# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
## 6 BN Model Example for IPT in an IoT Network  
The Internet of Things (IoT) network is composed of three primary components: devices, networks, and cloud computing. These devices, often referred to as \"smart\" devices, are equipped with sensors and actuators that enable them to gather data and perform various functions. The network connects these devices, allowing them to share data and communicate with other devices and systems. Data collected by these devices are stored, processed, and analyzed, in the cloud. IoT-based networks have diverse applications across industries. For instance, the implementation of IoT technology in the smart grid has been extensively studied by researchers [33, 34, 35, 36, 37], as it has the potential to enhance the efficiency, sustainability, and reliability of the electrical grid. Similarly, in various industries, intelligent instruments are being widely adopted as \"smart\" devices. For example, in industrial control, intelligent pressure transmitters (IPTs) are extensively used to measure liquid level, pressure, and flow. Compared to traditional pressure transmitters, IPTs can receive and output digital signals, based on specific communication protocols. Given that this transmitter is the most commonly used equipment in industrial control areas, the accuracy and timeliness of IPT fault detection are directly linked to production safety and stability. Therefore, IPTs in IoT systems can serve as models for verification purposes. The overall structure of the IoT system is illustrated in Figure 5.  
There are numerous types of IPTs, and a certain type of IPT was used as a method verification example in this study. The selected IPT contains a power resource module, a micro control unit (MCU) module, a sensor module, a signal conditioning module, a display module, and structure support components. The power resource module links the external power supply and converts it into a power supply that is suitable for transmitter operation. The sensor module collects data using on-site sensors. The signal conditioning module adjusts the data signal conditioning circuit. Figure 5: IoT system hardware architecture. The MCU module provides and processes measurement data for real-time applications of the human-machine interface and communication interfaces. Functions such as configuration, calibration, rectification, and diagnosis, have been realized using the MCU module. The display module integrates the display data function on the meter, the output data function, and local buttons for issuing requests. It is the human-machine interface through which the IPT interacts with the operators. IPTs have a variety of faults in real operation; the failure modes of each IPT module were analyzed and denoted as root events, and \"the un-normal performance of IPT\" was regarded as a top event. Based on the IPT module structure and failure mechanism, there are 33 typical IPT faults, x1-x33, and they are described in Table 3. However, these faults cannot be directly monitored by IoT sensors, and only a portion of the performance parameters of the functional modules can be monitored. Therefore, a fault tree (FT) for BN construction was defined according to the actual monitoring parameters, and it had five layers: a top node layer, a functional module node layer, a monitoring node layer, an intermediate node layer, and a root node layer, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline  
The fault tree in Figure 6 was transformed into a BN model using the leaky noisy-OR gate; the BN model has 1 top node, 6 module nodes, 10 monitored nodes, 3 intermediate nodes, and 33 root nodes. Five experts' opinions regarding the joint fault probability level of the root nodes were collected in this study.