Fault Detection and Improvement Design of Temperature Sensor in Wood Carbonization Furnace

Fault Detection and Improvement Design of Temperature Sensor in Wood Carbonization Furnace

Ming Yu Jian WangMeng Zhu JiaShun Luo 

College of Information and Computer Engineering, Northeast Forestry University, Harbin, Heilongjiang, 150040, China

School of electrical and information engineering, Northeast Agricultural University, Harbin, Heilongjiang, 150040 China

Corresponding Author Email: 
wang1342@foxmail.com
Page: 
43-56
|
DOI: 
https://doi.org/10.18280/ama_b.600103
Received: 
15 March 2017
| |
Accepted: 
15 April 2017
| | Citation

OPEN ACCESS

Abstract: 

As an important detection index in the carbonization process, wood carbonization furnace temperature ensures the reliability of wood carbonization control system. In this paper, a data-fusion-based method was proposed for fault detection and its improvements of carbonization furnace temperature sensor. By applying the data fusion algorithm to the control system of wood carbonization furnace, we obtained the modified data fusion method, with which we judged the working status of the temperature sensor in addition to the usage of the comprehensive supporting degree of sensors within the same group. A limited number of hardware backups were employed to guarantee the reliability of temperature measurement. The experiment result shows that our method performs effectively in enhancing the reliability and stability of wood charring furnace.

Keywords: 

wood carbonization, temperature measurement, failure, confidence level

1. Introduction
2. The Significance of Temperature Monitoring in Wood Carbonization Furnace
3. Calculation of The Confidence Degree Of Measured Temperature Based On Data Fusion
4. Historical Data Validation Cases
5. Case Verification
6. Conclusion
Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (2572015BB22) and National Science and Technology Project (2014BAF11B01) and Fundamental Research Funds for the Central Universities (DL11AB01).

  References

1. P.F. Li, Y.P. Yang, Research on improving measurement accuracy of solar heat pump system of temperature sensor, 2014, Journal of Sensors and Actuators, vol. 8, pp. 1017-1021.

2. W. Li, J.Y. Zhang, Study of multi-model soft close-loop fault-tolerant control with sensor faults, 2015, Application Research of Computers, vol. 4, pp. 447-450.

3. Y.L. Zhou, H.Y Li, H.W. Li, Application of improved LVQ neural network in fault diagnosis of fans, 2013, Control and Instruments in Chemical Industry, vol. 40, no. 1, pp. 610-615.

4. X.Z. Zuo, J. Kang, H. Li, L.W. Tang, Overview of fault prediction technology, 2010, Fire Control and Command Control, vol. 35, no. 1, pp. 1-5.

5. J.Q. An, K. Peng, W.H. Cao, M. Wu, A soft-sensing method for missing temperature information based on dynamic neural network on BF wall, 2016, Journal of Chemical Industry and Engineering, vol. 67, no. 3, pp. 903-911.

6. W. Li, Y.L. Yu, D.R. Sheng, J.H. Chen, Suppression technology for pressure fluctuation in hanger pressure test based on impedance method, 2016, Vibration test and diagnosis, vol. 36, no. 4, pp. 694-699.

7. D.S. Liu, The modeling of wood drying schedule based on mult-model data fusion modeling algorithms, 2007, Northeast Forestry University, vol. 26, no.7, pp. 82-84.

8. D.S. Liu, J.W. Zhang, Modeling of wood drying schedule based on multimode1data fusion modeling algorithms, 2007, Transducer and Microsystem Technologies, vol. 26, no. 7, pp. 82-84.

9. J.Q. An, M. Wu, Y. He, W.H. Cao, Temperature detection method of blast furnace burden surface based on the reliability of multi-source information, 2012, Journal of Shanghai University, vol. 46, no. 12, pp. 1945-1950.

10. F. Ruan, Research on small target recognition method based on optical sensor data fusion, 2016, Electronic Test, vol.8, no.12, pp. 85-89.

11. X.Y. Zhang, S. Zheng, H.C. Zhou, H.J. Xu, Visualizaion of pipe temperature distribution in tubular furnace based on radiation imaging model solving, 2015, Journal of Chemical Industry and Engineering, vol.6, no.15, pp. 965-971.

12. J.W. Zhang, R.L. Guo, Research on determining effective working state of sensor during wood drying process, 2009, Transducer and Microsystem Technologies, vol. 28, no. 5, pp 55-57.

13. J Bin, L Cui, Adaptive weighted fusion method for detecting wood drying kiln temperature, 2013, Journal of Anhui Agricultural Sciences, vol. 22, no. 22, pp. 9361-9362.

14. X.L. Wang, J.L. Pei, R.Z. Liu, X.K. Yi, Research of south xinjiang intelligent control of greenhouse based on multi-sensor data fusion, 2017, Journal of Agricultural Mechanization Research, vol. 39, no.7, pp 45-50 

15. J.H. Du, L.Y. Sun, Y.K. Zhang, Z.S. Shang Ye, F.X., ChuanMan, A study of technological parameters for agricultural three-step carbonization equipment, 2010, Machinery Design and Manufacture, vol. 24, no. 9, pp. 191-193.

16. H. Biao, X.R. Chen, M.S. Jiang, X.P. Tang, S.Y. Gao, Effect of carbonization temperature on microporous structure of charcoal firm Chinese fir wood, 2006, Chemistry and Industry of Forest Products, vol. 26, no. 1, pp. 70-74.