![]() 2004).įigure 1 outlines the historical maintenance paradigms along with the evolution of production paradigms summarized by Jovane et al. Yet, sometimes neither TBM or CBM is the optimal maintenance strategy allowing an element to breakdown may be the best option ( Takata et al. The cost of unplanned downtime can be up to $250K per hour in the process industry, so CBM can enhance profitability by eliminating unpredicted failures ( Koochaki et al. The only way to minimize the probability of failure, downtime, and maintenance costs is with CBM, motivating the use of prognostics ( Kothamasu et al. Then, the development of machine diagnostic techniques in the 1970s led to the concept of condition-based maintenance (CBM), in which preventive action is based upon detected symptoms of failures. The development of reliability engineering in the 1950s led to the introduction of time-based maintenance (TBM) based on the increase of failure with time ( Takata et al. The next natural step is to monitor and maintain a system in pre-established time intervals (preventative maintenance), which tends to be cost prohibitive ( Kothamasu et al. The oldest maintenance strategy is to “fix it when it breaks” (reactive maintenance), which has problems including unscheduled downtime, possible serious safety violations, and potentially significant damage to manufacturing equipment and the products being fabricated or assembled. Most product maintenance is either completely reactive or blindly preventative ( Djurdjanovic et al. The goal of maintenance is to preserve system and product functions throughout their lifecycles. Manufacturing processes are becoming more complex and dynamic, so the reliability of such systems is likewise becoming more challenging ( Lee et al. In addition to frequently collected and/or shared data, large amounts of diagnostic data, such as spindle current data collected at 1 kHz, are also sent infrequently over manufacturing networks and used for high-level control, such as tool replacement ( Moyne and Tilbury 2007). Non-physical information related to part specifications, parts ordering, and maintenance schedules for each machine also feed the information stream. A typical manufacturing system yields a vast amount of data produced by thousands of sensors that can record position, velocity, flow, temperature, and other physical quantities multiple times every minute ( Moyne and Tilbury 2007). Besides increasing costs of maintenance, manufacturing systems can also become more complicated to manage due to the increasing breadth of system information. ![]() manufacturers spend more than $7B per year recalling and renewing over 2000 defective products, and the associated costs are only increasing ( Venkatasubramanian 2005). The future of manufacturing is full of possibilities to utilize real-time and historical data to comprehensively manage maintenance, in order to decrease product lifecycle costs while increasing system availability. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. ![]() However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities.
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |