Spectro-Temporal Self-Similarity Based Identification of Corrupted Acoustic Signal of Distribution Transformer in Noisy Environment

Operating Acoustic Signal (OAS) emitted by electric equipment reveals its state. However, environmental noise (EN) could result in misleading result. Distribution transformers are deployed in noisy environment and its OAS is mixed with substantial noise. OAS of distribution transformer in normal and typical anomaly state behave spectro-temporal self-similarity (STSS), which is notable different from known ENs. A STSS based approach is proposed to identify whether OAS is corrupted by EN. 60s OAS of transformer are divided into overlapping segments. The features of these segments are grouped to determine whether they belong to the same cluster. Once they belong to the same cluster, it can be used for condition monitoring. Otherwise, it is corrupted by EN. Numerical simulation of onsite recorded OAS indicates that the proposed approach can identify OAS corrupted by EN precisely. Difficulty of removing EN from OAS could be avoided in a much easier alternative way to single out valid OAS not corrupted by EN instead of removing EN from OAS. The proposed approach facilitates OAS based condition monitoring of distribution transformers in noisy environment.