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Review of Vibration Monitoring Techniques Using Low Cost Sensors and Microcontrollers

Abhishek D Patange, Sharad S Mulik, Suhas P. Deshmukh, Mahesh S. Shewale

Abstract


Healthy condition of machineries sustains and preserves the economy of the industry. Many of the products are manufactured by continual machining processes. Unforeseen failures are unbearable for industries which lead to costly repairs. Efficient supervision for condition monitoring and quick detection of the mechanical faults is a critical assignment in the current business era. Condition monitoring is the practice used to supervise the state of equipment/machineries, so, as to spot the noteworthy change which might be cause of a rising faults or failures. It is a key aspect of prognostic maintenance and averts unexpected failure and costly repair. Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure. Several types of analyzers are commercially available and used for condition monitoring of a machine but the overall cost is very high and may not be affordable to all. Hence it necessitates reviewing vibration monitoring techniques which are inexpensive. This paper presents review of techniques used for vibration monitoring with micro-electro-mechanical sensors and Arduino microcontrollers

Keywords


Condition monitoring, vibration analysis, arduino controller, micro-electro-mechanical sensors

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References


Arturo Garcia-Perez, Gyakis Konstantinos N. Vibration Analysis as a Diagnosis Tool for Online Health Monitoring of Industrial Machines. International Journal of Shock and Vibrations, Procedia Eng. 2015; 122: 215–195p.

Michelle Thompson, Mark McCormick. Monitoring Machine Health. midstreambusiness.com, Jan 2013.

Young Jonathan C. Vibration Analysis using a MEMS Accelerometer. Thesis Submitted for the Degree of MS in Electrical Engg, Naval, Postgraduate School, Monterey, California. Dec 2006.

Rao Singiresu S. Mechanical Vibrations. 5th Edn. Prentice Hall Publication; Sep 2010.

Wanjun Huang, Dr. Duncan L. MacFarlane. Fast Fourier Transform and MATLAB Implementation.

Kevin Craig. Chair in Engineering Design & Professor of Mechanical Engineering Marquette University, A Note on Time and Frequency Domain.

Application Note 1405-1. Fundamentals of Signal Analysis Series Introduction to Time, Frequency and Modal Domains. USA: Agilent Technologies; May 24, 2002.

Chu F, Peng Z, Feng Z, et al. Modern Signal Processing Methods in Machinery Fault Diagnosis. Beijing: Science Press; 2009. (In Chinese).

Zhipeng Feng, Ming Liang, Fulei Chu. Recent Advances in Time–Frequency Analysis Methods for Machinery Fault Diagnosis: A Review with Application Examples. Mech Syst Signal Process. Jul 2013. DOI: 10.1016/j.ymssp.2013.01.017

Mark Looney. An Introduction to MEMS Vibration Monitoring. Analog Dialogue. Jun 2014; 48–06p.

Hunter Matthew T, Kourtellis Achilleas G, Ziomek Christopher D, et al. Fundamentals of Modern Spectral Analysis. IEEE 2010

Guoliang Lu, Yi qi Zhou, Changhou Lu, et al. A Novel Framework of Change-Point Detection for Machine Monitoring. Mech Syst Signal Process. 2011; 26: 2961–5453p.

Vikas Sharma, Anand Parey. Frequency Domain Averaging Based Experimental Evaluation of Gear Fault without Tachometer for Fluctuating Speed Conditions. Mech Syst Signal Process. 2017; 85: 278–295p.

Suratsavadee Korkua, Himanshu Jain, Wei-Jen Lee, et al. Wireless Health Monitoring System for Vibration Detection of Induction Motors. IEEE; 2010.

Samer Gowid, Roger Dixon, Saud Ghani. A Novel Robust Automated FFT-Based Segmentation and Features Selection Algorithm for Acoustic Emission Condition Based Monitoring Systems. Appl Acoust. 2015; 88: 66–74p.

Subimal Bikash Chaudhury, Mainak Sengupta, Kaushik Mukherjee. Vibration Monitoring of Rotating Machines Using MEMS Accelerometer. International Journal of Scientific Engineering and Research (IJSER). Sep 2014; 2(9). ISSN (Online): 2347-3878.

Adam Hjort, Mans Holmberg. Measuring Mechanical Vibrations Using an Arduino as a Slave I/O to an EPICS Control System. Department of Physics and Astronomy, Uppsala University; FREIA Report 2015/04 Jun 9, 2015.

Alessandro D’Ausilio, Arduino. A Low-Cost Multipurpose Lab Equipment. Behav Res. 2012; 44: 305–313p.

Madhavendra Saxena, Olvin Oliver Bannet, Manoj Gupta, et al. Bearing Fault Monitoring Using CWT Based Vibration Signature. 12th International Conference on Vibration Problems, ICOVP 2015, Procedia Eng. 2016; 144: 234–241p.

Somchai Biansoongnern, Boonyang Plungkang, Sriwichai Susuk. Development of Low Cost Vibration Sensor Network for Early Warning System of Landslides. Energy Procedia. 2016; 89: 417–420p.

Parikh Priyam A, Shah Hitesh B, Saurin Sheth. Development of a Multi-Channel Wireless Data Acquisition System for Swarm Robots. International Journal of Engineering Development and Research (IJEDR). 2014.

Puneet Bansal, Rajay Vedaraj IS. Monitoring and Analysis of Vibration Signal in Machine Tool Structures. International Journal of Engineering Development and Research (IJEDR). 2014; 2(2). ISSN: 2321-9939.

Patel Viral K, Patel Maitri N. Development of Smart Sensing Unit for Vibration Measurement by Embedding Accelerometer with the Arduino Microcontroller. International Journal of Instrumentation Science. 2017.

Emerson Galdino, Alexandre Cury. Development of Low-Cost Wireless Accelerometer for Structural Dynamic Monitoring. Proceedings of the XXXVII Iberian Latin-American Congress on Computational Methods in Engineering. Nov 6–9, 2016.

Theanh Nguyen, Chan Tommy HT, Thambiratnam David P, et al. Development of a Cost-Effective and Flexible Vibration DAQ System for Long-Term Continuous Structural Health Monitoring. Mech Syst Signal Process. 2015; 64–65: 313–324p.

Ozkan Celik, Sergey Dusheyko, Nick Patrick. A Low-Cost Dynamic Plant and Data Acquisition System for Laboratory Courses on Control Systems and Mechatronics. 120th ASEE Annual Conference and Exposition, American Society for Engineering Education. 2013.

Jaroslav Sobota, Roman Pisl, Pavel Balda, et al. Raspberry Pi and Arduino Boards in Control Education. The International Federation of Automatic Control (IFAC). 2013




DOI: https://doi.org/10.37591/joma.v4i2.7232

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