Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Vibration Signal Analysis of Induction Motor Bearing Faults: Some Aspects(Institute of Electrical and Electronics Engineers Inc., 2023) Bhaumik, D.; Sadda, A.; Punekar, G.S.Vibration monitoring and analysis techniques are among the most commonly used methods in identifying defects in induction motors. Motor defects like bent shafts and bearing defects are analyzed, focusing on twice-line-frequency (100 Hz) components for the vibration data of an induction motor belonging to a petrochemical industry. The motor defect in this case was a bent shaft. A marginal correlation between the vibration data and the 100 Hz component could be seen. A similar study is attempted using another data set collected from web resources. The tracking twice-line-frequency data reveals progressive deterioration of the motor condition with time; this is in spite of the motor exhibiting vibrations within the acceptable limits as per ISO 10816-3. As the vibration signals are non-stationary, the second data set is analyzed using discrete wavelet transform (DWT). The sub-band D4 of DWT showed a definite correlation with the ball-bearing faults. © 2023 IEEE.Item Detection of Industrial Motor Fault using Signal Processing Algorithms(Institute of Electrical and Electronics Engineers Inc., 2024) Bhaumik, D.Induction motors are the most commonly used motor in petrochemicals, agrochemicals, pharmaceuticals, cement, steel, and many other process industries. A failure of any single component or sub-components of the induction motor can result in a plant shutdown. Hence, it is crucial to diagnose different types of faults in induction motors. The current study overviews a vibration-based condition monitoring method for detecting bent shaft failures in large industrial induction motors. This four-pole, 1.7-MW induction motor is primarily utilized in the petrochemical sector. A bent shaft is a typical problem with the motor's rotational portion. A bent shaft creates excessive vibration in a machine depending on the extent and position of the bend. This present study uses vibration data samples collected during the last four years. The data is collected from the deteriorating status of the induction motor. As the vibration signal of the corresponding data sets is non-stationary time-frequency domain analysis is mainly used. Several types of failures happened in various frequency sub-bands of an induction motor. As a result, the discrete wavelet transform is primarily utilized to detect defective frequency ranges. The objective of this study is to use discrete wavelet transform based vibration analysis to check if there is a trend in percentage energy and, if so, to identify the frequency range of the bent shaft issue of an induction motor. In discrete wavelet transform analysis, the detail sub-band-1 shows the deteriorating condition of an induction motor. The frequency range of detail sub-band-l is 1280 Hz to 2560 Hz. On the time-frequency plots, more precise detection of frequency range may be obtained. If the vibration is increased from the lowest to highest peak value in a time-frequency plot, the corresponding energy likewise attempts to locate compactly in discrete bits. © 2024 IEEE.Item Application of Wavelet Packet Transform for Detecting Bearing Issues(Springer Science and Business Media Deutschland GmbH, 2024) Bhaumik, D.; Bhaumik, D.Vibration condition monitoring analysis is one of the important methods for detecting defects in motors. The vibration data related to the outer race fault of an induction motor (IM) is collected from public domain. The main motive is to observe and characterize outer race defect. Different wavelet transform is mainly used. Absolute energy of the defective condition is compared with the normal state of the motor by the topology of discrete wavelet-transform (DWT). The proper nodes in wavelet packet transform (WPT) can be determined by the best basis selection (BBS) method. The absolute energy of selected nodes is further compared with the normal condition for detecting fault. The sub-band or the node corresponding to the DWT and the WPT analysis is mainly utilized for identifying the proper frequency components in characterizing the defect severity. In the case of DWT-based analysis, the sub-band D4 (1.5 kHz to 3 kHz) showed a positive energy trend with an increase in defect severity. In WPT, nodes 33 (1.5 kHz to 2.25 kHz) and 34 (2.25 kHz to 3 kHz) showed a positive trend with increased defect severity. Although the change in energy in node 33 is more dominant than in node 34, it can be said that the frequency range corresponding to node 33 (1.5 kHz to 2.25 kHz) can characterize the outer race defect severities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.