Vibration-based fault diagnosis in rolling element bearings: ranking of various time, frequency and time-frequency domain data-based damage identi cation parameters
01 Oct 2013-Vol. 3, Iss: 2, pp 53-62
TL;DR: Shakya et al. as discussed by the authors performed a comparative study of various vibration signal-based damage identification parameters for rolling element bearings and concluded that the results suggest that the ranking is quite consistent, even with a different bearing type and damage characteristic.
Abstract: Piyush Shakya, Ashish K Darpe and Makarand S Kulkarni are with the Vibration Research Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi – 110016, India. A comparative study of various vibration signal-based damage identification parameters for rolling element bearings is undertaken. Defects of varying severity are seeded on the outer and inner races of a double-row angular contact bearing. The influence of a defect and its severity on the observed identification parameters is investigated using vibration data acquired from the bearing housing. A comparison among the various time domain, frequency domain and time-frequency domain parameters is made based on their robustness, sensitivity to damage change and early detectivity of the bearing faults. An overall ranking of the parameters is attempted with the objective of ascertaining effective damage identification parameters from among those available for diagnosis of the rolling element bearings. Validation of the ranking is carried out with the data obtained on a different test-rig for early detectivity of damage. The results suggest that the ranking is quite consistent, even with a different bearing type and damage characteristic.
Citations
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TL;DR: In this article, the authors provide a review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis.
Abstract: Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.
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Cites background from "Vibration-based fault diagnosis in ..."
...VA is the most common CM technology used in the industry for any kind of rotating equipment and it is an effective tool for the bearing fault diagnosis [14,32,33]....
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...Therefore, it is a versatile and complete tool to extract and detect useful information caused by most possible faults, wear and different types of damage either localized or distributed [26,33]....
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