02333ntm a2200205 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100003000109245011400139260003400253300005700287502009200344504002500436520159300461650004502054650002802099vtls000077780KUKTEM20251114204602.0140425t2012    my da  f m    000 0 eng d  aTHE0006479(Local)  aUMP0 aMohd Qayyuum Mohd Ridzuan10aBearing fault detection using vibration responds :ba statistical based analysis /cMohd Qayyuum Mohd Ridzuan  aKuantan, Pahang :bUMP,c2012  axix, 98 p. :bill. (some col.) ;c30 cm. +e1 CD-ROM  aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2012  aBibliography : 71-733 aBearing is one of the vital parts in any rotating machinery. Failure of this particular part can affect the machinery performance and in time will cause major failure to the machinery. Due to this crucial problem, on-line monitoring has become an alternative in prevention maintenance. The objective of this project is to analyze the vibration signal of both good bearing and defect bearing gain from an accelerometer. A set of good condition bearing is used along five types of defective bearing which  are outer race defect, inner race defect, corroded defect, contaminated defect and lastly point defect. The data is acquired using a Bruel & Kjaer accelerometer and a National Instrument Data Acquisition System (NiDAQ). The bearing will be run on three speed rotation which is 287, 1466 and 2664 RPM. The data is then analyzed using DASYLab software. From there it is further analyzed using global statistical parameters of Variance, Standard Deviation, Kurtosis, Skewness, Root Mean Square and also Crest Factor. The data then plotted on statistical distribution to obtain a  trend where, the value of this trend is used in Principal Component Analysis (PCA) to form a scatter plot of the data. Where all types of bearing will remain in their scatter area itself. Then it will be clustered using Agglomerative Hierarchical Clustering where a dendrogram is used to show a cluster of data in which the respective data for all types of bearing tested remain in their cluster. Finally, this method is suggested as an alternative in bearing fault detection, especially online  monitoring. 0aBearings (Machinery)xVibrationxTesting 0aMachine partsxFailures