Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.

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A survey on data-driven process monitoring and diagnostic methods for variation reduction in multi-station assembly systems

Assembly Automation ◽

10.1108/aa-10-2018-0174 ◽

2019 ◽

Vol 39 (4) ◽

pp. 727-739 ◽

Cited By ~ 2

Author(s):

Yinhua Liu ◽

Rui Sun ◽

Sun Jin

Keyword(s):

Data Mining ◽

Quality Control ◽

Fault Diagnosis ◽

Process Monitoring ◽

Information And Communications Technology ◽

Diagnostic Methods ◽

Data Driven ◽

Assembly Systems ◽

Content Type ◽

Variation Reduction

PurposeDriven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.Design/methodology/approachThis paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.FindingsA classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.Originality/valueThis paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

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A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

Journal of Engineering for Gas Turbines and Power ◽

10.1115/1.4030277 ◽

2015 ◽

Vol 137 (10) ◽

Cited By ~ 18

Author(s):

Dengji Zhou ◽

Huisheng Zhang ◽

Shilie Weng

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Gas Turbine ◽

Small Sample ◽

Diagnostic Methods ◽

Data Driven ◽

Support Vector ◽

Artificial Neural ◽

Gas Path Fault ◽

Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, data-driven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for data-driven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample.

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Rotating Shaft Fault Prediction Using Convolutional Neural Network: A Preliminary Study

Journal of KONES ◽

10.2478/kones-2019-0060 ◽

2019 ◽

Vol 26 (3) ◽

pp. 75-81

Author(s):

Davor Kolar ◽

Dragutin Lisjak ◽

Michał Pająk

Keyword(s):

Deep Learning ◽

Fault Diagnosis ◽

High Performance ◽

Expert Knowledge ◽

Technical State ◽

Fault Prediction ◽

Data Driven ◽

High Definition ◽

Rotating Shaft ◽

Signal Features

Abstract One of the most important subsystems of the vehicles and machines operating currently in industry and transportation are the rotating subsystems. During the operation, due to the forcing factors influence, the technical state of them is changing and the failure can occur. Fault diagnosis is maintenance task considered as an essential in such subsystems, since possibility of an early detection and diagnosis of the faulty condition can save both time and money. To do this the analysis of the subsystems vibrations is performed. The identified technical state should be considered in a context of the ability and different inability states. Therefore, the first step of the diagnostic procedure is the ability and different inability states identification. Traditional data-driven techniques of fault diagnosis require signal processing for feature extraction, as they are unable to work with raw signal data, consequently leading to need for both expert knowledge and human work. The emergence of deep learning architectures in condition-based maintenance promises to ensure high performance fault diagnosis while lowering necessity for expert knowledge and human work. This article presents authors initial research in deep learning-based data-driven fault diagnosis of rotating subsystems. The proposed technique input raw three-axis accelerometer signal as high-definition image into deep learning layers, which automatically extract signal features, enabling high classification accuracy.

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Implementation of Stochastic Methods for Industrial Gas Turbine Fault Diagnosis

Volume 1: Turbo Expo 2005 ◽

10.1115/gt2005-68739 ◽

2005 ◽

Cited By ~ 1

Author(s):

C. Romessis ◽

K. Mathioudakis

Keyword(s):

Fault Diagnosis ◽

Gas Turbine ◽

Gas Turbines ◽

Bayesian Belief Networks ◽

Diagnostic Methods ◽

Stochastic Methods ◽

Test Cases ◽

Industrial Gas Turbines ◽

Component Faults ◽

Industrial Gas Turbine

Implementation of stochastic diagnostic methods for diagnosis of sensor or component faults is presented. Two industrial gas turbines are considered as test cases, one twin and one single shaft arrangement. Methods based on Probabilistic Neural Networks (PNN) and Bayesian Belief Networks (BBN), are implemented. The ability for successful diagnosis is demonstrated on specific cases of sensor malfunctions, as well as on two types of compressor deterioration, fouling and variable vane mistuning. The examined diagnostic problem and the methods of PNN for sensor fault diagnosis and BBN for the diagnosis of component faults are first described. For each gas turbine case, the implementation of the diagnostic methods is shown and application to fault cases that occurred is presented. The effectiveness of the stochastic diagnostic methods demonstrates that they offer a powerful alternative diagnostic tool.

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Implementation of Fuzzy Logic Control on Battery Charging System

ACMIT Proceedings ◽

10.33555/acmit.v3i1.34 ◽

2019 ◽

Vol 3 (1) ◽

pp. 118-126 ◽

Cited By ~ 1

Author(s):

Prihangkasa Yudhiyantoro

Keyword(s):

Complex System ◽

Fuzzy Logic ◽

Fuzzy Logic Control ◽

Expert Knowledge ◽

Experimental Result ◽

Rule Base ◽

Battery Charging ◽

Charging System ◽

Logic Control ◽

Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the embership function and the rule base of the fuzzy controller.

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Cancer Biomarker Discovery for Precision Medicine: New Progress

Current Medicinal Chemistry ◽

10.2174/0929867325666180718164712 ◽

2020 ◽

Vol 26 (42) ◽

pp. 7655-7671 ◽

Cited By ~ 4

Author(s):

Jinfeng Zou ◽

Edwin Wang

Keyword(s):

Precision Medicine ◽

Cancer Patients ◽

Clinical Application ◽

Liquid Biopsy ◽

Biomarker Discovery ◽

Deep Understanding ◽

Genetic Mutation ◽

Data Driven ◽

Novel Approaches ◽

Functional Biomarkers

Background: Precision medicine puts forward customized healthcare for cancer patients. An important way to accomplish this task is to stratify patients into those who may respond to a treatment and those who may not. For this purpose, diagnostic and prognostic biomarkers have been pursued. Objective: This review focuses on novel approaches and concepts of exploring biomarker discovery under the circumstances that technologies are developed, and data are accumulated for precision medicine. Results: The traditional mechanism-driven functional biomarkers have the advantage of actionable insights, while data-driven computational biomarkers can fulfill more needs, especially with tremendous data on the molecules of different layers (e.g. genetic mutation, mRNA, protein etc.) which are accumulated based on a plenty of technologies. Besides, the technology-driven liquid biopsy biomarker is very promising to improve patients’ survival. The developments of biomarker discovery on these aspects are promoting the understanding of cancer, helping the stratification of patients and improving patients’ survival. Conclusion: Current developments on mechanisms-, data- and technology-driven biomarker discovery are achieving the aim of precision medicine and promoting the clinical application of biomarkers. Meanwhile, the complexity of cancer requires more effective biomarkers, which could be accomplished by a comprehensive integration of multiple types of biomarkers together with a deep understanding of cancer.

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