INDUCED DRAFT FAN DOMINANT FREQUENCY DETECTION USING SHORT-TIME FOURIER TRANSFORM METHOD
DOI:
https://doi.org/10.21776/jrm.v14i2.1305Keywords:
Vibration Analysis, Induced Draft Fan, Analysis Vibration, STFT, FFTAbstract
Weak suction and large vibrations indicate an Induced Draft Fan (IDF) problem. The Fast Fourier Transform (FFT) method cannot be applied to non-stationary vibration signals. Therefore, this study aims to analyze non-stationary vibration signals for IDF vibration signals at start-up so that the source of damage to the IDF can be found. The research process begins with a brief measurement of both bearing locations with horizontal and axial axes. Processing of the vibration signal from the measurement using the FFT method and the Short Time Fourier Transform (STFT). Based on the STFT spectrogram graph for measurements on the horizontal and axial axes, the dominant frequency values are the same. The frequency with the largest amplitude value is at one RPM IDF or 25 Hz. High vibration at 1 RPM is a big indication that the IDF is experiencing unbalance.
References
F. Anggara, D. Romahadi, A. L. Avicenna, and Y. H. Irawan, “Numerical analysis of the vortex flow effect on the thermal-hydraulic performance of spray dryer,” SINERGI, vol. 26, no. 1, pp. 23–30, Feb. 2022, doi: 10.22441/SINERGI.2022.1.004.
C. S. A. Gong Et Al., “Design and implementation of acoustic sensing system for online early fault detection in industrial fans,” J. Sensors, vol. 2018, 2018, doi: 10.1155/2018/4105208.
N. Dileep, K. Anusha, C. Satyaprathik, B. Kartheek, and K. R. A. Proffesor, “Condition Monitoring of FD-FAN Using Vibration Analysis,” Int. J. Emerg. Technol. Adv. Eng., vol. 3, no. 1, pp. 170–186, 2013.
S. Patidar and P. K. Soni, “An Overview on Vibration Analysis Techniques for the Diagnosis of Rolling Element Bearing Faults,” Int. J. Eng. Trends Technol., vol. 4, no. 5, pp. 1804–1809, 2013.
D. Romahadi, A. A. Luthfie, and L. B. D. Dorion, “Detecting classifier-coal mill damage using a signal vibration analysis,” SINERGI, vol. 23, no. 3, pp. 175–183, Sep. 2019, doi: 10.22441/SINERGI.2019.3.001.
D. Romahadi, F. Anggara, A. F. Sudarma, and H. Xiong, “The implementation of artificial neural networks in designing intelligent diagnosis systems for centrifugal machines using vibration signal,” SINERGI, vol. 25, no. November 2020, 2021, doi: 10.22441/sinergi.2021.1.012.
D. Romahadi, A. A. Luthfie, W. Suprihatiningsih, and H. Xiong, “Designing expert system for centrifugal using vibration signal and Bayesian Networks,” Int. J. Adv. Sci. Eng. Inf. Technol., vol. 12, no. 1, p. 23, Jan. 2022, doi: 10.18517/IJASEIT.12.1.12448.
D. Romahadi, H. Xiong, and H. Pranoto, “Intelligent system for gearbox fault detection & diagnosis based on vibration analysis using Bayesian Networks,” IOP Conf. Ser. Mater. Sci. Eng., vol. 694, no. 1, 2019, doi: 10.1088/1757-899X/694/1/012001.
G. Manhertz and A. Bereczky, “STFT spectrogram based hybrid evaluation method for rotating machine transient vibration analysis,” Mech. Syst. Signal Process., vol. 154, p. 107583, Jun. 2021, doi: 10.1016/J.YMSSP.2020.107583.
D. Romahadi, D. Feriyanto, W. Suprihatiningsih, and W. N. Setiawan, “Perancangan sistem diagnosis getaran motor menggunakan jaringan saraf tiruan propagasi mundur,” J. Rekayasa Mesin, vol. 13, no. 1, pp. 37–46, Jun. 2022, doi: 10.21776/UB.JRM.2022.013.01.5.
B. Hu and B. Li, “Blade crack detection of centrifugal fan using adaptive stochastic resonance,” Shock Vib., vol. 2015, 2015, doi: 10.1155/2015/954932.
Z. Gao, S. Member, C. Cecati, F. Ieee, and S. X. Ding, “IEEE Transactions on Industrial Electronics A Survey of Fault Diagnosis and Fault - Tolerant Techniques Part I : Fault Diagnosis with Model - Based and Signal - Based Approaches,” vol. 62, pp. 3757–3767, 2015.
K. Mollazade, H. Ahmadi, M. Omid, and R. Alimardani, “Vibration-Based Fault Diagnosis of Hydraulic Pump of Tractor Steering System by Using Energy Technique,” Mod. Appl. Sci., vol. 3, no. 6, 2009, doi: 10.5539/mas.v3n6p59.
C. Mateo and J. A. Talavera, “Short-Time Fourier Transform with the Window Size Fixed in the Frequency Domain (STFT-FD): Implementation,” SoftwareX, vol. 8, pp. 5–8, 2018, doi: 10.1016/j.softx.2017.11.005.
R. Dutta, J. P. Dwivedi, V. P. Singh, and A. Ghosh, “Using Vibration Analysis to Identify & Correct an Induced Draft Fan ’ s Foundation Problem of a Pollution Control Device - A Case Study,” Int. J. Appl. Eng. Res., vol. 13, no. 8, pp. 5831–5840, 2018.
S. R. Vippala, S. Bhat, and A. A. Reddy, “Condition monitoring of BLDC motor using short time fourier transform,” 2021 IEEE 2nd Int. Conf. Control. Meas. Instrumentation, C. 2021 - Proc., no. Cmi, pp. 110–115, 2021, doi: 10.1109/CMI50323.2021.9362938.
C. T. Alexakos, Y. L. Karnavas, M. Drakaki, and I. A. Tziafettas, “A Combined Short Time Fourier Transform and Image Classification Transformer Model for Rolling Element Bearings Fault Diagnosis in Electric Motors,” Mach. Learn. Knowl. Extr., vol. 3, no. 1, pp. 228–242, 2021, doi: 10.3390/make3010011.
D. Mokrzan and G. Szymański, “Time-frequency methods of non-stationary vibroacoustic diagnostic signals processing,” Rail Veh., no. 3, pp. 44–57, 2021, doi: 10.53502/rail-143047.
L. Sandrolini and A. Mariscotti, “Impact of short-time fourier transform parameters on the accuracy of EMI spectra estimates in the 2-150 kHz supraharmonic interval,” Electr. Power Syst. Res., vol. 195, no. July 2020, 2021, doi: 10.1016/j.epsr.2021.107130.
V. Dekys, P. Kalman, P. Hanak, P. Novak, and Z. Stankovicova, “Determination of Vibration Sources by Using STFT,” Procedia Eng., vol. 177, pp. 496–501, Jan. 2017, doi: 10.1016/J.PROENG.2017.02.251.
F. Jurado and J. R. Saenz, “Comparison between discrete STFT and wavelets for the analysis of power quality events,” Electr. Power Syst. Res., vol. 62, no. 3, pp. 183–190, Jul. 2002, doi: 10.1016/S0378-7796(02)00035-4.
W. Jiang, X. Wu, Y. Wang, B. Chen, W. Feng, and Y. Jin, “Time-frequency-analysis-based blind modulation classification for multiple-antenna systems,” Sensors (Switzerland), vol. 21, no. 1, pp. 1–19, 2021, doi: 10.3390/s21010231.
A. Jablonski and K. Dziedziech, “Intelligent spectrogram – A tool for analysis of complex non-stationary signals,” Mech. Syst. Signal Process., vol. 167, p. 108554, Mar. 2022, doi: 10.1016/J.YMSSP.2021.108554.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Dedik Romahadi
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.