scholarly journals Information Value-Based Fault Diagnosis of Train Door System under Multiple Operating Conditions

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3952
Author(s):  
Seokgoo Kim ◽  
Nam Ho Kim ◽  
Joo-Ho Choi
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Fault Isolation ◽  
Information Value ◽  
Operating Conditions ◽  
Data Driven ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Operating Regimes

While there are many data-driven diagnosis algorithms for fault isolation of complex systems, a new challenge arises in the case of multiple operating regimes. In this case, the diagnosis is usually carried out for each regime for better accuracy. However, the problem is that different results can be derived from each regime and they can conflict with each other, which may invalidate the performance of fault diagnosis. To address this challenge, a methodology for selecting the most reliable one among the different diagnostic results is proposed, which combines the Bayesian network (BN) and the information value (IV). The BN is trained for each regime and a conditional probability table is obtained for probabilistic fault diagnosis. The IV is then employed to evaluate the value of several diagnostic results. The proposed approach is applied to the fault diagnosis of a train door system and its effectiveness is proven.

Research on Power System Fault Diagnosis Based on Bayesian Networks

2013 ◽  
Vol 347-350 ◽  
pp. 1930-1934 ◽  
Author(s):  
Guo Feng Yang ◽  
Qing Ming Xiao ◽  
Hong Ouyang ◽  
Jia Kui Zhao ◽  
Ting Shun Li ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Power System ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Structure Learning ◽  
Parameter Learning ◽  
Conditional Probability Table ◽  
Probability Table ◽  
Diagnosis Model ◽  
Diagnosis Approach

Aiming at the incompleteness and uncertainty of information existing in power system fault diagnosis, a new fault diagnosis approach based on Bayesian network is proposed in this paper. Through the Bayesian network of structure learning and parameter learning, a power system fault diagnosis model based on Bayesian network has been proposed. Conditional probability table describes the connection degree between various factors in quantity. Diagnostic results of instance proved the effectiveness and superiority of the proposed method.

scholarly journals Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System

2019 ◽  
Vol 9 (4) ◽  
pp. 783 ◽  
Author(s):  
Silvio Simani ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Offshore Wind ◽  
Data Driven ◽  
Offshore Wind Turbine ◽  
Wind Turbine System ◽  
High Degree ◽  
Offshore Plants

Fault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines.

Data-driven fault diagnosis in SOFC-based power plants under off-design operating conditions

2019 ◽  
Vol 44 (54) ◽  
pp. 29002-29006 ◽  
Author(s):  
Paola Costamagna ◽  
Andrea De Giorgi ◽  
Gabriele Moser ◽  
Lissy Pellaco ◽  
Andrea Trucco
Keyword(s):  
Fault Diagnosis ◽  
Power Plants ◽  
Operating Conditions ◽  
Data Driven

On the Diagnostic Methods of Bayesian-Network in Smart Grid

2011 ◽  
Vol 71-78 ◽  
pp. 2424-2428
Author(s):  
Han Mei Hu ◽  
Jun Lei Zhao ◽  
Ping Wen Tu
Keyword(s):  
Fault Diagnosis ◽  
Smart Grid ◽  
Bayesian Network ◽  
Real Time ◽  
Fault Tolerant ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Self Healing ◽  
Diagnosis Method ◽  
Time Accuracy

Aiming at the smart grid self-healing characteristics, puts forward a Bayesian network fault diagnosis method. According to the protection movement signal and the circuit breaker tripping signal, establish the face of components of the smart grid line fault diagnosis model. The fault diagnosis method is real-time and accuracy, and fault-tolerant ability etc. characteristics. This method not only satisfy intelligent power grid self-healing characteristics on fault diagnosis real-time, accuracy and automatic fault diagnosis of the requirements, but also provide the smart grid fault isolation and system of self recover with strong guarantee.

scholarly journals A Comprehensive Case Study of Data-Driven Methods for Robust Aircraft Sensor Fault Isolation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1645
Author(s):  
Nicholas Cartocci ◽  
Marcello R. Napolitano ◽  
Gabriele Costante ◽  
Mario L. Fravolini
Keyword(s):  
Fault Diagnosis ◽  
Fault Isolation ◽  
Data Driven ◽  
Fault Estimation ◽  
Linear Regression Models ◽  
Sensor Fault ◽  
Fault Reconstruction ◽  
Air Speed ◽  
Using Data

Recent catastrophic events in aviation have shown that current fault diagnosis schemes may not be enough to ensure a reliable and prompt sensor fault diagnosis. This paper describes a comparative analysis of consolidated data-driven sensor Fault Isolation (FI) and Fault Estimation (FE) techniques using flight data. Linear regression models, identified from data, are derived to build primary and transformed residuals. These residuals are then implemented to develop fault isolation schemes for 14 sensors of a semi-autonomous aircraft. Specifically, directional Mahalanobis distance-based and fault reconstruction-based techniques are compared in terms of their FI and FE performance. Then, a bank of Bayesian filters is proposed to compute, in flight, the fault belief for each sensor. Both the training and the validation of the schemes are performed using data from multiple flights. Artificial faults are injected into the fault-free sensor measurements to reproduce the occurrence of failures. A detailed evaluation of the techniques in terms of FI and FE performance is presented for failures on the air-data sensors, with special emphasis on the True Air Speed (TAS), Angle of Attack (AoA), and Angle of Sideslip (AoS) sensors.

scholarly journals Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1537
Author(s):  
Hossein Varvani Farahani ◽  
Afshin Rahimi
Keyword(s):  
Fault Diagnosis ◽  
Attitude Control ◽  
Missing Values ◽  
Fault Isolation ◽  
Data Driven ◽  
Support Vector ◽  
Satellite Mission ◽  
Control Moment Gyroscope ◽  
Control Moment ◽  
Satellite Control

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications.

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