Abstract
Infrared thermography has been used as a key means for the identification of overheating defects in power cable accessories. At present, analysis of thermal imaging pictures relies on human visual inspections, which is time-consuming and laborious and requires engineering expertise. In order to realize intelligent, autonomous recognition of infrared images taken from electrical equipment, previous studies reported preliminary work in preprocessing of infrared images and in the extraction of key feature parameters, which were then used to train neural networks. However, the key features required manual selection, and previous reports showed no practical implementations. In this contribution, an autonomous diagnosis method, which is based on the Faster RCNN network and the Mean-Shift algorithm, is proposed. Firstly, the Faster RCNN network is trained to implement the autonomous identification and positioning of the objects to be diagnosed in the infrared images. Then, the Mean-Shift algorithm is used for image segmentation to extract the area of overheating. Next, the parameters determining the temperature of the overheating parts of cable accessories are calculated, based on which the diagnosis are then made by following the relevant cable condition assessment criteria. Case studies are carried out in the paper, and results show that the cable accessories and their overheating regions can be located and assessed at different camera angles and under various background conditions via the autonomous processing and diagnosis methods proposed in the paper.
Highlights
Power cables have been widely used in urban power systems, and their safe operation is key to the reliability of the power grid [1]
Reference [3,4] indicated that the manufacturing of fault-free cable accessories is almost impossible, and poor workmanship during installations and design defects may result in cable faults
This paper proposes a method for autonomous diagnosis of overheating defects in cable accessories based on a Faster RCNN network and Mean-Shift algorithm
Summary
Power cables have been widely used in urban power systems, and their safe operation is key to the reliability of the power grid [1]. The processing and analysis of infrared images taken during inspections mainly require visual inspection This is time-consuming and laborious on the one hand, and on the other hand, it relies too much on expert experience and is prone to erroneous diagnosis. The image features related to the temperature gradients of equipment were used as the input of neural networks for the autonomous diagnosis of electrical equipment. This paper proposes a method for autonomous diagnosis of overheating defects in cable accessories based on a Faster RCNN network and Mean-Shift algorithm. The collected infrared images of cable accessories during routine inspection activities are used as samples to complete the training of the Faster RCNN network as to identify and locate the objects to be diagnosed. The temperature characteristic parameters are calculated, the condition of cable accessories can be diagnosed according to pre-set diagnostic criteria
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