Oscillation-based analog diagnosis using artificial neural networks based inference mechanism

In this paper, Oscillation-Based Diagnosis (OBD) of analog electronic circuits, derived from Oscillation-Based Test (OBT), is described for the first time. OBT is an effective and simple solution to the testing problem of continuous time analog filters. The inadequacy of using an infinite-gain model of the op-amps is demonstrated and a practical implementation of the theoretical concept of OBT is discussed. A realistic model of the op-amp is therefore implemented. A fault dictionary is created and used to perform diagnosis, with artificial neural networks (ANNs) as classifiers. The robustness of the ANN diagnostic concept is demonstrated by the addition of white noise to the “measured” signals. The effectiveness of OBD is demonstrated by testing and diagnosis of a second order notch cell, realized with one operational amplifier. Single soft and catastrophic faults are considered in detail and an example of the diagnosis of double soft faults is also presented.

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► We introduce oscillation-based diagnosis as a method for diagnosis of analog cells.
► We consider single soft and catastrophic faults, and double soft faults.
► We use the simulation-before-test concept to create fault dictionaries.
► Input ANN signals are the fault effects and we give a fault code to every fault to be learned by the ANN.
► Diagnosis is performed by comparison of the ANN output excited by the measured signals, with the list of fault codes.