Reliability Prediction Based on Dynamic Data Collected with Modern Technology

Virginia Polytechnic Institute And State University, Blacksburg VA

Investigators

Abstract

The research objective of this award is to develop a general framework that can incorporate large-scale dynamic data to obtain more accurate reliability predictions. Modern technology, such as smart-chips, sensors and wireless networks, has changed data collection processes. There are more and more products installed with automatic data-collecting devices (ADCDs) which can dynamically record system performance, usage and environmental information for individual units in the field and/or transmit this information to data centers with owner's permissions. These products range from jet engines, wind turbines, power transformers and CAT scanners, to automobiles, copier machines and smart-phones. This research will first develop general models for incorporating dynamic data into predictions. Then methods will be developed for quantifying statistical uncertainties and advantage of using dynamic data. Sensitivity analysis will be conducted to assess model uncertainties. Computationally efficient algorithms and free software that is capable of processing large-scale datasets will also be developed. The developed methods will be validated with datasets from industrial and government partners. If successful, this research will provide a much-needed new paradigm for the arriving generation of field reliability data. In the near future when the cost of ADCDs further decreases, more and more products will be equipped with ADCDs. This research will have applications in various important areas such as manufacturing, renewable energy, and health care, because reliability information is critical for manufacturers to improve the competitive position of their products, and is also important for cost analysis, capital expenditures, and risk controls. The development of free software will make it possible for the developed methods to be widely disseminated. Graduate and undergraduate students from under-represented groups and women will be involved in this research. The integration of research with teaching will present students with modern reliability data analysis concepts and techniques.