A correlation coefficient based vibration indicator for detecting natural pitting progression in spur gears. (15th August 2019)

• Record Type: Journal Article
• Authors: Kundu, Pradeep; Darpe, Ashish K.; Kulkarni, Makarand S.
• Is Part Of: Mechanical systems and signal processing. Volume 129(2019)
• Page Start: 741
• Page End: 763
• Publication Date: 2019-08-15
• Subjects: Correlation coefficient -- Gear -- Vibration -- Pitting; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.04.058
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A CCR parameter is introduced for monitoring the pitting progression in spur gears. An approach is given for selecting the best vibration sensor position. Performance of the proposed CCR parameter is compared with existing parameters for pitting fault detection and validated using five accelerated naturally pitting fault progression experiments. Abstract: This paper presents a correlation coefficient based parameter for detecting the natural progression of pitting fault in spur gear. The higher amplitudes of the shaft rotational frequency, gear mesh frequency and its harmonics dominates the sidebands appear due to the presence of pitting on the gear tooth surface. Hence, sidebands are filtered out to extract and uncover precise information related to the extent of pitting and the obtained residual vibration signal is used subsequently. The correlation of the gear residual vibration signal in healthy stage is continuously compared with the gear residual vibration signal obtained in the later stage as the time progresses. The parameter that quantifies this correlation is termed as the correlation coefficient of the residual vibration signal (CCR). The defect may have a different level of influence on the vibrations at different positions although the sensors are located close to the bearing location on the gearbox. Based on CCR parameter, a qualitative analysis based on the known health stage change position of the gear and a quantitative analysis based on the metrics such as robustness, trendability and monotonicity is carried out for selecting the best vibration sensor position for gear health monitoring. In contrast to the existing parameters for pitting fault detection and progression such as RMS, peak, crest factor, M6A, FM4, NA4, energy ratio and ALR, the proposed CCR parameter shows a significant variation with an increase in the pitting fault severity level.