Clustering of frequency-based vibration signal for bearing fault detection

Chia, Ming Xuan (2013) Clustering of frequency-based vibration signal for bearing fault detection. Faculty of Mechanical Engineering, Universiti Malaysia Pahang.

14.Clustering of frequency-based vibration signal for bearing fault detection.pdf - Accepted Version

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Bearing 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 study the trend of frequency spectrum from different bearing defects and to apply clustering approach using Principle Component Analysis, PCA on frequency domain signals. A set of good condition bearing is used along with four types of defective bearing which are inner race defect,corroded defect, contaminated defect and lastly roller defect. The signals are acquired using a PCB piezoelectric accelerometer and a National Instrument Data Acquisition System (NI-DAQ). The bearing will be run on three speed rotation which is 440, 1480 and 2672 RPM. The data is acquired by using DASYLab software, for both the time domain and frequency domain signals. The data then analyzed using PCA method through MATLAB software. Data is then plotted on scatter plot. After that, the data 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.

Item Type: Undergraduates Project Papers
Additional Information: Project paper (Bachelor of Mechanical Engineering) -- Universiti Malaysia Pahang - 2013, SV: MR. MOHD FADHLAN BIN MOHD YUSOF, CD NO. 8197
Uncontrolled Keywords: Bearings (Machinery); Vibration Testing; Machine parts Failures
Subjects: T Technology > TJ Mechanical engineering and machinery
Faculty/Division: Faculty of Mechanical Engineering
Depositing User: Mr. Nik Ahmad Nasyrun Nik Abd Malik
Date Deposited: 16 Nov 2015 08:11
Last Modified: 18 Jul 2023 04:00
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