Author(s)

Chunhua Feng ¹, Zihan Jiang ¹

Affiliation(s)

¹ School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

Corresponding Author

Chunhua Feng

ABSTRACT

For illustrating the application of test technology theory in engineering practice, a multi-sensor data fusion fault diagnosis method is proposed, which uses data from flow, pressure and acceleration sensors and combines with deep learning to fuse multiple signals. Firstly, different failure modes of a certain device are simulated, and the original time series of different sensors are structured using long short-term memory. Then, the data processed by different fault modes are input into the convolutional neural network for recognition. Finally, the output of multiple networks is fused to achieve more comprehensive and accurate fault detection. This case illustrates the function of testing basic theory in solving practical engineering and the method of practical application.

KEYWORDS

Testing courses, Multi-sensor signal processing, LSTM

CITE THIS PAPER

Chunhua Feng, Zihan Jiang, Application of Multi-sensor Signal Processing in Testing Courses. Advances in Computer, Signals and Systems (2024) Vol. 8: 59-64. DOI: http://dx.doi.org/10.23977/acss.2024.080308.

REFERENCES

[1] Wang L, Cao H., Ye Z., Yan J. (2024) DVGT former: A dual-view graph Transformer to fuse multi-sensor signals for remaining useful life prediction. Mechanical Systems and Signal Processing, 207, 110935.
[2] Liang J., Mao Z., Liu F., Kong X., Zhang J., Jiang Z. (2023) Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions. Engineering Applications of Artificial Intelligence, 126, 106912.
[3] Guo J., He Q., Zhen D., Gu F., Ball A.D. (2023) Multi-sensor data fusion for rotating machinery fault detection using improved cyclic spectral covariance matrix and motor current signal analysis. Reliability Engineering & System Safety, 230, 108969.
[4] Wang Z, Yan J. (2024) Multi-sensor fusion based industrial action recognition method under the environment of intelligent manufacturing. Journal of Manufacturing Systems, 74, 575-586. 
[5] Kibrete F, Woldemichael D E, Gebremedhen H S. (2024) Multi-Sensor data fusion in intelligent fault diagnosis of rotating machines: A comprehensive review. Measurement, 114658.

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