NDLI: Diagnosis and prognosis of bearings using data mining and numerical visualization techniques

Content Provider	IEEE Xplore Digital Library
Author	Blair, J. Shirkhodaie, A.
Copyright Year	2001
Description	Author affiliation: Dept. of Mech. Eng., Tennessee State Univ., Nashville, TN, USA (Blair, J.)
Abstract	Traditionally, condition-based monitoring techniques have been used to diagnose failure in rotary machinery by application of low-level signal processing and trend analysis techniques. Such techniques consider small windows of data from large data sets to give preliminary information of developing fault(s) or failure precursor(s). However, these techniques only provide information of a minute portion of a large data set, which limits the accuracy of predicting the remaining useful life of the system. Diagnosis and prognosis (DAP) techniques should be able to identify the origin of the fault(s), estimate the rate of its progression and determine the remaining useful life of the system. This research demonstrates the use of data mining and numerical visualization techniques for diagnosis and prognosis of bearing vibration data. By using these techniques a comprehensive understanding of large vibration data sets can be attained. This approach uses intelligent agents to isolate particular bearing vibration characteristics using statistical analysis and signal processing for data compression. The results of the compressed data can be visualized in 3-D plots and used to track the origination and evolution of failure in the bearing vibration data. The Bearing Test Bed is used for applying measurable static and dynamic stresses on the bearing and collecting vibration signatures from the stressed bearings.
Sponsorship	Russ College of Eng. & Technol.
Starting Page	395
Ending Page	399
File Size	527449
Page Count	5
File Format	PDF
ISBN	0780366611
DOI	10.1109/SSST.2001.918553
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-03-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Signal processing Fault diagnosis Data visualization Vibration measurement Condition monitoring Machinery Failure analysis Signal analysis Accuracy
Content Type	Text
Resource Type	Article

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