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Fault diagnosis has been established in two main approaches: model-based fault diagnosis and model-free fault diagnosis. Present paper focuses on the later, mainly as an extension of the approach proposed in [17]. The challenge here is to classify faults at early stages, with an accurate response. However, as the term model-free implies, a model for the plant is not available neither for fault-free nor for fault-present scenarios. The objective, thus, is to classi‐ fy faults based on system’s response and the related signal analysis, in terms of dilation and shift decomposition, as obtained by a wavelets approach. So, self-organizing maps (SOM) are proposed as a powerful nonlinear neural network to achieve such a fault classification.

Using Wavelets for Feature Extraction and Self Organizing Maps for Fault Diagnosis of Nonlinear Dynamic Systems