An efficient feature extraction approach based on manifold learning for analogue circuits fault diagnosis

Automated fault diagnosis of analogue circuits makes use of dimension reduction (DR) technologies to extract fault features. Traditional DR methods such as the principal component analysis (PCA) and kernel based DR methods have achieved valuable results. However, these methods give inadequate consideration to the geometric structure of fault data which is embedded in high dimensional spaces (HDS). This negatively affects the low-dimensional features. Recently introduced, manifold learning methods have the ability to recover the intrinsic geometric structure of a data set in low dimensional spaces (LDS). Locally linear embedding (LLE) and diffusion maps (DM) are two popular approaches of manifold learning methods. The former preserves the local linear structure of a data set and the latter is resistant to outlier effects. We present a novel DR algorithm using both LLE and DM to extract fault features based on the analysis of the intrinsic geometric structure of fault data. First, by the correlation analysis of the original fault data, we conclude that they are locally linear dependent, and then use LLE to obtain mid-level fault features. The calculation of local outlier factor (LOF) values confirms the existence of outliers in the fault data with mid-level features. Then, we apply DM to eliminate the negative effect of outliers and derive visual data with high-level features. Our experimental results show that the proposed algorithm offers performance superior to the traditional DR methods regardless of whether the circuit is linear or non-linear.