scholarly journals Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

2021 ◽  
Vol vm02 (is01) ◽  
pp. 15-21
Author(s):  
Dinh-dung Nguyen ◽  
Hong Son Tran ◽  
Thi Thuy Tran ◽  
Dat Dang Quoc ◽  
Hong Tien Nguyen
Keyword(s):  
Fault Diagnosis ◽  
Angular Velocity ◽  
Fault Detection ◽  
Intelligent Systems ◽  
Fault Detection And Diagnosis ◽  
Process Models ◽  
State Observers ◽  
Improvement Method ◽  
Detection And Diagnosis ◽  
Sensor Detection

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

Method on the Fault Detection and Diagnosis for the Railway Turnout Based on the Current Curve of Switch Machine

2013 ◽  
Vol 427-429 ◽  
pp. 1022-1027 ◽  
Author(s):  
Xue Mei Mo ◽  
Yu Fang ◽  
Yun Guo Yang
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Curve Matching ◽  
Reference Curve ◽  
Current Curve ◽  
Diagnosis Method ◽  
Detection And Diagnosis ◽  
Railway Turnout

This paper proposes a method of the fault detection and diagnosis for the railway turnout based on the current curve of switch machine. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted in the paper. Based on envelope and morpheme match algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Moreover, the SVM-based fault diagnosis method is used to make sure that the fault conditions could be diagnosed intelligently. Finally, the experimental results show that the proposed method can accurately identify the turnout fault status in the conversion process, and the accuracy rate in the diagnosis of the fault location is above 98%, which verify the effectiveness of the method in the fault detection and diagnosis.

scholarly journals Fault Detection and Diagnosis in Industrial Processes with Variational Autoencoder: A Comprehensive Study

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 227
Author(s):  
Jinlin Zhu ◽  
Muyun Jiang ◽  
Zhong Liu
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Process Monitoring ◽  
Industrial Process ◽  
Fault Detection And Diagnosis ◽  
Propagation Mechanism ◽  
Tennessee Eastman Process ◽  
Variational Autoencoder ◽  
Detection And Diagnosis ◽  
Detection Schemes

This work considers industrial process monitoring using a variational autoencoder (VAE). As a powerful deep generative model, the variational autoencoder and its variants have become popular for process monitoring. However, its monitoring ability, especially its fault diagnosis ability, has not been well investigated. In this paper, the process modeling and monitoring capabilities of several VAE variants are comprehensively studied. First, fault detection schemes are defined in three distinct ways, considering latent, residual, and the combined domains. Afterwards, to conduct the fault diagnosis, we first define the deep contribution plot, and then a deep reconstruction-based contribution diagram is proposed for deep domains under the fault propagation mechanism. In a case study, the performance of the process monitoring capability of four deep VAE models, namely, the static VAE model, the dynamic VAE model, and the recurrent VAE models (LSTM-VAE and GRU-VAE), has been comparatively evaluated on the industrial benchmark Tennessee Eastman process. Results show that recurrent VAEs with a deep reconstruction-based diagnosis mechanism are recommended for industrial process monitoring tasks.

scholarly journals Research on SDG Fault Diagnosis of Ocean Shipping Boiler System Based on Fuzzy Granular Computing Under Data Fusion

2018 ◽  
Vol 25 (s2) ◽  
pp. 92-97 ◽  
Author(s):  
Ying Zhu ◽  
Liang Geng
Keyword(s):  
Graph Theory ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Granular Computing ◽  
Information Science ◽  
Research Work ◽  
Research Field ◽  
Fault Detection And Diagnosis ◽  
Ocean Shipping ◽  
Detection And Diagnosis

Abstract The research work in this paper belongs to the application of granular computing, graph theory and its application in fault detection and diagnosis. It is a cross cutting and frontier research field in computer science, information science and graph theory. The results of this paper are of great significance to the application of the fault detection and diagnosis of the ocean boilers system. This research combines granular computing theory and signed directed graph, and proposes a new method of fault diagnosis, and applies it to the fault diagnosis of ocean ship boiler system.

Operational Reliability Improvement of Plants Through Intelligent Fault Detection and Diagnosis

2003 ◽  
Author(s):  
E. Ricky Odoom
Keyword(s):  
Fault Detection ◽  
Intelligent Systems ◽  
Operational Reliability ◽  
Fault Detection And Diagnosis ◽  
Complex Dynamic ◽  
Artificial Intelligent ◽  
Reliability Improvement ◽  
Process Plants ◽  
Basic Concepts ◽  
Detection And Diagnosis

Real-time Fault Detection and Diagnosis of modern dynamic process plants are continuously receiving increasing attention both theoretically and practically. In recent years, attempts have been made to apply Artificial Intelligence techniques to the Fault Detection Diagnosis task for improving the operational reliability of complex dynamic plants. The aim of this paper is to discuss the basic concepts, issues and tools of some of the emerging intelligence technologies for Fault Detection and Diagnosis schemes. The emphasis is given to the methods, which are based on Artificial Intelligent systems and which are appropriate for diagnosing faults in complex dynamic plants.

scholarly journals Review on Transformer Fault Detection and Diagnosis Algorithms

2021 ◽  
pp. 27-33
Author(s):  
Mohammed Misbahul Islam ◽  
Mrs. Madhu Upadhyay
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Power Transformer ◽  
Gas Analysis ◽  
Fault Detection And Diagnosis ◽  
Electrical Network ◽  
Dissolved Gas ◽  
On Line ◽  
Detection And Diagnosis ◽  
Transformer Fault

The method of fault diagnosis based on dissolved gas analysis (DGA) is of great importance to detect possible failures in the transformer and to improve the safety of the electrical system. The DGA data of the transformer in the smart grid has the characteristics of a large amount, different types and a low density of values. Since the power transformer is an important type of power supply in the electrical network, this document provides a complete overview of the power transformer and describes how to diagnose faults. Furthermore, on-line monitoring, the method of fault diagnosis and condition-based maintenance strategy decision-making method as also have been described. The paper presents detailed literature on the recent advancements and methods being adopted by various authors on fault detection.

scholarly journals Condition monitoring of railway pantographs to achieve fault detection and fault diagnosis

2018 ◽  
Vol 234 (3) ◽  
pp. 289-300 ◽  
Author(s):  
Tingyu Xin ◽  
Clive Roberts ◽  
Paul Weston ◽  
Edward Stewart
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Frequency Response ◽  
Condition Monitoring ◽  
Fault Detection And Diagnosis ◽  
Pneumatic Actuator ◽  
Rolling Stock ◽  
Test Rig ◽  
Response Test ◽  
Detection And Diagnosis

Railway pantographs are used around the world for collecting electrical energy to power railway vehicles from the overhead catenary. Faults in the pantograph system degrade the quality of the contact between the pantograph and catenary and reduce the reliability of railway operations. To maintain the pantographs in a good working condition, regular inspection tasks are carried out at rolling stock depots. The current pantograph inspections, in general, are only effective for the detection of major faults, providing limited incipient fault detection or fault diagnosis capabilities. Condition monitoring of pantographs has the potential to improve pantograph performance and reduce maintenance costs. As a first step in the realisation of practical pantograph condition monitoring, a laboratory-based pantograph test rig has been developed to gain an understanding of pantograph dynamic behaviours, particularly when incipient faults are present. In the first work of this kind, dynamic response data have been acquired from a number of pantographs that have allowed fault detection and diagnosis algorithms to be developed and verified. Three tests have been developed: (i) a hysteresis test that uses different excitation speeds, (ii) a frequency response test that uses different excitation frequencies, and (iii) a novel changing gradient test. Verification tests indicate that the hysteresis test is effective in detecting and diagnosing pneumatic actuator and elbow joint faults. The frequency response test is able to monitor the overall degradation in the pantograph. The changing gradient test provides fault detection and diagnosis in the pantograph head suspension and pneumatic actuator. The test rig and fault detection and diagnosis algorithms are now being developed into a depot-based prototype together with a number of industrial partners.

Method on Fault Detection and Diagnosis for Track Circuit Based on Main Rail Voltage

2014 ◽  
Vol 670-671 ◽  
pp. 1172-1178
Author(s):  
Da Zhuang Wu ◽  
Yu Fang ◽  
Quan Song Ma
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Location ◽  
Fault Detection And Diagnosis ◽  
Fault Classification ◽  
Curve Matching ◽  
Reference Curve ◽  
Diagnosis Method ◽  
Track Circuit ◽  
Detection And Diagnosis

This paper proposes a method of the fault detection and diagnosis for the railway circuit of ZPW-2000 system based on the main track voltage curve. Exact curve matching fault detection method and SVM-based fault diagnosis method are adopted. Based on envelope algorithm, exact curve matching method is used to match the detected current curve with the reference curve so as to predict whether the curve would have fault or not. Then, the SVM-based fault diagnosis method is used to make sure that the fault classification could be diagnosed intelligently. The experiment results show that the proposed method can accurately identify the track circuit fault state, and the accuracy rate in the diagnosis of the fault location is above 99%, which verify the effectiveness of the method in the fault detection and diagnosis.

Investigation of Sensor Fault Diagnosis in Air Handling Units Based on Wavelet Energy Entropy

2013 ◽  
Vol 645 ◽  
pp. 316-319
Author(s):  
Su Ping Li ◽  
Yao Ling Fan
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Measurement Data ◽  
Fault Detection And Diagnosis ◽  
Measured Signal ◽  
Wavelet Energy ◽  
Energy Entropy ◽  
Air Handling Units ◽  
Detection And Diagnosis ◽  
Wavelet Energy Entropy

This paper presents a novel fault diagnosis method for sensors in air-handling units based on wavelet energy entropy. Instead of directly comparing the numerous data under noise conditions, the wavelet energy entropy deviation is used for the fault detection and diagnosis. The actual Three-level wavelet analysis is used to decompose the measurement data captured from sensors first and then the concept of Shannon entropy is referred to define the wavelet energy entropy. Once the wavelet energy entropy is obtained, whether the sensors are faulty can be confirmed through comparing the deviation of the wavelet energy entropy residual of the measured signal and the estimated one to the preset threshold. Testing results show that the wavelet energy entropy is a sensitive indictor to diagnose the sensor faults. The deviations of wavelet energy entropy of sensors under fault-free conditions and faulty ones all exceed the threshold. The severer the fault is, the larger the residuals of the wavelet energy entropy will be. The results prove that the proposed method is valid and effective for the fault detection and diagnosis of the sensors.

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