Non‐destructive testing method of micro‐debonding defects in composite insulation based on high power ultrasonic

Wang, Hanqing; Cheng, Li; Liao, Ruijin; Zhang, Sida; Yang, Lijun

doi:10.1049/hve.2019.0044

Cited by 22 publications

(14 citation statements)

References 20 publications

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“…The surface microstructure of the GRP rod is typically observed with a scanning electron microscope [21] and metallographic microscopy [25,26]. Because the fragility of the glass fibre makes it impossible to make slices, using the same techniques to analyse the holes inside the rod is difficult.…”

Section: Analysis Methods Of Pore Structurementioning

confidence: 99%

Study on the detection method of holes in composite insulator rods

et al. 2021

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The holes in composite insulator rods are a type of quality defects. However, not all holes can be identified by the dye penetration test according to existing standards and some holes can cause abnormal heating of the composite insulator during operation. Microcomputed tomography is used to study the structure of these holes. Based on the pore structure, the holes are classified as dependent or independent. The dependent hole is formed by several interconnected adjacent pores and the overlong equivalent penetration path makes it cannot be effectively identified by the standard dye penetration test. Considering the structure of the dependent hole, ethanol gas was used to increase the effectiveness of the dye penetration test. The experimental results show that the detection rate of dependent holes is approximately 80% as the ethanol concentration is close to saturation, and the porosity can be inferred based on the number of penetration points. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Analysis Methods Of Pore Structurementioning

confidence: 99%

Study on the detection method of holes in composite insulator rods

et al. 2021

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“…Compared with traditional learning algorithms such as SVM, DL is a data driven method without the process of manual feature extraction. Due to the use of complex models, depth features have more accurate and general expression ability [76]. DL technique has been widely applied to identify objects in transcribing speech into text, match news items, posts or products with users' interests, and select relevant results of search, especially in image target detection [75].…”

Section: Image‐based Intelligent Fault Identificationmentioning

confidence: 99%

“…As mentioned as Section 4.5, the thermal fault of power equipment is a comprehensive result by various underlying causes, which are companied with other physical phenomena, such as electrical discharges and mechanical vibrations and can be reflected in a specific range of optical or acoustical spectrum, as shown in Figure 17. It is believed that in the foreseeable future, the knowledge dimensionality on fault diagnosis can be significantly improved by adopting more visual perception technologies [76,[86][87][88][89].…”

Section: Comprehensive Utilization Of Joint Visualization Diagnosis T...mentioning

confidence: 99%

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

et al. 2020

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As a non‐contact temperature distribution measurement method, infrared thermography (IRT) has emerged as an indispensable tool in condition monitoring and fault diagnosis of electrical equipment based on the absolute and relative temperature values. Manual fault inspection, as an expert‐experiences based evaluation method, has formed a mature technical scheme with a large number of application cases. However, the efficiency and accuracy of manual fault inspection are being challenged by the rapid growth in the number of equipment in power grid. The situation is improving with the advanced of image processing technique. Machine‐assisted fault diagnosis provides a novel method to assist human beings to complete fault diagnosis under the intervention of human prior knowledge. However, the limitations of infrared images bring challenges to image analysis processing especially target detection. In pursuit of automatic fault diagnosis, deep learning algorithms are introduced to achieve target detection in the complex environment. This study reviews the development of IRT‐based diagnostics beginning with the general procedures, objects, and limitations of IRT‐based fault inspection, and then gives an insight into the popular machine‐assisted fault diagnosis as well as image‐based intelligent fault identification. In addition, the future recommendations of IRT are also provided from construction of intelligent infrared detection system, establishment of an open and shared infrared image database and comprehensive utilization of joint visualization diagnosis technology.

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“…Li et al [14] derived the quantitative relationship between the nonlinear coefficient  and millimeter scale debonding defects. Wang et al [15] effectively diagnosed the early micro-debonding defects at the composite interface using the nonlinear coefficient .…”

Section: Introductionmentioning

confidence: 99%

Application of local defect internal resonance method for detection of adhesive interface debonding

2022

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The bonding interface is prone to debonding under environmental implication and long-time operational vibrations, which may affect the structural integrity severely. This paper presents an interface debonding identification method to solve adhesive interface debonding between aluminum plates in the framework of the internal resonance of local defects. Firstly, based on a two degree of freedom (2-DOF) nonlinear model of local defects in the bonding interface, the multi-scale method is used to explain the mechanism of the internal resonance of local defects and conditions for the generation of the internal resonance. By analyzing the saturation phenomenon peculiar to the 2:1 internal resonance of local defects in the spectrum, the adhesive interface debonding identification experiment of a bonding aluminum plate is carried out. The effectiveness of the local defect internal resonance method is verified by experimental results and theoretical analysis. The results demonstrate that the bonding structure appears the saturation phenomenon under the specific excitation conditions. Furtherly the local defect internal resonance method is found to be effective in solving adhesive interface debonding detection.

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Non‐destructive testing method of micro‐debonding defects in composite insulation based on high power ultrasonic

Cited by 22 publications

References 20 publications

Study on the detection method of holes in composite insulator rods

Study on the detection method of holes in composite insulator rods

Infrared thermography‐based diagnostics on power equipment: State‐of‐the‐art

Application of local defect internal resonance method for detection of adhesive interface debonding

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