Step-time measurement: A scalable sub-cycle time defining methodology for anomaly detection and predictive maintenance in sequential production lines

Sub-cycle time periods from machines in production lines offer valuable insights into component-level health. They enable data-driven condition monitoring without the need for additional sensors. However, the lack of a standardized methodology for defining these sub-cycle time periods limits the practicality and scalability of this approach in real-world applications. We propose a scalable methodology to define sub-cycle time periods within the machine cycle time, using Programmable Logic Controllers (PLCs) programmed in compliance with the IEC 60848 standard. To achieve scalability, the proposed methodology makes sub-cycle time period definition automatic, simple and thus, fast. This is achieved by defining each sub-cycle time period as the total activation time of a Step. For this reason, the sub-cycle time periods defined with this methodology are named “Step-time”s. Because the methodology does not depend on the type of action or actuator involved, and because it can be applied to any step without requiring changes to the overall program structure, it can be easily replicated across multiple steps, modules, or even machines. This modularity enables a scalable deployment of Step-time measurements, whether for a few components or across entire production lines. Moreover, our methodology offers deeper insights into machine behavior by distinguishing between different operational contexts for the same component. To assess its feasibility in industrial production environments, we developed two implementation approaches, one based on Structured Text (ST) and another using Sequential Function Charts (SFC). The results demonstrate that machine anomalies such as air leaks, pressure drops and fluctuations in pneumatic circuits, are accurately reflected in Step-times. This confirms the high resolution of the Step-times and highlights its potential for powering data-driven condition monitoring systems in future works. Finally, the data acquisition results indicate that the proposed methodology has minimal impact on the PLC scan-cycle, making it suitable for most industrial use cases.