Improved Bayesian network in steam turbine fault diagnosis

Previous work has proposed the ontology-based semi-automatic generation of antipattern Bayesian Network(BN) models. The generated BN model can be used to illustrate the effects of uncertainty on antipatterns using Bayesian propagation. This can guide users in detecting particular antipattern attributes of importance based on uncertain ontological information. However, the proposed approach has been implemented in the Protege ontology editor environment and requires human intervention to specify how the BN model will be generated. The fully automated generation of ontology-based antipattern BN models still remains an open issue. SPARSE is an OWL ontology based intelligent system that assists software project managers in the antipattern detection process. In this paper, we propose the use of the resulting detected antipatterns of SPARSE, their attributes (i.e. causes, symptoms, consequences) and the ontological relationships between these attributes, in order to automatically generate BN models of the detected antipatterns. We illustrate how this approach can be implemented using an example of 8 antipattern attributes of 6 inter-related antipatterns detected using SPARSE.

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.

A method for solving the fault diagnosis problem of air brake system based on probabilistic approach is presented. The fault diagnosis model based on Bayesian network was built for the uncertainty characteristic of fault in the air brake system. Through evaluating the characteristic of Bayesian networks in the diagnosis inference and model expression, it is demonstrated that this method can solve the uncertain problems in fault diagnosis. The test result has shown that the Bayesian network model is effective in fault diagnosis of the air brake system.

Similarity measure is an important tool in pattern recognition and fault diagnosis. This paper proposes two cotangent similarity measures for single-valued neutrosophic sets (SVNSs) based on cotangent function. Then, the weighted cotangent similarity measures are introduced by considering the importance of each element. Moreover, by the comparison between the cotangent similarity measures of SVNSs and existing cosine similarity measure of SVNSs, the developed cotangent similarity measures demonstrate their advantages and rationality and in some cases can overcome some disadvantages of the cosine similarity measure defined in vector space. Finally, the cotangent similarity measures are applied to the fault diagnosis of steam turbine. The proposed fault diagnosis method demonstrates its effectiveness and rationality by the comparative analysis with the cosine similarity measure in the fault diagnosis of steam turbine.

Since the neutrosophic number consists of its determinate part d and its indeterminate part eI denoted by N = d + eI, it is very suitable for dealing with real problems with indeterminacy. Therefore, this paper proposes the exponential similarity measure of neutrosophic numbers and a fault diagnosis method of steam turbine by using the exponential similarity measure of neutrosophic numbers. By the similarity measure between the fault diagnosis patterns and a testing sample with neutrosophic numbers and its relation indices, we can determine the fault type and indicate fault trends. Then, the vibration fault diagnosis results of steam turbine demonstrate the effectiveness and rationality of the proposed diagnosis method. The proposed diagnosis method not only gives the main fault types of steam turbine but also provides useful information for multi-fault analyses and future fault trends. The developed diagnosis method is effective and reasonable in the fault diagnosis of steam turbine under an indeterminate environment.

To solve the problem of fault prediction and diagnosis of household air conditioning, an improved Bayesian network (BN) fault diagnosis model is proposed. Firstly, the orthogonal defect classification (ODC) is used to make statistics and analysis of air conditioning fault data, and the structure of BN fault diagnosis model is built based on the analysis results. Then, genetic algorithm (GA) is used to optimize the conditional probability of network nodes and determine the network parameters. Finally, the cooling and heating failure data of household air conditioning were taken as an example to diagnose. Compared with the traditional BN model, the accuracy of fault diagnosis increases from 81.13% to 92.83%, which verifies the effectiveness of the model.

A multivariate Bayesian network analysis of water quality factors influencing trihalomethanes formation in drinking water distribution systems

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets.

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

Stroke is a major chronic non-communicable disease with high incidence, high mortality, and high recurrence. To comprehensively digest its risk factors and take some relevant measures to lower its prevalence is of great significance. This study aimed to employ Bayesian Network (BN) model with Max-Min Hill-Climbing (MMHC) algorithm to explore the risk factors for stroke. From April 2019 to November 2019, Shanxi Provincial People's Hospital conducted opportunistic screening for stroke in ten rural areas in Shanxi Province. First, we employed propensity score matching (PSM) for class balancing for stroke. Afterwards, we used Chi-square testing and Logistic regression model to conduct a preliminary analysis of risk factors for stroke. Statistically significant variables were incorporated into BN model construction. BN structure learning was achieved using MMHC algorithm, and its parameter learning was achieved with Maximum Likelihood Estimation. After PSM, 748 non-stroke cases and 748 stroke cases were included in this study. BN was built with 10 nodes and 12 directed edges. The results suggested that age, fasting plasma glucose, systolic blood pressure, and family history of stroke constitute direct risk factors for stroke, whereas sex, educational levels, high density lipoprotein cholesterol, diastolic blood pressure, and urinary albumin-to-creatinine ratio represent indirect risk factors for stroke. BN model with MMHC algorithm not only allows for a complicated network relationship between risk factors and stroke, but also could achieve stroke risk prediction through Bayesian reasoning, outshining traditional Logistic regression model. This study suggests that BN model boasts great prospects in risk factor detection for stroke.

ObjectiveThis study aims to identify relevant risk factors, assess the interactions between variables, and establish a predictive model for ischemic stroke (IS) in patients with cardiac myxoma (CM) using the Bayesian network (BN) approach.MethodsData of patients with CM were collected from three tertiary comprehensive hospitals in Beijing from January 2002 to January 2022. Age, sex, medical history, and information related to CM were extracted from the electronic medical record system. The BN model was constructed using the tabu search algorithm, and the conditional probability of each node was calculated using the maximum likelihood estimation method. The probability of each node of the network and the interrelationship between IS and its related factors were qualitatively and quantitatively analyzed. A receiver operating characteristic (ROC) curve was also plotted. Sensitivity, specificity, and area under the curve (AUC) values were calculated and compared between the BN and logistic regression models to evaluate the efficiency of the predictive model.ResultsA total of 416 patients with CM were enrolled in this study, including 61 with and 355 without IS. The BN model found that cardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility were directly associated with the occurrence of IS in patients with CM. The BN model for predicting CM-IS achieved higher scores on AUC {0.706 [95% confidence interval (CI), 0.639–0.773]} vs. [0.697 (95% CI, 0.629–0.766)] and sensitivity (99.44% vs. 98.87%), but lower scores on accuracies (85.82% vs. 86.06%) and specificity (6.56% vs. 11.48%) than the logistic regression model.ConclusionCardiac symptoms, systemic embolic symptoms, platelet counts, and tumor with high mobility are candidate predictors of IS in patients with CM. The BN model was superior or at least non-inferior to the traditional logistic regression model, and hence is potentially useful for early IS detection, diagnosis, and prevention in clinical practice.

Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

BackgroundThis study aims to establish a Bayesian network risk prediction model for gastric cancer using data mining methods. It explores both direct and indirect factors influencing the incidence of gastric cancer and reveals the interrelationships among these factors.MethodsData were collected from early cancer screenings conducted at the People’s Hospital of Lincang between 2022 and 2023. Initial variable selection was performed using Least Absolute Shrinkage and Selection Operator (Lasso) and Sliding Windows Sequential Forward Selection (SWSFS), and the screened variables and demographic characteristics features were used as variables for constructing the Bayesian network (BN) model. Subsequently, the performance of the models was evaluated, and the optimal model was selected for network mapping and Bayesian inference using the best model.ResultsThe incidence rate of gastric cancer in this region’s high-risk population was determined to be 7.09%. The BN model constructed from the set of variables consisting of Lasso’s selection variables and demographic characteristics had better performance. A total of 12 variables were incorporated into the BN model to form a network structure consisting of 13 nodes and 18 edges. The model shows that age, gender, ethnicity, current address, upper gastrointestinal symptoms (nausea, acid reflux, vomiting), alcohol consumption, smoking, SGIM gastritis, and family history are important risk factors for gastric cancer development.ConclusionThe Bayesian network model provides an intuitive framework for understanding the direct and indirect factors contributing to the early onset of gastric cancer, elucidating the interrelationships among these factors. Furthermore, the model demonstrates satisfactory predictive performance, which may facilitate the early detection of gastric cancer and enhance the levels of early diagnosis and treatment among high-risk populations.

In this paper, we compare the efficiency of fault detection and diagnosis in networks having different topological properties, such as scale-free networks and Erdos-Renyi random graphs. Efficiency measures include both the number of tests (e.g., end-to-end network probes) necessary for diagnosis and the computational complexity of diagnosis. We observe that diagnosis in scale-free networks typically requires significantly larger number of tests than diagnosis in random networks. However, the computational complexity of diagnosis appears to be much lower for scale-free networks since the corresponding Bayesian network models used for probabilistic diagnosis tend to have much lower induced width - a topological parameter controlling the complexity of inference in Bayesian networks. We believe that our observations provide important insights for design and deployment of cost-efficient diagnostic methods in computer networks and distributed systems.

Minimum training sample size requirements for achieving high prediction accuracy with the BN model: A case study regarding seismic liquefaction