Purposeful Failure Methodology for Machinery

This failure methodology utilizes purposeful generation of labeled failure mode datasets via surrogate purposeful failure twin (SPFT) simulations. Machine learning powered performance analysis, predictive maintenance, and production management capable of reducing unexpected failure and unnecessary downtime are enabled via this innovation. This technology revolutionizes the manufacturing operations management (MOM) system market, which currently suffers from a gap in the availability of diagnostic and predictive failure data for manufacturing equipment. Current failure data generation methodologies are time consuming and costly, or do not represent all possible failure modes experienced by the equipment. Failing equipment can lead to safety hazards, reduced product quality, and avoidable reduction in output capacity. Improving failure metrics, real-time analysis, and incorporating artificial intelligence methodologies can alleviate these negative occurrences and bring manufacturers into the Industry 4.0 future with significantly improved overall equipment effectiveness (OEE). Clemson University researchers have developed a Purposeful Failure Methodology, which generates labeled relevant datasets via SPFT simulation and utilizes machine learning to not only improve prescriptive equipment maintenance, but also pinpoint equipment failure modes through real-time data collection from in-line equipment.