Residual Life Prediction And Optimal Maintenance Decision For A Piece Of Equipment

This book addresses remaining life prediction and predictive maintenance of equipment. It systematically summarizes the key research findings made by the author and his team and focuses on how to create equipment performance degradation and residual life prediction models based on the performance monitoring data produced by currently used and historical equipment. Some of the theoretical results covered here have been used to make remaining life predictions and maintenance-related decisions for aerospace products such as gyros and platforms. Given its scope, the book offers a valuable reference guide for those pursuing theoretical or applied research in the areas of fault diagnosis and fault-tolerant control, remaining life prediction, and maintenance decision-making.

This book reports on the proceeding of the 5th International Conference on Intelligent, Interactive Systems and Applications (IISA 2020), held in Shanghai, China, on September 25–27, 2020. The IISA proceedings, with the latest scientific findings, and methods for solving intriguing problems, are a reference for state-of-the-art works on intelligent and interactive systems. This book covers nine interesting and current topics on different systems’ orientations, including Analytical Systems, Database Management Systems, Electronics Systems, Energy Systems, Intelligent Systems, Network Systems, Optimization Systems, and Pattern Recognition Systems and Applications. The chapters included in this book cover significant recent developments in the field, both in terms of theoretical foundations and their practical application. An important characteristic of the works included here is the novelty of the solution approaches to the most interesting applications of intelligent and interactive systems.

There is a world-wide drive to optimize maintenance decisions in an increasingly competitive manufacturing industry. Preventive maintenance if often the most organized and cost efficient strategy to follow, but a decision still has to be made on the optimal instant to perform preventive maintenance. Use based preventive maintenance decisions have been optimized through statistical analysis of failure date while predictive preventive maintenance (condition monitoring) has been optimized by utilizing more sophisticated technology. Very little work has however been done to combine the advantages of the two schools of thought. This thesis originated from a realization of the potential improvement in maintenance practice by combining use based preventive maintenance optimization techniques with high technology condition monitoring. In this thesis an approach is developed to estimate residual life of industrial equipment dynamically by combining statistical failure analysis and sophisticated condition monitoring technology. The approach is based on failure intensity proportions determined from historic survival time information and corresponding diagnostic information such as condition monitoring. Combined Proportional Intensity Models (PIMs) for non-repairable and repairable systems, containing the majority of conventional PIM enhancements as special cases, with numerical optimization techniques to solve for the regression coefficients, are derived. In addition to the residual life estimation approach, a user-friendly graphical method with which residual life estimates can be presented was also developed. This method is natural and easy to comprehend, even by inexperienced data analysts. The residual life estimation approach is applied to a typical data set from a South African industry and results are compared to those obtained from a similar, established maintenance decision support tool. This comparison showed that the approach developed in this thesis is relevant, practical and marginally better than the established decision support tool for certain criteria.

2016 International Conference on Electrical Engineering and Automation (EEA2016) was held in Hong Kong, China from June 24th-26th, 2016. EEA2016 has provided a platform for leading academic scientists, researchers, scholars and students around the world, to get together to compare notes, and share their results and findings, in areas of Electronics Engineering and Electrical Engineering, Materials and Mechanical Engineering, Control and Automation Modeling and Simulation, Testing and Imaging, Robotics, Actuating and Sensoring.The conference had received a total of 445 submissions. However, after peer review by the Technical Program Committee only 129 were selected to be included in this conference proceedings; based on their originality, ability to test ideas, and contribution to the understanding and advancement in Electronics and Electrical Engineering.

This book reviews the status quo and visions for the future in the wind energy industry in China and around the globe, focusing on its roles in optimizing energy structure, alleviating environmental pollution, and coping with climate change. Providing a blueprint of wind power development till 2050, it suggests a series of further measures in the context of policies, regulations, laws, and marketing in order to overcome the existing bottlenecks. Moreover, it proposes a number of potential innovative technologies related to IT+ and advanced manufacturing, including integrated & distributed power and micro-grid systems, multi-energy complement, green and intelligent manufacturing, reliability design, blade design, manufacturing and maintenance, drive drain systems, and offshore wind farms. This book offers researchers and engineers insights into sustainable development in the wind power industry.

Within the last fifty years the performance requirements for technical objects and systems were supplemented with: customer expectations (quality), abilities to prevent the loss of the object properties in operation time (reliability and maintainability), protection against the effects of undesirable events (safety and security) and the ability to

This book provides a complete picture of several decision support tools for predictive maintenance. These include embedding early anomaly/fault detection, diagnosis and reasoning, remaining useful life prediction (fault prognostics), quality prediction and self-reaction, as well as optimization, control and self-healing techniques. It shows recent applications of these techniques within various types of industrial (production/utilities/equipment/plants/smart devices, etc.) systems addressing several challenges in Industry 4.0 and different tasks dealing with Big Data Streams, Internet of Things, specific infrastructures and tools, high system dynamics and non-stationary environments . Applications discussed include production and manufacturing systems, renewable energy production and management, maritime systems, power plants and turbines, conditioning systems, compressor valves, induction motors, flight simulators, railway infrastructures, mobile robots, cyber security and Internet of Things. The contributors go beyond state of the art by placing a specific focus on dynamic systems, where it is of utmost importance to update system and maintenance models on the fly to maintain their predictive power.

This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions.