Diagnosability study for quality improvement based on distributed sensing and information technology

  • Authors:

  • Du Shichang;Xi Lifeng;Pan Ershun;Shi Jianjun;Ni Jun;Liu C. Richard

  • Affiliations:

  • 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

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2007

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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.