A reliability theory has been developing for almost 70 years, and all the time scientists, engineers and practitioners attempt to point out an instance of a machine’s failure to be able to prevent its breakdown. Since that time, many kinds of instruments have been developed and have been pursuing that goal. Nowadays, some AI systems utilize the statistical analysis of big data to solve that problem; however, their developers meet either poor and insufficient data or wrong data which are either almost or completely not related with the equipment’s lifespan. So, it is inevitable that AI assesses the probability of failure seldom more than 50%. To be able to provide a precise diagnosis of machinery health, the algorithms of AI should include the physics-based rules of degradation. The process of degradation could be defined as a period of machinery lifespan since the destructive forces had emerged and began to influence the weakest part of the machine until the defect has evolved and collapsed machinery operation. Within the process of degradation, machinery undergoes three stages which could be specified as non-linear wear, exponential wear, and critical wear. Protection systems work within the stage of critical wear. Condition monitoring systems and most AI systems work either within the stage of critical or exponential wear. However, if the goal is to prevent failure, the systems should identify defects within the non-linear-wear stage. The real-time diagnostic COMPACS system is the proven instrument for acquiring the paramount safety and uptime of the gas refining and storage facilities, the shortest possible turnaround, and the reasonable maintenance cost. During Gastech 2019 at the booth T186, the customers can apply for the pilot project of the COMPACS system’s free diagnostics, after which the affordable-price subscription for the diagnostics could be submitted.
1904281830 GASTECH PREVIEW AI Driven Reliability.docx