single-au.php

IJAT Vol.16 No.3 pp. 329-339
doi: 10.20965/ijat.2022.p0329
(2022)

Paper:

Performance Evaluation of Low-Cost Vibration Sensors in Industrial IoT Applications

Ali Iqbal, Naeem S. Mian, Andrew Longstaff, and Simon Fletcher

Centre for Precision Technologies, School of Computing and Engineering, University of Huddersfield
Queensgate, Huddersfield, West Yorkshire HD1 3DH, United Kingdom

Corresponding author

Received:
January 30, 2021
Accepted:
November 4, 2021
Published:
May 5, 2022
Keywords:
Industrial Internet of Things, precision manufacturing, measuring instruments, calibration, Industry 4.0
Abstract

The recent development of low-cost accelerometers, driven by the Industrial Internet of Things (IIoT) revolution, provides an opportunity for their application in precision manufacturing. Sensor data is often of the highest consideration in any precision machining process. While high-cost vibration sensors have traditionally been employed for vibration measurements in industrial manufacturing, the measurement uncertainty affecting the accuracy of low-cost vibration sensors has not been explored and requires performance evaluation. This research focuses on the characterization of measurements from low-cost tri-axial micro electro-mechanical systems (MEMS) accelerometers in terms of identifying the parameters that induce uncertainties in measured data. Static and dynamic calibration was conducted on a calibration test bench using a range of frequencies while establishing traceability according to the ISO 16063 series and the IEEE-STD-1293-2018 standards. Moreover, comparison tests were performed by installing the sensors on machine tools for reliability evaluation in terms of digital transmission of recorded data. Both tests further established the relationship between the baseline errors originating from the sensors and their influence on the data obtained during the dynamic performance profile of the machine tools. The outcomes of this research will foresee the viability offered by such low-cost sensors in metrological applications enabling Industry 4.0.

Cite this article as:
A. Iqbal, N. Mian, A. Longstaff, and S. Fletcher, “Performance Evaluation of Low-Cost Vibration Sensors in Industrial IoT Applications,” Int. J. Automation Technol., Vol.16, No.3, pp. 329-339, 2022.
Data files:
References
  1. [1] M. Galetto, A. Schiavi, G. Genta, A. Prato, and F. Mazzoleni, “Uncertainty evaluation in calibration of low-cost digital MEMS accelerometers for advanced manufacturing applications,” CIRP Annals, Vol.68, Issue 1, pp. 535-538, 2019.
  2. [2] K. Szipka, A. Archenti, G. W. Vogl, and M. A. Donmez, “Identification of machine tool squareness errors via inertial measurements,” CIRP Annals, Vol.68, No.1, pp. 547-550, 2019.
  3. [3] A. Albarbar, S. Mekid, A. Starr, and R. Pietruszkiewicz, “Suitability of MEMS Accelerometers for Condition Monitoring: An experimental study,” Sensors, Vol.8, No.2, pp. 784-799, 2008.
  4. [4] C. Murphy, “Choosing the Most Suitable Predictive Maintenance Sensor,” 2020.
  5. [5] C. Mongia, D. Goyal, and S. Sehgal, “Vibration response-based condition monitoring and fault diagnosis of rotary machinery,” Materials Today: Proc., 2021.
  6. [6] D. Gupta, V. H. C. de Albuquerque, A. Khanna, and P. L. Mehta, “Smart Sensors for Industrial Internet of Things,” Springer, 2021.
  7. [7] D. Goyal, S. S. Dhami, and B. S. Pabla, “Vibration Response-Based Intelligent Non-Contact Fault Diagnosis of Bearings,” J. of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems, Vol.4, No.2, 021006, 2021.
  8. [8] J. R. Evans et al., “Performance of Several Low-Cost Accelerometers,” Seismological Research Letters, Vol.85, No.1, pp. 147-158, 2014.
  9. [9] D. Giagopoulos, D. C. Papadioti, C. Papadimitriou, and S. Natsiavas, “Bayesian Uncertainty Quantification and Propagation in Nonlinear Structural Dynamics,” 2009.
  10. [10] E. Ahmed et al., “The role of big data analytics in Internet of Things,” Computer Networks, Vol.129, No.P2, pp. 459-471, 2017.
  11. [11] A. Gilchrist, “Industry 4.0: The Industrial Internet of Things,” Apress, 2016.
  12. [12] F. Tao, Q. Qi, A. Liu, and A. Kusiak, “Data-driven smart manufacturing,” J. of Manufacturing Systems, Vol.48, pp. 157-169, 2018.
  13. [13] J. Chen et al., “Toward Intelligent Machine Tool,” Engineering, Vol.5, Issue 4, pp. 679-690, 2019.
  14. [14] D. G. Pascual, P. Daponte, and U. Kumar, “Handbook of Industry 4.0 and Smart Systems,” CRC Press, 2019.
  15. [15] R. Ajith, A. Tewari, D. Gupta, and S. Tallur, “Low-Cost Vibration Sensor for Condition-Based Monitoring Manufactured From Polyurethane Foam,” IEEE Sensors Letters, Vol.1, pp. 1-4, 2017.
  16. [16] S. Abba, J. W. Namkusong, J. A. Lee, and M. Liz Crespo, “Design and Performance Evaluation of a Low-Cost Autonomous Sensor Interface for a Smart IoT-Based Irrigation Monitoring and Control System,” Sensors, Vol.19, No.17, 3643, 2019.
  17. [17] A. Villarroel, G. Zurita, and R. Velarde, “Development of a Low-Cost Vibration Measurement System for Industrial Applications,” Machines, Vol.7, No.1, 12, 2019.
  18. [18] A. Sabato, C. Niezrecki, and G. Fortino, “Wireless MEMS-Based Accelerometer Sensor Boards for Structural Vibration Monitoring: A Review,” IEEE Sensors J., Vol.17, No.2, pp. 226-235, 2017.
  19. [19] J. Fu, Z. Li, H. Meng, J. Wang, and X. Shan, “Performance evaluation of low-cost seismic sensors for dense earthquake early warning: 2018–2019 field testing in southwest China,” Sensors, Vol.19, No.9, 1999, 2019.
  20. [20] G. D’Emilia, A. Gaspari, and E. Natale, “Evaluation of aspects affecting measurement of three-axis accelerometers,” Measurement, Vol.77, pp. 95-104, 2016.
  21. [21] G. D’Emilia and E. Natale, “Use of MEMS sensors for condition monitoring of devices: discussion about the accuracy of features for diagnosis,” Int. J. of Metrology and Quality Engineering, Vol.12, 15, 2021.
  22. [22] BS ISO 16063-11:1999, “Methods for the calibration of vibration and shock transducers. Primary vibration calibration by laser interferometry,” 0580385841;9780580385841;, B. ISO, 2001.
  23. [23] BS ISO 16063-21:2003+A1:2016, “Methods for the calibration of vibration and shock transducers. Vibration calibration by comparison to a reference transducer,” 0580676935;9780580676932;, B. ISO, 2003.
  24. [24] IEEE STD 1293-2018 (Revision of IEEE STD 1293-1998): IEEE Standard Specification Format Guide and Test Procedure for Linear Single-Axis, Nongyroscopic Accelerometers, IEEE, 2019.
  25. [25] B. I. UK, “Digital manufacturing – Trustworthiness and precision of networked sensors – Guide,” BSI, 2019.
  26. [26] G. D’Emilia, A. Gaspari, F. Mazzoleni, E. Natale, and A. Schiavi, “Calibration of tri-axial MEMS accelerometers in the low-frequency range – Part 1: Comparison among methods,” J. of Sensors and Sensor Systems, Vol.7, No.1, pp. 245-257, 2018.
  27. [27] N. Garg, O. Sharma, A. Kumar, and M. I. Schiefer, “A novel approach for realization of primary vibration calibration standard by homodyne laser interferometer in frequency range of 0.1 Hz to 20 kHz,” Measurement, Vol.45, No.8, pp. 1941-1950, 2012.
  28. [28] A. Albarbar and S. Teay, “MEMS Accelerometers: Testing and Practical Approach for Smart Sensing and Machinery Diagnostics,” Advanced Mechatronics and MEMS Devices II. Microsystems and Nanosystems, pp. 19-40, 2017.
  29. [29] BS ISO 21748:2017, “Guidance for the use of repeatability, reproducibility and trueness estimates in measurement uncertainty evaluation,” British Standards Institute, 2017.
  30. [30] Analog Devices, “ADXL354/ADXL355 Low Noise, Low Drift, Low Power, 3-Axis MEMS Acceleromters Data Sheet (Rev. A),” 2016.
  31. [31] P. Piezotronics, “PCB 356A02 Tri-axial Accelerometer Data Sheet.” https://www.pcb.com/products?m=353B03 [Accessed October 9, 2020]
  32. [32] Renishaw, “XL-80 Laser Measurement system.” https://www.pcb.com/products?m=353B03 [Accessed October 9, 2020]
  33. [33] D. S. B. P. Resolution, “DS18B20 1-Wire Digital Thermometer, Data Sheet, Maxim Integrated Products.” https://www.maximintegrated.com/en/products/sensors/DS18B20.html [Accessed December 29, 2019]
  34. [34] D. Goyal, Vanraj, B. S. Pabla, and S. S. Dhami, “Non-contact sensor placement strategy for condition monitoring of rotating machine-elements,” Engineering Science and Technology, an Int. J., Vol.22, No.2, pp. 489-501, 2019.
  35. [35] BS ISO 266:1997, “Acoustics – Preferred Frequencies, 0 580 28782 3,” B. ISO, 1997.
  36. [36] “Evaluation of measurement data – guide to the expression of uncertainty in measurement,” JCGM 100: 2008 GUM 1995 with minor corrections, I. BIPM, I. IFCC, I. ISO, and O. IUPAP, JCGM, 2008. https://www.bipm.org/utils/common/documents/jcgm/JCGM_100_2008_E.pdf [Accessed June 4, 2020]

Creative Commons License  This article is published under a Creative Commons Attribution 4.0 International License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 04, 2022