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Intelligent Sensors for Intelligent Systems: Fault Tolerant Measurement Methods for Intelligent Strain Gauge Pressure Sensors

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Intelligent Systems and Applications (IntelliSys 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 295))

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Abstract

A new method is described for measuring an existing pressure transducer with greater potential for analysis. The new method has the potential to allow more comprehensive and early detection of sensor faults. This supports further work to develop fault tolerance, on board data quality estimation and failure prediction enabling intelligent sensors to operate more independently and reliably. A computer model of the sensor was constructed, and measurement approaches compared. A typical measurement scenario was simulated under normal operating conditions before and after an over pressure damage event. The range of failure modes detectable using this approach are discussed. The new method was then simulated using the same overpressure damage event. The results of the simulation are discussed and compared. The new measurement method has the potential to allow more comprehensive and early detection of sensor faults.

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References

  1. Thabet, M., et al.: Management of compressed air to reduce energy consumption using intelligent systems. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys. AISC, vol. 1252, pp. 206–217. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_16

    Chapter  Google Scholar 

  2. Sanders, D.A., Robinson, D.C., Hassan, M., Haddad, M., Gegov, A., Ahmed, N.: Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys. AISC, vol. 869, pp. 1229–1236. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-01057-7_92

    Chapter  Google Scholar 

  3. Omoarebun, P.O., Sanders, D., Haddad, M., Hassan Sayed, M., Tewkesbury, G., Giasin, K.: An intelligent monitoring system for a crude oil distillation column. In: 2020 IEEE 10th International Conference on Intelligent Systems (IS), pp. 159–164. IEEE IS Proceedings Series. IEEE (2020). https://doi.org/10.1109/IS48319.2020.9200175

  4. Ikwan, F., et al.: Intelligent risk prediction of storage tank leakage using an Ishikawa diagram with probability and impact analysis. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) IntelliSys. AISC, vol. 1252, pp. 604–616. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-55190-2_45

    Chapter  Google Scholar 

  5. Sanders, D.: New method to design large scale high-recirculation airlift reactors. J. Environ. Eng. Sci. 12(3), 62–78 (2017). https://doi.org/10.1680/jenes.17.00008

    Article  Google Scholar 

  6. Painting, A., Sanders, D.: Disaster prevention through intelligent monitoring. J. Syst. Saf. 52(3), 23–30 (2016). http://www.system-safety.org/jss/

  7. Tewkesbury, G.E.: Design using distributed intelligence within advanced production machinery. Ph.D. thesis, University of Portsmouth, UK (1994)

    Google Scholar 

  8. Tewkesbury, G., Sanders, D., Strickland, P., Hollis, J.: Task orientated programming of advanced production machinery. In: Dedicated Conference on Mechatronics, 1993, pp. 623–630. Automotive Automation (1993)

    Google Scholar 

  9. Ahmad, R., Kamaruddin, S.: An overview of time-based and condition-based maintenance in industrial application. Comput. Ind. Eng. 63(1), 135–149 (2012). https://doi.org/10.1016/j.cie.2012.02.002

    Article  Google Scholar 

  10. Haddad, M.J.M., Sanders, D.: Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty. Oper. Res. Perspect. 5, 357–370 (2018). https://doi.org/10.1016/j.orp.2018.10.003

    Article  MathSciNet  Google Scholar 

  11. Liang, H., Chen, H., Lu, Y.: Research on sensor error compensation of comprehensive logging unit based on machine learning. J. Intell. Fuzzy Syst. 37(3), 3113–3123 (2019). https://doi.org/10.3233/JIFS-179114

    Article  Google Scholar 

  12. X-CUBE-AI – STMicroelectronics. STMicroelectronics (2021). https://www.st.com/en/embedded-software/x-cube-ai.html. Accessed 26 Apr 2021

  13. Lai, L., Suda, N., Chandra, V.: CMSIS-NN: Efficient neural network kernels for arm cortex-M CPUs, arXiv, pp. 1–10 (2018)

    Google Scholar 

  14. TensorFlow Lite for Microcontrollers. TensorFlow (2021). https://www.tensorflow.org/lite/microcontrollers. Accessed 26 Apr 2021

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Correspondence to David Sanders .

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Barker, T., Tewkesbury, G., Sanders, D., Rogers, I. (2022). Intelligent Sensors for Intelligent Systems: Fault Tolerant Measurement Methods for Intelligent Strain Gauge Pressure Sensors. In: Arai, K. (eds) Intelligent Systems and Applications. IntelliSys 2021. Lecture Notes in Networks and Systems, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-030-82196-8_46

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