# Bayesian Uncertainty Inferencing for Fault Diagnosis of Intelligent Instruments in IoT Systems
###### Abstract  
Intelligent instruments are common components in industrial machinery, and fault diagnosis in IoT systems requires the handling of real-time sensor data and expert knowledge. IoT sensors cannot collect data for the diagnosis of all fault types in a specific instrument, and long-distance data transfer introduces additional uncertainties. However, because industrial equipment has complex fault causes and performances, it is typically difficult or expensive to obtain exact fault probabilities. Therefore, in this study, we proposed an innovative failure detection and diagnosis model for intelligent instruments in an IoT system using a Bayesian network, with a focus on handling uncertainties in expert knowledge and IoT monitoring information. The model addresses the challenge of complex fault causes and performances in industrial equipment, which make the obtainment of exact fault probabilities difficult or expensive. The trapezoidal intuitionistic fuzzy number (TrIFN)-based entropy method was applied in order to aggregate expert knowledge to generate priority probability, and the Leaky-OR gate was used to calculate CPT. The effectiveness of the proposed strategy was demonstrated through its application to an intelligent pressure transmitter (IPT) using the GeNle software. intelligent instrument; IoT; Bayesian network; TrIFN; Leaky-OR gate Article  
1  
Fault diagnosis can be achieved using data-driven, knowledge-driven, and mathematical-model-based approaches [1]. Among these, knowledge-driven approaches usually focus on the experience or knowledge of machinery or physical systems; they are simple to use and efficient in the detection of faults in expert knowledge. However, knowledge-driven approaches depend heavily on personal understanding of fault mechanisms. Determining the causes of all faults is difficult, and it is difficult to respond appropriately to new faults or faults based on expert knowledge. Data-driven approaches for fault diagnosis include multivariate statistical methods such as PCA, SVM, and deep learning algorithm methods. Nearly all of these require large volume labeled fault data to train a fault model that can respond to target fault types. Unfortunately, fault data are typically insufficient to train a good model. Data-driven models are only useful for faults with large amounts of data. A mathematical-model-based approach simulates a system's behavior using a mathematical function model, and then diagnoses faults by contrasting the model's output with the actual system state/operation data. However, the interaction between the mechanical and the electrical parts of intelligent instruments, makes it impossible to create a clear model for all faults. Among all fault diagnosis techniques, Bayesian networks (BNs) can handle ambiguous information, such as data and expert knowledge; update probabilities; execute bidirectional reasoning; and handle data scarcity [2; 3; 4], which means they can be used to effectively reason based on uncertain information, including data and expert knowledge. Therefore, this study attempts to combine a knowledge-driven fault diagnosis approach with a BN framework, in order to build a practical hybrid intelligent instrument fault diagnosis method using IoT monitoring information. A BN-based intelligent instrument fault diagnosis model is constructed by synthesizing the instrument's historical fault data information, IoT online monitoring data, operation and maintenance observation information, and expert experience. When unusual/informal data from online intelligent instruments are detected, the established model can diagnose the possible fault types and instrument operating states.