Intelligent defect diagnosis of GIS basin insulator via multi-source ultrasonic fusion

ABSTRACTTo take advantage of ultrasonic-based non-destructive testing (NDT) and data-driven intelligent defect diagnosis, the current study proposes a feature tensor classifier based on multi-source ultrasonic fusion, to enhance the defect diagnosis adaptability and reliability for gas-insulated switchgear (GIS) basin insulator. First, multi-source ultrasonic signals are acquired by finite element modelling (FEM), describing the healthy states of the GIS basin insulator completely. Second, time of flight (Tof)-featured tensors are expressed by wavelet transform (WT), and used to create the datasets. Third, a deep learning-based feature tensor classifier is proposed, and concerned training, validation, and testing processes are carried out. Finally, the effectiveness of feature tensor extraction is evaluated, and the anti-noise performance of the Tof-featured tensor classifier is verified. The main contributions indicate that the Tof-featured tensor classifier can realise excellent diagnosis performance, the average accuracy is, respectively, 90.53%, 99.75%, and 100% in training, validation, and testing sets, while the signal tensor classifier has poor performance. In addition, three other noised datasets are applied, and the result shows that the anti-noise performance of the Tof-featured tensor classifier is feasible, when SNR is greater than 1 dB.KEYWORDS: GIS basin insulatorintelligent fault diagnosismulti-source ultrasonic fusiontof-featured tensorconvolution neural network AcknowledgmentsMy heartfelt thanks are due to Prof. Han for his academic supervision and personal support. This work was supported by the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (Grant No. STIP2020L0699), Fund program of Key Laboratory of Signal Capturing & Processing, North University in Shanxi (Grant No. ISPT2020-8).Disclosure statementNo potential conflict of interest was reported by the author(s).