Optimization of sound sensor placement for condition monitoring of fixed-axis gearbox. Issue 1 (1st January 2017)

• Record Type: Journal Article
• Authors: Vanraj, ; Dhami, S.S.; Pabla, B.S.
• Editors: Ding, Guoping
• Is Part Of: Cogent engineering. Volume 4:Issue 1(2017)
• Publication Date: 2017-01-01
• Subjects: optimal sensor placement -- response surface methodology (RSM) -- fault diagnosis -- sound analysis -- fixed-axis gearbox; Engineering -- Periodicals; Technology -- Periodicals; Engineering; Technology; Periodicals; 620
• DOI: 10.1080/23311916.2017.1345673
• Languages: English
• ISSNs: 2331-1916

Abstract

Abstract : Condition monitoring is expected to assist in the prevention of machine failures and enhance the reliability with lower maintenance cost. With advancement in sensor and computer technologies, it is now possible to acquire and process a large amount of machine response data in order to extract the characteristic features which provide the indication about health condition of the system. In real applications, the machine has infinite node positions but sensors can be only placed at a finite number of locations. Hence selection of optimal node positions is a challenging task and needs to be addressed. The objective of sound sensor placement optimization is to obtain a sensor layout that gives as much information of the dynamic system as possible for condition monitoring. This paper proposes a methodology for placing sound sensor on a fixed-axis gearbox to obtain high-quality information regarding the dynamic characteristics of machine. The gearbox is operated under no load and load with varying levels of gear faults and sound sensor placement positions. Loudness, loudness level, and sharpness of sound has been considered as response parameters. Mathematical relations and models between input variables and response parameters are developed. Sound sensor placement is optimized for maximum Loudness, loudness level, and sharpness values. The models are experimentally validated and tested. Results indicate that an overall accuracy of 92.2% is achieved and the approach has significant utility in industrial environment where system complexity makes the choice of sensor placement vital for condition monitoring.