ArticleSensors (Basel, Switzerland)2017
Application of Fault Tree Analysis and Fuzzy Neural Networks to Fault Diagnosis in the Internet of Things (IoT) for Aquaculture.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Exploration and analysis of the application of the internet of things in nursing education in the 5G era: innovation of nursing education methods.BMC medical education · 2026Review
- Article
- Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.Sensors (Basel, Switzerland) · 2023Article
- Current Status and Prospects of Research on Sensor Fault Diagnosis of Agricultural Internet of Things.Sensors (Basel, Switzerland) · 2023Review
- Reliability Analysis of Wireless Sensor Network for Smart Farming Applications.Sensors (Basel, Switzerland) · 2021Article
- Single Fault Diagnosis Method of Sensors in Cascade System Based on Data-Driven.Sensors (Basel, Switzerland) · 2021Article
- Fault Detection and Identification Method for Quadcopter Based on Airframe Vibration Signals.Sensors (Basel, Switzerland) · 2021Article
- Exploring Quality Evaluation of Innovation and Entrepreneurship Education in Higher Institutions Using Deep Learning Approach and Fuzzy Fault Tree Analysis.Frontiers in psychology · 2021Article
- Intelligent fish farm-the future of aquaculture.Aquaculture international : journal of the European Aquaculture Society · 2021Review
- Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability.Sensors (Basel, Switzerland) · 2019Article
- Design and Deployment of Low-Cost Sensors for Monitoring the Water Quality and Fish Behavior in Aquaculture Tanks during the Feeding Process.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
4 authors.
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Abstract
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.
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