Oscillation-based diagnosis by using harmonics analysis on analog filters

Abstract

The Oscillation-Based Test (OBT) method converts the circuits under test (CUTs) into self-oscillating mode by changing CUT's typology or adding feedback path(s). In traditional OBT, the frequencies and amplitudes of sinusoidal oscillation are used as fault features to build up the fault dictionary, which is capable of test but not diagnosis. This paper designs harmonic feedback path to enlarge the harmonics in oscillation, and then builds up fault dictionary to diagnose the analog faults and eliminate the deviation effects by membership functions. The simulation and PCB experiment results prove that the proposed method is both applicable and practical.

Parametric Circuit Fault Diagnosis Through Oscillation-Based Testing in Analogue Circuits: Statistical and Deep Learning Approaches

“…Before presenting the details each approach in this work, it is prudent to briefly review prior approaches. Previous OBD studies [2], [11], [12] all fall within the supervised category because labelled data can be generated during the simulation and is used to explicitly create a classification model by using the known input and output of each sample. This model can then be used to classify new unseen and unlabeled samples to perform OBD.…”

“…White noise, with an amplitude of 1% of the output signals, was added to the simulated signals to verify robustness, though the link between this introduction and probability component value variation is not presented. Other prior literature on OBD includes membership function diagnosis in [12], with the state-of-the-art work in [13] incorporating simultaneous Monte Carlo statistical variation for non-faulty component values.…”

“…This time series diagnosis is performed both with a statistical learning approach [16], and a deep learning approach [17]. Both approaches are compared to the harmonics analysis currently used in OBD [2], [11], [12] and class activation maps (CAMs) used to highlight which data are more crucial in correct classification. This is a significant finding in selecting of suitable data, potentially reducing the resources required in collecting data that does not contribute to classification accuracy.…”

Oscillation-Based DFT for Second-Order Bandpass OTA-C Filters

“…Based on reconfiguration of original circuit to oscillator, [24] proposed a built-in self-test circuit for testing analogue and mixed-signal circuits and considered the proposed solution on testing the typical benchmark circuit of second-order active filter. To extend OBT, [25] used harmonics analysis to improve the fault coverage and the capacity of fault locating. In [26], diagnosis based on OBT was implemented by creating fault dictionary and running artificial neural networks as classifiers.…”