Probabilistic Graphical Models for the Diagnosis of Analog Electrical Circuits

Abstract

Abstract. We describe an algorithm to build a graphical model-more precisely: a join tree representation of a Markov network-for a steady state analog electrical circuit. This model can be used to do probabilistic diagnosis based on manufacturer supplied information about nominal values of electrical components and their tolerances as well as measurements made on the circuit. Faulty components can be identified by looking for high probabilities for values of characteristic magnitudes that deviate from the nominal values.

Fault diagnosis in analog electrical circuits: data-driven method

“…-the simulation of a circuit for different faults to generate training data for an artificial neural network is presented in [2,15]; -the method of establishing a fault dictionary using Wavelet transform is developed in [13]; -in [6,7] the probabilistic graphical models are used; here faulty components are identified by looking for high probabilities for values of characteristic magnitude that deviate considerably from the nominal values; -the probabilistic model-based approach presented in [12] is formally founded and based on Bayesian network and arithmetic circuits; -the method based on diagnostic observers of states developed in [5,[20][21][22] considers linear and nonlinear circuits as dynamic systems; it is necessary to stress that the method suggested in [21] can be used for diagnosis in electrical circuits containing non-smooth nonlinearities such as hysteresis and saturation.…”

Filaretov, V. F.; Zhirabok, A. N. and Tkachev, D.: Non-Parametric Method for Fault Diagnosis in Electrical Circuits

“…in [6,7] the probabilistic graphical models are used, here faulty components are identified by looking for high probabilities for values of characteristic magnitude that deviate considerably from the nominal values;…”