This paper suggests three novel methods for selecting the frequencies of sinusoidal test signals to be used in fault diagnosis of analog electronic circuits. The first and second methods are based on a sensitivity analysis and show to be particularly effective in linear circuits where a priori information and designer experience can be exploited. Conversely, the third method selects the input frequencies to be used for diagnostic purposes without requiring any hypothesis about the circuit or testing design background. As such, the method is particularly appealing in complex -possibly nonlinear - circuits where the designer experience is of little value and an effective "blind" approach saves both designer and testing time. The suggested frequency selection methods are then contrasted to each other against performance and computational complexity.
Alippi, C., Catelani, M., Fort, A., Mugnaini, M. (2005). Automated selection of test frequencies for fault diagnosis in analog electronic circuits. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 54(3), 1033-1044 [10.1109/TIM.2005.847115].
Automated selection of test frequencies for fault diagnosis in analog electronic circuits
FORT A.;MUGNAINI M.
2005-01-01
Abstract
This paper suggests three novel methods for selecting the frequencies of sinusoidal test signals to be used in fault diagnosis of analog electronic circuits. The first and second methods are based on a sensitivity analysis and show to be particularly effective in linear circuits where a priori information and designer experience can be exploited. Conversely, the third method selects the input frequencies to be used for diagnostic purposes without requiring any hypothesis about the circuit or testing design background. As such, the method is particularly appealing in complex -possibly nonlinear - circuits where the designer experience is of little value and an effective "blind" approach saves both designer and testing time. The suggested frequency selection methods are then contrasted to each other against performance and computational complexity.File | Dimensione | Formato | |
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https://hdl.handle.net/11365/21708
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