Next Article in Journal
Fabrication of a Miniature Multi-Parameter Sensor Chip for Water Quality Assessment
Next Article in Special Issue
Context Sensing System Analysis for Privacy Preservation Based on Game Theory
Previous Article in Journal
Low Cost and Flexible UAV Deployment of Sensors
Previous Article in Special Issue
Adaptive Information Dissemination Control to Provide Diffdelay for the Internet of Things
Open AccessArticle

Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture

by Yingyi Chen 1,2,3,*, Zhumi Zhen 1,2,3, Huihui Yu 1,2,3 and Jing Xu 1,2,3
1
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2
Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China
3
Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, Beijing 100083, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yunchuan Sun
Sensors 2017, 17(1), 153; https://doi.org/10.3390/s17010153
Received: 3 November 2016 / Revised: 18 December 2016 / Accepted: 9 January 2017 / Published: 14 January 2017
In the Internet of Things (IoT) equipment used for aquaculture is often deployed in outdoor ponds located in remote areas. Faults occur frequently in these tough environments and the staff generally lack professional knowledge and pay a low degree of attention in these areas. Once faults happen, expert personnel must carry out maintenance outdoors. Therefore, this study presents an intelligent method for fault diagnosis based on fault tree analysis and a fuzzy neural network. In the proposed method, first, the fault tree presents a logic structure of fault symptoms and faults. Second, rules extracted from the fault trees avoid duplicate and redundancy. Third, the fuzzy neural network is applied to train the relationship mapping between fault symptoms and faults. In the aquaculture IoT, one fault can cause various fault symptoms, and one symptom can be caused by a variety of faults. Four fault relationships are obtained. Results show that one symptom-to-one fault, two symptoms-to-two faults, and two symptoms-to-one fault relationships can be rapidly diagnosed with high precision, while one symptom-to-two faults patterns perform not so well, but are still worth researching. This model implements diagnosis for most kinds of faults in the aquaculture IoT. View Full-Text
Keywords: Internet of Things; fault tree analysis; fuzzy neural network; fault diagnosis Internet of Things; fault tree analysis; fuzzy neural network; fault diagnosis
Show Figures

Figure 1

MDPI and ACS Style

Chen, Y.; Zhen, Z.; Yu, H.; Xu, J. Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture. Sensors 2017, 17, 153.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop