Technology for Sensing and Reducing Noise in Acoustic Emission Signals for Online Detection of Wear Condition of Transmission Gears in a Nuclear Power Plant

Once a nuclear power plant is started, its power transmission devices need to be kept running for a long time without stopping.Currently, the wear condition of gears in the transmission gear box of a nuclear power plant cannot be monitored in real time.An unexpected stoppage due to the damage of power transmission gears would cause significant losses.To solve this problem, we propose a novel technology for sensing and reducing the noise in acoustic emission (AE) signals, which may be used to effectively detect, analyze, and monitor the surface friction condition of transmission gears in a nuclear power plant in real time.In this study, we first analyze the crucial factors that affect the signal energy of AE from the contact surface of teeth via the Hertz contact theory.Second, we propose a novel noise reduction method to deal with the detected AE signals.Third, we establish a mathematical model that correlates the lubricant viscosity, gear load, and the running speed of gears with the energy of AE signals.Fourth, we design a novel AE signal acquisition-analysis system.Finally, we perform verification tests via this system.The results of these tests are satisfactory.Using the proposed sensing and noise reduction methodology, we effectively analyzed the surface wear of transmission gears in a nuclear power plant in real time.Appropriate safety measures can be taken in time on the basis of our monitoring results.