Abstract
This paper presents an approach to estimate the remaining useful life of sensors. First, a system state machine is defined to divide the sampled data received from the sensors into different categories. Then, the sampled data sets are sent to the fault model to detect whether a fault has occurred. The time of occurrence for each type of fault is recorded and weighted with different coefficient. The weighted values are accumulated to form a trend data graph. An exponential curve fitting is then used to approximate the trend of data to determine the remaining useful life function and threshold is also generated from the cumulative faults value. The experimental results shows the proposed model has a precision of 66.67% and recall rate near 100% within 10-h timespan. Thus, the proposed model may not only prolong the life span of sensors, but may also reduce the cost to replace them.
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Acknowledgement
The authors would like to thank the Ministry of Science and Technology for supporting this research, which is part of the project numbered 103-2221-E-006-257-MY3, 104-2221-E-151-007-, 105-2221-E-151-034-MY2, and 105-2221-E-006-138-MY2.
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Chai, FC., Lo, CC., Horng, MF., Kuo, YH. (2017). Remaining Useful Life Estimation-A Case Study on Soil Moisture Sensors. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_32
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DOI: https://doi.org/10.1007/978-3-319-54430-4_32
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