Article Abstract

	Title:	Diagnosability study for quality improvement based on distributed sensing and information technology
	Author:	Du Shichang, Xi Lifeng, Pan Ershun, Shi Jianjun, Ni Jun, Liu C. Richard
	Address:	Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China. ' Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China. ' Department of Industrial Engineering and Management, School of Mechanical Engineering, Shanghai Jiaotong University, Shanghai, China. ' Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, USA. ' Department of Mechanical Engineering, The University of Michigan, Ann Arbor, MI 48109, USA. ' School of Industrial Engineering, Purdue University, West Lafayette, IN 47907, USA
	Journal:	International Journal of Computer Applications in Technology 2007 - Vol. 28, No.2/3  pp. 117 - 127
	Abstract:	With rapid innovations in information technology and sensing technology, increasingly less expensive and smart devices with multiple heterogeneous on-board sensors, networked through wireless links and deployable in large numbers, are distributed throughout complex Multistage Manufacturing Systems (MMSs). These technologies provide unprecedented opportunities for quality improvement. If product-sensing data are obtained via certain distributed sensing and information system, the problem of whether the faults of a manufacturing system are diagnosable is of great interest to both academia and industry. In this study, the diagnosability of the process faults in a MMS is defined in a general way using a linear input-output model, which does not depend on specific diagnosis algorithms. The condition of faults diagnosability, the diagnosability matrix and indices are defined and derived. Finally, the methodology is illustrated by a machining process and a hot deformation process.
	Keywords:	benchmarking; diagnosability; distributed sensing systems; fault diagnosis; information technology; manufacturing information systems; multistage manufacturing systems; quality improvement; machining; hot deformation.
	DOI:	10.1504/IJCAT.2007.013349
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