Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions
AbstractPin connections are commonly used in many engineering fields, and continuous operation may cause severe wear on the pins and may lead to their eventual fracture, if undetected. However, a reliable nonintrusive real-time method to monitor the wear of pin connections is yet to be developed. In this paper, acoustic emission (AE)-based parametric analysis methods, including the logarithm of the cumulative energy (LAE), the logarithm of the slope of cumulative energy (LSCE), the b-value method, the Ib-value method, and the fast Fourier transformation (FFT), were developed to quantify the wear degree of pin connections. The b-value method offers a criterion to quickly judge whether severe wear occurs on a pin connection. To assist the research, an experimental apparatus to accelerate wear test of pin connections was designed and fabricated. The AE sensor, mounted on the test apparatus in a nondestructive manner, is capable of real-time monitoring. The micrographs of the wear of pins, and the surface roughness of pins, verified that the values of the max LAE and the max LSCE became larger as the wear degree of pin connections increased, which means different values of the max LAE and the max LSCE can reflect different wear degree of pin connections. Meanwhile, the results of the micrographs and surface roughness confirmed that the b-value is an effective method to identify severe wear, and the value “1” can be used as a criterion to detect severe damage in different structures. Furthermore, the results of spectrum analysis in the low frequency range showed that the wear frequency was concentrated in the range of 0.01 to 0.02 MHz for the pin connection. This study demonstrated that these methods, developed based on acoustic emission technique, can be utilized in quantifying the wear degree of pin connections in a nondestructive way. View Full-Text
Share & Cite This Article
Wang, J.; Huo, L.; Liu, C.; Song, G. Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions. Sensors 2018, 18, 3503.
Wang J, Huo L, Liu C, Song G. Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions. Sensors. 2018; 18(10):3503.Chicago/Turabian Style
Wang, Jingkai; Huo, Linsheng; Liu, Chunguang; Song, Gangbing. 2018. "Wear Degree Quantification of Pin Connections Using Parameter-Based Analyses of Acoustic Emissions." Sensors 18, no. 10: 3503.
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.