implementation was carried out using standardised function blocks in the Beckhoff TwinCAT 3 automation software and a soft PLC simulated on an industrial PC. This solution makes a significant contribution to increasing machine availability and safety in industrial environments. The work focuses primarily on small and medium-sized enterprises in order to offer a costefficient approach.

Technical Appropach
The technical implementation was carried out by developing modular function blocks in structured text language (ST) within the Beckhoff TwinCAT 3 environment. Two basic functions were implemented: a cycle counter for monitoring switching cycles and actuator controls, and a monitoring block for runtime analyses for more complex actuators such as motors, axis systems or actuating cylinders. The software prototype works with already installed sensors and operating data, liminating the need for additional hardware. In addition, test modules were created in the Siemens TIA Portal to simulate realistic process sequences. The development followed the evolutionary prototyping approach, whereby rough and detailed designs were iteratively refined and expanded. Early implementation allows initial states to be recorded, followed by evaluation through comparison of initialisation and monitoring values, in which treshold or maximum values, states and anomalies are recorded and analysed. The modular design allows for easy expandability for future machine concepts.

Retrospective
The project demonstrated that efficient condition monitoring can be achieved without costly monitoring systems and additional special sensors. Through the targeted use of existing signals, practical modules were created that can be applied in industrial environments. The monitoring modules developed provide important insights into switching cycles and operating times and can therefore be used for condition-based maintenance. Particularly noteworthy is the reusability of the modules in different machine configurations and the associated modularity of the solution. Challenges arose primarily in the design of universally applicable threshold values and the differentiated evaluation of complex machine states. Overall, the work lays a solid technical and theoretical foundation for further developments, such as cloud-based analyses or AI supported prediction models.