Remaining useful life estimation-a case study on soil moisture sensors

Fang Chien Chai, Chun Chih Lo, Mong Fong Horng, Yau-Hwang Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
EditorsSatoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
PublisherSpringer Verlag
Pages328-338
Number of pages11
ISBN (Print)9783319544298
DOIs
Publication statusPublished - 2017 Jan 1
Event9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan
Duration: 2017 Apr 32017 Apr 5

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10192 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
CountryJapan
CityKanazawa
Period17-04-0317-04-05

Fingerprint

Soil Moisture
Soil moisture
Fault
Sensor
Sensors
Curve fitting
Life Span
State Machine
Divides
Model
Life
Costs
Experimental Results
Coefficient
Graph in graph theory
Estimate
Trends

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Chai, F. C., Lo, C. C., Horng, M. F., & Kuo, Y-H. (2017). Remaining useful life estimation-a case study on soil moisture sensors. In S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (Eds.), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings (pp. 328-338). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54430-4_32
Chai, Fang Chien ; Lo, Chun Chih ; Horng, Mong Fong ; Kuo, Yau-Hwang. / Remaining useful life estimation-a case study on soil moisture sensors. Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. editor / Satoshi Tojo ; Le Minh Nguyen ; Ngoc Thanh Nguyen ; Bogdan Trawinski. Springer Verlag, 2017. pp. 328-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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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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Chai, FC, Lo, CC, Horng, MF & Kuo, Y-H 2017, Remaining useful life estimation-a case study on soil moisture sensors. in S Tojo, LM Nguyen, NT Nguyen & B Trawinski (eds), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10192 LNAI, Springer Verlag, pp. 328-338, 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, Kanazawa, Japan, 17-04-03. https://doi.org/10.1007/978-3-319-54430-4_32

Remaining useful life estimation-a case study on soil moisture sensors. / Chai, Fang Chien; Lo, Chun Chih; Horng, Mong Fong; Kuo, Yau-Hwang.

Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. ed. / Satoshi Tojo; Le Minh Nguyen; Ngoc Thanh Nguyen; Bogdan Trawinski. Springer Verlag, 2017. p. 328-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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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Chai FC, Lo CC, Horng MF, Kuo Y-H. Remaining useful life estimation-a case study on soil moisture sensors. In Tojo S, Nguyen LM, Nguyen NT, Trawinski B, editors, Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Springer Verlag. 2017. p. 328-338. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-54430-4_32