timebased statistical chain wear detection

grabcad

The solution employs a procedural approach without requiring expensive equipment. A crucial component is a cleverly designed hardware piece added to the drivetrain. The chain monitoring process involves sequential numbering of each link, regular cleaning, and daily test procedures before and after workdays. A unique detection method utilizes statistical analysis of data acquired by human employees. The procedure includes cleaning the chain and sensors on a special cog (pentacog), applying a known test load, cycling the chain through the pentacog, turning on recording devices, and running until the end of the chain. Data is then uploaded to an ongoing database for automated analysis. The heuristic analysis focuses on time intervals between sensor blips to identify probable wear areas, with recommendations for further human inspection. Long-term data collection improves prediction accuracy. The pentacog, a gear with special geometry that aligns and detects chain movement, is engaged only during testing. A remote timer circuit records events against a timeline. The system compares links' measurements over time, neutralizing irregularities through analysis software. Regression curves help plot trends and make quantitative predictions about wear based on available variables. This cost-effective method proves reliable in the long run due to probability's 100% reliability.