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


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.


Figure 1. Structure of the aquaculture IoT

Figure 1. Structure of the aquaculture IoT

The aquaculture IoT includes a data acquisition layer, transmission layer, storage layer and application layer, as shown in Figure 1. As the lowest level in the hierarchy, the data acquisition layer consists of different kinds of sensors, a weather station and monitor terminals (collectors) deployed in the experimental base. The weather station collects environment parameters in the pond micro climate. Sensor probes monitor water quality in real time by obtaining suitable signals. Then, these signals are transmitted to a monitor terminal. To make these signals readable, they are processed in a particular way, so water quality can be displayed in the user interface.

Figure 2. The possible locations fault occurs in the aquaculture IoT

Figure 2. The possible locations fault occurs in the aquaculture IoT

Thus faults definition is important and necessary for fault diagnosis in the aquaculture IoT. The faults are classified based on the fault location and numerical characteristics. In fact, a fault can occur at different locations. The red spots in Figure 2 show the possible locations where a fault can occur.


Figure 9. Power fault tree

Figure 9. Power fault tree

The fault tree of the IoT is divided into six sub-modules based on fault definitions: power fault tree, collector fault tree, sensor fault tree, software fault tree, environmental interference fault tree and communication modules fault tree. Figure 9 shows the power fault tree.

Figure 10. Sensor fault tree

Figure 10. Sensor fault tree

Figure 10 shows the sensor fault tree. Sensor faults are top events caused by damaged sensor probes, sensors sinking to the bottom of a pond, other sensor fault symptoms or the basic symptom contaminated sensor probe.


Figure 11. Neural network training error curve

Figure 11. Neural network training error curve

According the analysis of the 22 fault symptoms as the input layer nodes and the 13 output layer nodes for the Internet of Things (IoT) in aquaculture, we selected 10 nodes as hidden layer nodes. In the Discussion section, the results show the 10 hidden layer nodes can satisfy the fault diagnosis for the Internet of Things (IoT) in aquaculture. Training samples are obtained from practical maintenance experience. Figure 11 shows the training results of the FNN. The network converges quickly and achieves the best accuracy in epoch 50.


This study present an intelligent method based on fault tree analysis and a fuzzy neural network to diagnose faults in the IoT of aquaculture. In this method, six fault trees are developed based on fault definition. These trees display clear logic relationships of symptoms and faults in the IoT. It also provides a basis for the fuzzy neural network modeling. The FNN model is trained to modify connection weights and thresholds, and obtain the nonlinear mapping relationships between fault pairs.

The results obtained from experimental applications made on the aquaculture IoT show that this model implements diagnosis for most kinds of faults. By this study, the model provides users with fault information and maintenance suggestions. Furthermore, the application has reduced the reliance on experts, changed the traditional way of fault diagnosis, and guaranteed the safety of the aquaculture IoT. Further work needs to be performed. First, as this aquaculture IoT has just been developed for a short time, the fault data in the aquaculture IoT is not sufficient.

Thus, gathering more fault information and accumulating more experience are necessary to improve the knowledge base. Second, the one symptom to two fault symptoms relationship needs to be researched to make this method suitable for most situations. Data accumulation can improve the performance in some degree, and some algorithms may optimize the fuzzy neural network in future. Third, the choice of membership degree still relies on expert experience in this method. A proper fuzzy function should be considered to improve the model’s accuracy in future work.

Source:  China Agricultural University
Authors: Yingyi Chen | Zhumi Zhen | Huihui Yu | Jing Xu

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