Machine condition monitoring enabled by broad range vibration frequency detecting triboelectric nano-generator (TENG)-based vibration sensors. (July 2022)

• Record Type: Journal Article
• Authors: Mehamud, Idiris; Marklund, Pär; Björling, Marcus; Shi, Yijun
• Is Part Of: Nano energy. Volume 98(2022)
• Publication Date: 2022-07
• Subjects: Condition monitoring -- Bearing defect -- Vibration sensor -- High-frequency -- Spring-assisted -- Triboelectric-nanogenerator; Nanoscience -- Periodicals; Nanotechnology -- Periodicals; Nanostructured materials -- Periodicals; Power resources -- Technological innovations -- Periodicals; Nanoscience; Nanostructured materials; Nanotechnology; Power resources -- Technological innovations; Periodicals; 621.042
• DOI: 10.1016/j.nanoen.2022.107292
• Languages: English
• ISSNs: 2211-2855

Abstract

Abstract: Vibration analysis is an efficient method to monitor machine condition status. Different types of vibration sensors, such as microelectromechanical, and piezoelectric accelerometers have been used to measure vibrations, but face the problems of relying on external power and high cost. Recently, triboelectric nano-generator (TENG)-based vibration sensors have attracted attention to solve these problems. However, previous studies on TENG-based mechanical vibration sensors are limited to a low-frequency range (less than 200 Hz), which is below industry machine condition monitoring requirements (often 10–1000 Hz). This work aims at enabling TENG-based vibration sensors for higher frequencies and a broad range of frequency detection through structural design supported by numerical simulations. Numerical simulation results indicate that the frequency detection range is controlled by structural design and can be easily expanded for high-frequency detection by reducing the size and improving the shape of the structure. Spring-assisted TENG-based vibration sensors with the possibility of detecting the vibration within 0–1200 Hz, which covers the major mechanical failures and imperfections in vibrational frequency ranges, are prepared according to the structural design and numerical simulation results. The experimental results show that the developed sensor successfully detects signals within the frequency range of 0–1200 Hz. Due to optimized structural symmetry and effective spring stiffness, the two spring-assisted (TS) structures generate higher electric signal output (up to 200 V and 0.9 µA). The prepared TENG vibration sensors are further compared with a high-quality commercial vibration sensor in terms of vibrational signal response and detecting bearing defects. The results show that the prepared TENG vibration sensors can provide at least the same function as the commercial vibration sensor and demonstrate a promising potential to detect machine working conditions. Graphical Abstract: ga1 Highlights: A Self-powered TENG Sensor for machine condition monitoring was proposed. Different Structural designs are compared to enhance the TENG performance. Through numerical calculation and simulation, broad range vibration frequency detection of 0–1200 Hz was achieved. Successful detection of bearing defects on the raceway was achieved.