Abstract
‘Testing Technology and Data Processing (TTDP)’ is one of the core courses for the undergraduates in mechanical engineering subject. This paper designs an experimental case to improve the students’ abilities in signal acquisition, preprocessing, feature extraction, and artificial intelligence (AI)-based pattern recognition. The case study is based on an internet of things (IoT) node that integrating with accelerometer, microphone, and magnetic sensors. The order tracking algorithm and a double-layer bidirectional long short-term memory (DBiLSTM) model are used to process the multi-sensor data for condition monitoring and fault diagnosis of a motor. The students’ feedback demonstrates that the designed case improves their interests to this course, and also improves their abilities in engineering practice.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xia, M., Shao, H., Williams, D., Lu, S., Shu, L., de Silva, C.W.: Intelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning. Reliab. Eng. Syst. Saf. 215, 107938 (2021)
Wang, X., Lu, S., Huang, W., Wang, Q., Zhang, S., Xia, M.: Efficient data reduction at the edge of industrial Internet of Things for PMSM bearing fault diagnosis. IEEE Trans. Instrum. Meas. 70, 3508612 (2021)
Lu, S., Qian, G., He, Q., Liu, F., Liu, Y., Wang, Q.: In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system. IEEE Sens. J. 20, 8287–8296 (2020)
Zhou, X.Y., Wu, X.L.: Teaching mode based on educational big data mining and digital twins. Comput. Intell. Neurosci. 2022, 9071944 (2022)
Lu, S., Yan, R., Liu, Y., Wang, Q.: Tacholess speed estimation in order tracking: a review with application to rotating machine fault diagnosis. IEEE Trans. Instrum. Meas. 68, 2315–2332 (2019)
Zhou, L.X., Zhang, Z.Y., Zhao, L.J., Yang, P.L.: Attention-based BiLSTM models for personality recognition from user-generated content. Inf. Sci. 596, 460–471 (2022)
Yang, W., Yao, Q., Ye, K., Xu, C.-Z.: Empirical mode decomposition and temporal convolutional networks for remaining useful life estimation. Int. J. Parallel Prog. 48(1), 61–79 (2019). https://doi.org/10.1007/s10766-019-00650-1
Nie, X.Y., Xie, G.: A novel framework using gated recurrent unit for fault diagnosis of rotary machinery with noisy labels. Meas. Sci. Technol. 32, 055107 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Lu, S., Wang, X., Ju, B., Liu, Y., Xie, F., Xia, M. (2023). An Experimental Case Study for the Course of ‘Testing Technology and Data Processing’. In: Hong, W., Weng, Y. (eds) Computer Science and Education. ICCSE 2022. Communications in Computer and Information Science, vol 1813. Springer, Singapore. https://doi.org/10.1007/978-981-99-2449-3_20
Download citation
DOI: https://doi.org/10.1007/978-981-99-2449-3_20
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2448-6
Online ISBN: 978-981-99-2449-3
eBook Packages: Computer ScienceComputer Science (R0)