Abstract
The trend features, which are named trend symptoms later, of the operation conditions of critical equipment in a nuclear power plant can reflect its operation conditions, especially the potential failures. The trend features can be acquired based on the related physical quantities, such as temperatures, pressures, and flows, measured by sensors. When judging the trend symptoms of the operation and equipment, it is often necessary to select a suitable inputting data length. This paper proposes a method to determine the inputting data length based on wavelet analysis. The equipment history data are filtered by wavelet analysis to remove the noise and burr, then the smooth data curve after filtering will be segmented by a time series segmentation method based on local maximum and minimum, and the analysis results are obtained by linear fitting of the data within each segment. Then, the shortest segment of the segmentation results is conservatively selected as the reference value for the inputting data length of the corresponding equipment. The effectiveness of the proposed method is demonstrated by testing with several pieces of equipment history data. The results show that the proposed method can improve real-time performance while ensuring the accuracy of determining the symptom of equipment operation data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer, Berlin (2002)
He, Q.P., Qin, S.J., Wang, J.: A new fault diagnosis method using fault directions in fisher discriminant analysis. AIChE J. 51(2), 555–571 (2005)
Dai, X., Gao, Z.: From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans. Industr. Inf. 9(4), 2226–2238 (2013)
Ma, J., Jiang, J.: Applications of fault detection and diagnosis methods in nuclear power plants: a review. Prog. Nucl. Energy 53(3), 255–266 (2011)
Ding, S.X.: Application of partial least squares regression to fault diagnosis. In: Ding, S.X. (ed.) Data-driven Design of Fault Diagnosis and Fault-tolerant Control Systems. AIC, pp. 95–116. Springer, London (2014). https://doi.org/10.1007/978-1-4471-6410-4_6
Jia, F., Lei, Y., Lin, J., et al.: Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72, 303–315 (2016)
Jin, Y., Shan, C., Wu, Y., et al.: Fault diagnosis of hydraulic seal wear and internal leakage using wavelets and wavelet neural network. IEEE Trans. Instrum. Meas. 68(4), 1026–1034 (2018)
Lin, G.B., Fu, P., Zhang, E.Q., et al.: A new classifying method for mechanical seal condition based on acoustic emission and wavelet neural network. Lubr. Eng. 000(009), 40-45 (2014)
Gu, J., He, P., Liu, C.X.: On -line vibration monitoring system of nuclear reactor main pump based on PXI. Nucl. Electr. Detect. Technol. 33(12), 1498–1501 (2013)
Shu, X.T., Yang, Z., Xu, Y.Z., et al.: Fault diagnosis of vibration induced by fluid of 100D main pump for CPR1000 unit. Nucl. Power Eng. 42(3), 5 (2021)
Zhao, X., Zhang, S., Zhou, C., et al.: Experimental study of hydraulic cylinder leakage and fault feature extraction based on wavelet packet analysis. Comput. Fluids 106, 33–40 (2014)
Goharrizi, A.Y., Sepehri, N.: Application of fast fourier and wavelet transforms towards actuator leakage diagnosis: a comparative study. Int. J. Fluid Power 14(2), 39–51 (2013)
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
Zhang, WJ., Zhou, SQ., Li, D., Huang, XJ. (2023). Determination of the Inputting Data Length for the Diagnosis of the Operation Trend Based on Wavelet Analysis. In: Gu, P., Xu, Y., Chen, W., Chen, R., Sun, Y., Liu, Z. (eds) New Energy Power Generation Automation and Intelligent Technology. SICPNPP 2023. Lecture Notes in Electrical Engineering, vol 1055. Springer, Singapore. https://doi.org/10.1007/978-981-99-3455-3_30
Download citation
DOI: https://doi.org/10.1007/978-981-99-3455-3_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-3454-6
Online ISBN: 978-981-99-3455-3
eBook Packages: EnergyEnergy (R0)