Fault diagnosis is useful in helping technicians detect, isolate, and identify faults, and troubleshoot. Bayesian network (BN) is a probabilistic graphical model that effectively deals with various uncertainty problems. This model is increasingly utilized in fault diagnosis. This paper presents bibliographical review on use of BNs in fault diagnosis in the last decades with focus on engineering systems. This work also presents general procedure of fault diagnosis modeling with BNs; processes include BN structure modeling, BN parameter modeling, BN inference, fault identification, validation, and verification. The paper provides series of classification schemes for BNs for fault diagnosis, BNs combined with other techniques, and domain of fault diagnosis with BN. This study finally explores current gaps and challenges and several directions for future research.

Bayesian Networks in Fault Diagnosis

Intelligent Power System Operation and Control: Japan Case Study Application of Intelligent Methods to Power Systems Application to Power System Planning Application to Power System Control and Restoration Future Implementations Automatic Generation Control (AGC): Fundamentals and Concepts AGC in a Modern Power System Power System Frequency Control Frequency Response Model and AGC Characteristics A Three-Control Area Power System Example Intelligent AGC: Past Achievements and New Perspectives Fuzzy Logic AGC Neuro-Fuzzy and Neural-Networks-Based AGC Genetic-Algorithm-Based AGC Multiagent-Based AGC Combined and Other Intelligent Techniques in AGC AGC in a Deregulated Environment AGC and Renewable Energy Options AGC and Microgrids Scope for Future Work AGC in Restructured Power Systems Control Area in New Environment AGC Configurations and Frameworks AGC Markets AGC Response and an Updated Model Neural-Network-Based AGC Design An Overview ANN-Based Control Systems Flexible Neural Network Bilateral AGC Scheme and Modeling FNN-Based AGC System Application Examples AGC Systems Concerning Renewable Energy Sources An Updated AGC Frequency Response Model Frequency Response Analysis Simulation Study Emergency Frequency Control and RESs Key Issues and New Perspectives AGC Design Using Multiagent Systems Multiagent System (MAS): An Introduction Multiagent Reinforcement-Learning-Based AGC Using GA to Determine Actions and States An Agent for ss Estimation Bayesian-Network-Based AGC Approach Bayesian Networks: An Overview AGC with Wind Farms Proposed Intelligent Control Scheme Implementation Methodology Application Results Fuzzy Logic and AGC Systems Study Systems Polar-Information-Based Fuzzy Logic AGC PSO-Based Fuzzy Logic AGC Frequency Regulation Using Energy Capacitor System Fundamentals of the Proposed Control Scheme Study System Simulation Results Evaluation of Frequency Regulation Performance Application of Genetic Algorithm in AGC Synthesis Genetic Algorithm: An Overview Optimal Tuning of Conventional Controllers Multiobjective GA GA for Tracking Robust Performance Index GA in Learning Process Frequency Regulation in Isolated Systems with Dispersed Power Sources Configuration of Multiagent-Based AGC System Configuration of Laboratory System Experimental Results

Intelligent Automatic Generation Control

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

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Improving the accuracy of medical diagnosis with causal machine learning.

Transient fault (TF) and intermittent fault (IF) of complex electronic systems are difficult to diagnose. As the performance of electronic products degrades over time, the results of fault diagnosis could be different at different times for the given identical fault symptoms. A dynamic Bayesian network (DBN)-based fault diagnosis methodology in the presence of TF and IF for electronic systems is proposed. DBNs are used to model the dynamic degradation process of electronic products, and Markov chains are used to model the transition relationships of four states, i.e., no fault, TF, IF, and permanent fault. Our fault diagnosis methodology can identify the faulty components and distinguish the fault types. Four fault diagnosis cases of the Genius modular redundancy control system are investigated to demonstrate the application of this methodology.

A Dynamic-Bayesian-Network-Based Fault Diagnosis Methodology Considering Transient and Intermittent Faults

This paper proposes a graphic modeling approach, fault diagnosis method based on fuzzy reasoning spiking neural P systems (FDSNP), for power transmission networks. In FDSNP, fuzzy reasoning spiking neural P systems (FRSN P systems) with trapezoidal fuzzy numbers are used to model candidate faulty sections and an algebraic fuzzy reasoning algorithm is introduced to obtain confidence levels of candidate faulty sections, so as to identify faulty sections. FDSNP offers an intuitive illustration based on a strictly mathematical expression, a good fault-tolerant capacity due to its handling of incomplete and uncertain messages in a parallel manner, a good description for the relationships between protective devices and faults, and an understandable diagnosis model-building process. To test the validity and feasibility of FDSNP, seven cases of a local subsystem in an electrical power system are used. The results of case studies show that FDSNP is effective in diagnosing faults in power transmission networks for single and multiple fault situations with/without incomplete and uncertain SCADA data, and is superior to four methods, reported in the literature, in terms of the correctness of diagnosis results.

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Fault Diagnosis of Electric Power Systems Based on Fuzzy Reasoning Spiking Neural P Systems

In this paper, three element-oriented models based on simplified Bayesian networks with Noisy-Or and Noisy-And nodes are proposed to estimate the faulty section of a transmission power system. The three models are used to test if any transmission line, transformer, or busbar within a blackout area is faulty. They can deal with uncertain or incomplete data and knowledge relating to power system diagnosis, so they are flexible. The structures and initial parameters of the Bayesian networks depend on the prior knowledge of the domain experts. The parameters can be revised by using an error back propagation algorithm similar to the back-propagation algorithm for artificial neural networks. The fault diagnosis models do not vary with the change of the network structure, so they can be applied to any transmission power system. Furthermore, they have clear semantics, rapid reasoning, powerful error tolerance ability, and no convergence problem during the diagnosing procedure. Experimental tests show that the approach is feasible and efficient, so the prototype program based on the approach is promising to be used in a large transmission power system for online fault diagnosis.

Bayesian networks-based approach for power systems fault diagnosis

Deployment strategy of WSN based on minimizing cost per unit area

In this paper, an approximate inference algorithm on Bayesian networks (BN) for reliability assessment of power systems by time-sequence simulation is presented. Based on uniformly distributed pseudo random numbers, the duration time sampling for a component state can be obtained through inverse transform method. According to the logical relationship of the power system to be assessed, the sampling of minimal state cut sets of the system and the system node's sampling are obtained respectively. Thus, the corresponding sample sequence in forward sampling is available. When the amount of sample sequence is large enough, marginal and conditional statistic values are close to marginal and conditional probability of the node. Then the approximate inference results of the BN can be obtained. Numerical simulating results show the effectiveness of the proposed algorithm. It can easily identify the weaker components of a system in reliability, calculate the system reliability indices, and is suitable for a large-scale power system.

Bayesian Network Based Time-Sequence Simulation for Power System Reliability Assessment

This paper presents an application method of Bayesian networks (BN) to the reliability assessment of power systems. Bayesian networks provide a flexible framework to represent probabilistic information and to make inference on it. Uncertainty and dependency of the components' information in a system are easily incorporated in the analysis. The flexibility of the probabilistic inference algorithms in Bayesian networks permit to compute both the system's reliability indices and the mutual affection on reliability indices of all components. However, a BN cannot be constructed easily based on the topology of the relating power system. The paper gives a new method to construct a Bayesian network based on the assessed system's fault tree or its minimal path set. The method is efficient and can compute components failure probabilities on the condition of the system failure. Its advantages are demonstrated through two examples.

Reliability assessment of power systems by Bayesian networks

The dual population ant colony optimization (DPACO) was tried to be applied to power system dynamic reactive power optimization. The installation positions of capacitors were taken as obstacles, the capacities of capacitors installed were taken as the paths through which the ants climb over the obstacles and the mathematical models of the reactive power planning under the multiple load state were adopted. In running process, the pheromone was adjusted according to the antpsilas search results and the principle of pheromone modification and the convergence speed was fastened. At the same time, the dual population ant colony optimization (DPACO) avoided trapping in local optimum and increased the precision of reactive power optimization for doing well in global optimization. After optimization, the voltage quality was enhanced obviously and comprehensive fees decrease significantly. The running results show that dual population ant colony optimization (DPACO) applied to power system dynamic reactive power optimization is feasible and effective.

Huo Limin

Papers

Bayesian networks-based approach for power systems fault diagnosis

Deployment strategy of WSN based on minimizing cost per unit area

Bayesian Network Based Time-Sequence Simulation for Power System Reliability Assessment

Reliability assessment of power systems by Bayesian networks

Reactive Power Optimization for distribution systems based on Dual Population Ant Colony Optimization