Bayesian Causal Network for Discrete Variables

Ensuring the safety of industrial systems requires not only detecting the faults, but also locating them so that they can be eliminated. The previous chapters have discussed the fault detection and identification methods. Fault traceability is also an important issue in industrial system. This chapter and Chap. 10.1007/978-981-16-8044-1_14 aim at the fault inference and root tracking based on the probabilistic graphical model. This model explores the internal linkages of system variables quantitatively and qualitatively, so it avoids the bottleneck of multivariate statistical model without clear mechanism. The exacted features or principle components of multivariate statistical model are linear or nonlinear combinations of system variables and have not any physical meaning. So the multivariate statistical model is good at fault detection and identification, but not at fault root tracking.

A Belief Network Reasoning Framework for Fault Localization in Communication Networks

A small fault in a large communication network may cause abrupt and large alarms, making the localization of the root cause of failure a difficult task. Traditionally, fault localization is carried out by an operator who uses alarms in alarm lists; however, fault localization process complexity needs to be addressed using more autonomous and intelligent approaches. Here, we present an overall framework that uses a message propagation mechanism of belief networks to address fault localization problems in communication networks. The proposed framework allows for knowledge storage, inference, and message transmission, and can identify a fault’s root cause in an event-driven manner to improve the automation of the fault localization process. Avoiding the computational complexity of traditional Bayesian networks, we perform fault inference in polytrees with a noisy OR-gate model (PTNORgate), which can reduce computational complexity. We also offer a solution to store parameters in a network parameter table, similar to a routing table in communication networks, with the aim of facilitating the development of the algorithm. Case studies and a performance evaluation show that the solution is suitable for fault localization in communication networks in terms of speed and reliability.

Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study.

Intelligent Fault Inference of Inverters Based on a Three-Layer Bayesian Network

A three-layer Bayesian intelligent fault inference model (BIFIM) for an inverter is established to infer the probable uncertain faults. The topological structure of the BIFIM includes the inverter’s operation conditions for the first layer, the inverter’s faults for the second layer, and the fault symptoms for the third layer, which combines the field technicians’ knowledge and experiences with historical running data. The prior probability table of the root node is acquired by the method of basic probabilities corrected historical operation data. The conditional probability parameter table of the BIFIM is obtained by the improved maximum expectation algorithm. Four kinds of incomplete evidence were reasoned and verified, including simple evidence with obvious support, incomplete evidence information, complex evidence without obvious support, and evidence with information conflict. The proposed strategy can make use of the available evidences to inference the probabilities of faults, indicating different reasoning abilities under the different degree of completeness of evidence, especially demonstrating the same inference result under some incomplete evidence information as under complete evidence information.

A theory for non-linear prediction approach in the presence of vague variables: with application to BMI monitoring

In the statistical literature, truncated distributions can be used for modeling real data. Due to error of measurement in truncated continuous data, choosing a crisp trimmed point caucuses a fault inference, so using fuzzy sets to define a threshold pointmay leads us more efficient results with respect to crisp thresholds. Arellano-Valle et al. [2] defined a selection distribution for analysis of truncated data with crisp threshold. In this paper, we define fuzzy multivariate selection distribution that is an extension of the selection distributions using fuzzy threshold. A practical data set with a fuzzy threshold point is considered to investigate the relationship between high blood pressure and BMI.

Design of Urban-Rural Power Grid Dispatching Fault Diagnosis Expert System

The paper designs the urban-rural power grid dispatching fault diagnosis expert system which acquires fault information by SCADA system of automatic system of urban-rural power grid, and uses artificial intellegence method to analyze fault information and make fault diagnosis. The paper implements the core part of the fault expert systemthe design of knowledge base and fault inference engine.