Abstract:

Even though analog circuits comprise a small minority among all circuits, they are responsible for the vast majority of all faults. Hence, analog circuit fault diagnosis and prognosis is crucial in preventing failure and reducing unplanned downtime in industrial electronics. There are a multitude of ways that any analog circuit can fail, which leads to proportional scaling in the number of possible fault classes with circuit complexity. This paper presents an advanced design of experiments-based approach, using supersaturated and space-filling designs, to account for components that degrade in both an individual and interacting fashion, to narrow down the number of possible fault classes that must be considered. Next, a wavelet-based deep learning network called WavePHMNet is developed that can localize the circuit component(s) that is the source of degradation and estimate the value of the degraded component(s), all based solely on the output waveforms produced by the circuit. This degraded value can be used in conjunction with component degradation models to predict circuit remaining useful life. An implementation of this approach is demonstrated on three circuits: a Sallen-Key bandpass filter (7 components), a two-switch forward convertor (25 components), and a digital to analog convertor (260 components). The approach is also demonstrated experimentally on the two-switch forward convertor circuit.

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