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

Prof. Venkat Venkatasubramanian, Purdue University

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 life cycles. 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. Given the size, scope, and complexity of the systems and interactions it is becoming difficult for plant personnel to anticipate, diagnose and control serious abnormal events in a timely manner. In a large process plant, there may be as many as 1500 process variables observed every few seconds leading to information overload. Furthermore, the measurements may be insufficient, incomplete and/or unreliable due to a variety of causes such as sensor biases or failures. In addition, the emphasis on quick diagnosis aggravates the situation by causing psychological strains on plant personnel. In the discrete parts industries, one has similar problems. In the auto industry, for example, 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. Complex systems are challenging because they are highly interconnected among subsystems and components. It is their interconnectedness that makes them fragile when the cumulative effects of multiple abnormalities can cause systemic failures. Given such difficult conditions, 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. Industrial statistics show that about 70% of the industrial accidents are caused by human errors. These abnormal events have significant economic, safety and environmental impact as seen in Union Carbide's Bhopal, India, accident and Occidental Petroleum's Piper Alpha accident. In the pharma industries, for example, there were more than 354 prescription drugs recalls in 2002, up from 176 in 1998. All these cost the companies and consumers in billions of dollars every year. It is estimated that the petrochemical industry in the U. S. incurs approximately $20 billion in losses due to poor management of equipment and processes which lead to such abnormal situations. The cost is much more when one includes similar situations in other industries such as pharmaceutical, specialty chemicals, power, desalination and so on. Recently, Nucor Corporation Inc. paid $100 million towards fines and remedies in a pollution control lawsuit. Similarly, accidents cost the British economy up to $27 billion every year. U.S. manufacturers spend over $7 billion annually recalling and renewing over 2,000 defective products. A far greater amount is spent on legal fees to fight lawsuits and warranty claims. All of these costs are associated with product lifecycle management and are on the rise. Businesses and federal organizations are increasingly required to manage their entire products’ life cycles to avoid costly failure or degradation in performance through service/maintenance, more robust design and control, and so on. These product life cycle 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 the process industries, there are two different, but related, components of the overall abnormal events management (AEM) problem. One is process safety during real-time operations, and the other is safety in design. These two problem areas, with the application of intelligent systems concepts and tools, are now poised to play a dominant role in defining the course of process systems research and application for the coming decade. In this talk, I will present an overview of these two problem areas, the challenges and the opportunities. Recent progress has promising implications on the use of intelligent systems for a variety of applications in the chemical, petrochemical, power and discrete parts manufacturing industries for inherently safer design, operator training, abnormal events management and optimal process operations.