Prognostic and Diagnostic Monitoring of Complex Systems for Product Lifecycle Management: Challenges and Opportunities

摘要

Modern technological advances have resulted in a myriad of complex systems, processesrnand products. These increasingly complicated processes, systems and products posernconsiderable challenges in their design, analysis, manufacturing and management forrnsuccessful operation and use over their life cycles. In the process industries, for example, thernmaintenance and management of complex process equipment and processes, and theirrnintegrated operation, play a cruicial role in ensuring the safety of plant personnel and thernenvironment as well as the timely delivery of quality products. In the discrete partsrnindustries, such as the auto industry, many product malfunctions are due to unanticipatedrndynamic interactions, due to repeated use or misuse of components. These interactionsrnthrive in complex systems when the combined effects of uncertainty and operationalrnadversity are not properly addressed either in design or in operation. Given the size, scope,rnand complexity of the systems and inretactions it is becoming increasingly difficult for plantrnpersonnel to anticipate, diagnose and control serious abnormal events in a timely manner.rnHence, it should come as no surprise that human operators tend to make erroneous decisionsrnand take actions which make matters even worse, as reported in the literature. All theserncost the companies and consumers in billions of dollars every year in product lifecyclernmanagement. Businesses and federal organizations are increasingly required to manage theirrnentire products’ life cycles to avoid costly failure or degradation in performance throughrnservice/maintenance, more robust design and control, and so on. These product life cyclernmanagement (PLM) issues present us with both major challenges and opportunities. Therernexist considerable incentives in developing appropriate prognostic and diagnosticrnmethodologies for monitoring, analyzing, interpreting, and controlling such abnormal eventsrnin complex systems and processes. People in the process and product industries view this asrnthe next major challenge in control systems research and application. In this paper, we willrnpresent an overview of these challenges and the opportunities. Recent progress hasrnpromising implications on the use of intelligent systems for product lifecycle managementrnapplications in the chemical, petrochemical, pharmaceutical and discrete parts