Jie Xu;Lingrong Kong;Yu Wang;Haoyu Wang;Haodong Hong
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Monitoring Downhole Machinery Operations Using Noncontact Triboelectric Nanogenerators and Deep Learning
Accurate monitoring of downhole equipment during drilling processes holds significant importance in mitigating drilling risks and accidents while enhancing drilling efficiency. Instantaneous reversal and lateral vibrations of downhole machinery can cause substantial damage to the equipment and drill bit, making them crucial parameters to monitor. This article proposes a noncontact triboelectric nanogenerator (NC-TENG) capable of employing deep learning methods to monitor four operational conditions in downhole environments: clockwise rotation, counterclockwise rotation, low-frequency vibration interference, and high-frequency vibration interference, achieving a classification accuracy of up to 99.45%. Furthermore, the NC-TENG boasts advantages such as a simple structure, extended lifespan, and high signal-to-noise ratio, making it more suitable for complex downhole conditions. The introduction of the NC-TENG offers a viable approach for the development of novel intelligent measurement devices and technologies for downhole applications.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
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-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice