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This paper presents an automated method for analog system diagnostics, which aims to detect and localize multiple faults in noisy conditions. The generic architecture of the diagnostic scheme and its stages of denoising, stamp extraction, and fault detection are explained. The method is tested on three systems of various physical nature. Then, approaches to automated diagnostics of the different classes of the systems are proposed. Machine learning methods (decision-tree-based fuzzy logic) are used to effectively detect faults. Their advantages are explained and confirmed by examples.