CAREER: Collaborative Information Processing of Distributed Sensor Networks for Manufacturing Quality Improvement

点击翻译按钮获取中文摘要

This Faculty Early Career Development (CAREER) proposes to develop, teach, and implement collaborative information processing methodologies for distributed sensor networks in manufacturing quality improvement to support an integrated research and education program. Recent research has focused on development of hardware (sensor devices) and network techniques. The challenge now is to provide decision support capabilities that will allow the full potential of distributed sensor networks to be realized for manufacturing quality control. Toward that goal, the proposed methodology includes: (i) collaborative fault diagnoses in networked sensor systems; (ii) in-service sensor system self-diagnosis; and (iii) diagnosis-oriented optimal design of sensor distribution. The CAREER project will employ a multidisciplinary approach, where engineering knowledge, multivariate statistics (such as spatio-temporal analysis), as well as control theory (such as parity space approaches) will be integrated to address the technological challenges. The CAREER project will undertake various collaborative efforts to expand research opportunities for undergraduate students and teachers/students from local high schools. If successful, this CAREER project will advance the state of the art on distributed sensor networks by contributing new concepts, criteria, and algorithms to its information-processing capabilities and create an enabling methodology for next-generation manufacturing diagnostic systems. Accomplishing the goal will lead to a remarkable cost reduction as a result of improved quality and reduced downtime, boosting the competitiveness of US industries and our nation's economy. The CAREER education program will make a positive contribution to workforce training through curriculum and lab development, teaching innovations, and other outreach activities. Collaboration with a minority-serving university and local high schools will enhance engineering education for underrepresented groups. Broad dissemination through conference/journal publications and international/industrial collaborations will lead to wide application of the resulting methodology to many other distributed sensor networks that are of vital importance to the nation's economic growth.