Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities

Modern technological advances have resulted in a myriad of complex systems, processes and products. These increasingly complicated processes, systems and products pose considerable challenges in their design, analysis, manufacturing and management for successful operation and use over their lifecycles. In the process industries, for example, the maintenance and management of complex process equipment and processes, and their integrated operation, play a crucial role in ensuring the safety of plant personnel and the environment as well as the timely delivery of quality products. In the discrete parts industries, such as the auto industry, many product malfunctions are due to unanticipated dynamic interactions due to repeated use or misuse of components. These interactions thrive in complex systems when the combined effects of uncertainty and operational adversity are not properly addressed either in design or in operation. Given the size, scope and complexity of the systems and interactions, it is becoming increasingly difficult for plant personnel to anticipate, diagnose and control serious abnormal events in a timely manner. Hence, it should come as no surprise that human operators tend to make erroneous decisions and take actions which make matters even worse, as reported in the literature. All these cost the companies and consumers in billions of dollars every year in product lifecycle management. Businesses and federal organizations are increasingly required to manage their entire products' lifecycles to avoid costly failure or degradation in performance through service/maintenance, more robust design and control, and so on. These product lifecycle management (PLM) issues present us with both major challenges and opportunities. There exist considerable incentives in developing appropriate prognostic and diagnostic methodologies for monitoring, analyzing, interpreting and controlling such abnormal events in complex systems and processes. People in the process and product industries view this as the next major challenge in control systems research and application. In this paper, we will present an overview of these challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for product lifecycle management applications in the chemical, petrochemical, pharmaceutical and discrete parts manufacturing industries.