Abstract
The defective bearing in a rotating machine may affect its performance and hence reduce its efficiency. So the monitoring of bearing health and its fault diagnosis is essential. A vibration signature is one of the measuring parameters for fault detection. However, this vibration signature may get corrupted with noise. As a result this noise must be removed from the actual vibration signature before its analysis to detect and diagnose the fault. ANC (adaptive noise control)-based filtering techniques are used for this noise removal and hence to improve the SNR (signal-to-noise ratio). In our study an experimental setup is developed and then the proposed work is executed in three stages. In the first stage the vibration signatures are acquired and then ANC is implemented to remove the background noise. In the second stage the time (statistical) and the frequency analysis of the filtered vibration signals are done to detect the fault. In the third stage the statistical parameters of the vibration signatures are used for the classification of the fault present in the bearing using random forest and J48 classifiers.
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References
Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11:2300–2312
Li W, Qiu M, Zhu Z, Wu B, Zhou G (2016) Bearing fault diagnosis based on spectrum images of vibration signals. Meas Sci Technol 27:035005 (10Â pp)
Shakya P, Darpe AK, Kulkarni MS (2013) Vibration-based fault diagnosis in rolling element bearings: ranking of various time, frequency and time-frequency domain data-based damage identification parameters. Int J Cond Monit 3
Li B, Zhang P, Wang Z, Mi S, Zhang Y (2011) Gear fault detection using multi-scale morphological filters. Measurement 44:2078–20895
Albarbar A, Gu F, Ball AD, Starr A (2009) Acoustic monitoring of engine fuel injection based on adaptive filtering techniques. Appl Acoust 70:247–255
Liao CW, Lin JY (2007) New FIR filter-based adaptive algorithms incorporating with commutation error to improve active noise control performance. Automatica 43:325–331
Troparevsky MI, D’Attellis CE (2004) On the convergence of the LMS algorithm in adaptive filtering. Signal Process 84:1985–1988
Cho YW, Kim JM, Park YY (2016) Design and implementation of marine elevator safety monitoring system based on machine learning. Indian J Sci Technol 9:109889
Abraham Siju K, Sugumaran V, Amarnath M (2016) Acoustic signal based condition monitoring of gearbox using wavelets and decision tree classifier. Indian J Sci Technol 9:101335
Kankar PK, Sharma Satish C, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38:1876–1886
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Sahoo, S., Das, J.K. (2019). Bearing Fault Detection and Classification Using ANC-Based Filtered Vibration Signal. In: Kumar, A., Mozar, S. (eds) ICCCE 2018. ICCCE 2018. Lecture Notes in Electrical Engineering, vol 500. Springer, Singapore. https://doi.org/10.1007/978-981-13-0212-1_34
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DOI: https://doi.org/10.1007/978-981-13-0212-1_34
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