PROGNOSTIC ALGORITHM DEVELOPMENT FOR PLANT MONITORING AND MAINTENANCE PLANNING

The economic goals in a typical industrial plant are to improve product quality, maximize equipment up-time, reliability, and availability, and minimize spare part inventories and maintenance costs. Modern facilities are comprised of thousands of subsystems with critical unique components. Simple components and more complex engineering systems alike are typically engineered to perform satisfactorily. Their lives can be predicted under normal operation runtime. It should be the same with chronological time lapse from the moment of installation. However, their ages accelerate faster than chronological time lapse if they are operated under unfavorable working conditions, making their remaining life predictions likely not accurate, thus making failure imminent. These components most become more sophisticated and advanced to meet supercritical demands, and unplanned critical failures of any these components can result in costly operation stoppages. Speedy repair costs of failed components during operation can be extremely costly, not only due to the failed component, but also to collateral damage to other components, which can result in significant economic loss, lost production, personal injury, and even loss of life.

Today’s marketplace faces global competition, ever-changing customer perception, and evolving demand. Industrial plants are constantly retooling their operations and equipment to act in a supercritical manner, and this is happening amidst the already complex nature of mechanical structures, operational stress, and environmental influence. To address these continuous changes, early fault detection is imperative to accurately predict the Remaining Useful Life (RUL) of machinery to prevent performance degradation and malfunction, which leads to substantial damage. Predicting the RUL of degraded components and putting these components to use will reduce spare part inventories and maintenance and increase reliability, availability, and performance to minimize plant downtime and production loss while enhancing operation safety.

The primary purpose of this dissertation is to create an improved prognostic algorithm and methodology to predict the time of machinery failure. Empirical wear models built using historical operating conditions are then used to monitor the RUL of machinery and components. Machinery online monitoring data are used to determine the current health state of components along their life curves.