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According to the advantages of integrating wireless sensors networks (WSN) and radio frequency identification (RFID), this paper proposes a novel method for methane gas density monitoring and predicting based on a passive RFID sensor tag and a convolutional neural networks (CNN) algorithm. The proposed wireless sensor is based on electronic product code (EPC) generation2 (G2) protocol and the sensor data is embedded into the identification (ID) information of the RFID chip. The wireless sensor consists of a communication section, radio-frequency (RF) front-end section, and digital section. The communication section is used to perform the transmission and reception of wireless signals, modulation, and demodulation. The RF front-end section is adopted to provide the stable supply voltage for other parts. The digital section is employed to achieve sensor data and control the overall operation of the wireless sensor based on EPC protocol. Because the miscellaneous noises will decrease the accuracy during the process of data wireless transmission, the CNN algorithm is adopted to extract the robust feature from raw data. The measurement results show that the exploited RFID sensor can realize a maximum communication distance of 10.3 m and can accurately measure and predict the methane gas density in an underground mine. The RFID sensor technology is a beneficial supplement to the current underground WSN monitoring system.

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto‐Encoder (DCAE) for small negative samples. The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning. In order to reduce the high cost of training Deep Neural Networks, this paper pre‐trained the Convolutional Neural Networks (CNN) through open labelled datasets. Through transferring learning, the encoder part of the traditional Convolutional Auto‐Encoder was replaced by the first three layers of the CNN, and a small number of defect samples were used to fine‐tune the parameters. A threshold discrimination scheme was designed to evaluate the model detection, realising the self‐explosion detection of insulator by judging the residual result and abnormal score. The experimental results show that compared with the existing insulator self‐explosion detection schemes, the proposed scheme can reduce the model training time by up to 40%, and the recognition accuracy can reach 97%. Moreover, this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

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