MARC View

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001			vtls000052257
003			KUKTEM
005			20251114204444.0
008			110324t2010 my a f m 000 0 eng d
020			_aTHE0006478(Local)
039		9	_a201905141206 _bamirul _c201107140040 _dVLOAD _c201103241239 _dida _y201103241237 _zida
040			_aUMP
090			_aTJ1071 .H57 2010 rs Bc.
100	0		_aMohd Nor Hisyam Che Ab Aziz
245	1	0	_aFault detection for rotating machine using time frequency localization method / _cMohd Nor Hisyam Che Ab Aziz
246	3		_aFault detection for rotating machine using time frequency localization method _h[electronic resource]
260			_aKuantan, Pahang : _bUMP, _c2010
300			_axvii, 84 p. : _bill. (some col.) ; _c30 cm.+ _e1 computer disc
502			_aProject paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2010
504			_aBibliography : p. 75-77
520	3		_aBearing is one of the important things in machining. Bearing are considered as critical mechanical components and a defect in a bearing causes malfunction to machine. Failed machines can lead to economic loss and safety problems due to unexpected and sudden production stoppages. These machines need to be monitored during the production process. Because of that, on-line condition monitoring become alternatives to solve this problem compared with off-line monitoring. Objective of this project is to analyze data acquired from testing of fault detection to differentiate between the defective bearings and good bearings using an accelerometer. A set of good bearing and defective bearing with different failure was using in this experiment. Four units of bearing which is one is good bearing, one corroded bearing, one sandy bearing, and one bearing with damage at the ball was used in this experiment. The data were obtained from experiment on test rig using Bruel & Kjaer accelerometer and data acquisition system. All the bearings were run with different speed which is 4000rpm, 7000rpm, and 10000rpm. The data were analyzed using PULSE LabShop software. The data from three rotations for each bearing was analyzed using time domain, frequency domain, and time-frequency domain analysis. The time-frequency domain method used in this experiment is Short-time Fourier Transform (STFT), and S-transform. STFT and S-transform are applied to detect the location of the signal that has high vibration. The highly damaged bearing is detected based on the high magnitude distribution value in the obtained time-frequency domain. Based on the result, it has a different in vibration between all the bearings. The data for a good bearing were used as benchmark to compare with the defective bearing. For a good bearing, higher vibrations occur at low frequency which is below than 5 kHz using a STFT and below 5μHz when using S-transform. For the defective bearings, the higher vibrations occur at high frequency which is above than 5 kHz when using a STFT analysis and above 5μHz when using S-transform. From the graph, the different between good bearing and defective bearings can be made. The findings indicate that time frequency localization transform method can be used to develop an effective condition monitoring tool. The use of signal processing analysis in this study can be used in industrial applications. This signal processing analysis is recommended to use in on-line monitoring of parameters while the machine is producing.
650		0	_aBearings (Machinery) _xVibration _xTesting
650		0	_aMachine parts _xFailures
999			_aVIRTUA40 _c2332 _d2338
999			_aVTLSSORT008002000400090010002450246026003000502050405200650065019992