Least squares (LS) estimation has been extensively used for parameter identification and model-based diagnosis. However, if ill-conditioning is present, the LS estimation approach tends to generate imprecise results and thus impacts the diagnostic performance. In this paper, an adjusted least squares approach is proposed to deal with the ill-conditioning problem in the diagnosis of compliant sheet metal assembly process. The adjusted LS approach is able to overcome the ill-conditioning and give precise results for certain linear combinations of the faults. Simulations and industrial case study are conducted to compare the diagnostic performance of the adjusted and regular LS approach. In addition, a two-stage assembly model is developed for further fault isolation with inclusion of additional measurement information.
Adjusted Least Squares Approach for Diagnosis of Ill-Conditioned Compliant Assemblies
Contributed by the Manufacturing Engineering Division for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received August 1999; revised March 2000. Technical Editor: E. C. DeMeter.
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Rong, Q., Shi, J., and Ceglarek, D. (March 1, 2000). "Adjusted Least Squares Approach for Diagnosis of Ill-Conditioned Compliant Assemblies ." ASME. J. Manuf. Sci. Eng. August 2001; 123(3): 453–461. https://doi.org/10.1115/1.1365116
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