Asset Counter Sweep

Asset Counter Sweep is a background process that summarizes production count data and updates Asset Counter values at defined intervals.

Production counts are typically captured through OPC and updated frequently, for example, every minute. During production run, this can result in multiple production count records. For example, a run that lasts 8 hours can generate 480 count records. When counts are tracked throughout the entire lifecycle of an asset, over time, this can result in large volume of production count records, which can impact the reporting performance.

To reduce the volume of data used for asset reporting, an Asset Counter is linked to a Production Counter. At predefined intervals, by default once each day, a background process summarizes the production count data for that period and updates the asset counter. As a result, the number of asset counter records is much lower than the number of production count records. However, this approach involves a trade-off. Asset counter values can be resolved only to the time-period by which the assets are collected (swept). For example, if counter data is collected daily, asset usage can be analyzed only at the day level. Furthermore, if the background process runs overnight, counts generated during the current day are not available until the next collection cycle.

In most cases, this reduced level of detail is sufficient for processes that use asset counters, such as maintenance planning or maintenance triggers. However, processes that require more frequent updates may require a shorter sweep interval. For example, if a maintenance procedure is scheduled for every 500,000 cycles and this threshold typically reached after eight weeks, a one-day loss in resolution has minimal impact. However, if maintenance is scheduled every three days, a one-day loss in resolution can significantly impact scheduling accuracy.

The sweep frequency can be increased, for example, to every 4 hours. This provides more detailed counter data and improves reporting resolution. However, a higher collection frequency generates more records, which can increase storage and processing requirements. Therefore, an appropriate balance must be maintained between the level of detail required and the volume of data generated.