The Impact of Automated Fault Detection and Extraction Technology on Seismic Interpretation

2019 ◽  
Author(s):  
AbdulMohsin A. Al-Maskeen ◽  
Sadaqat S. Ali ◽  
Muhammad Khan
Keyword(s):  
Fault Detection ◽  
Seismic Interpretation ◽  
Extraction Technology ◽  
The Impact

scholarly journals Hybrid Active-Passive Robust Fault-Tolerant Control of Event-Triggered Nonlinear NCS

2017 ◽  
Vol 11 (1) ◽  
pp. 68-86 ◽  
Author(s):  
Jun Wang ◽  
Xiaowan Yao ◽  
Wei Li
Keyword(s):  
Fault Detection ◽  
Fault Tolerant ◽  
Discrete Event ◽  
Integrated Design ◽  
Fuzzy Model ◽  
Fault Tolerant Control ◽  
Linear Matrix ◽  
Nonlinear Networked Control Systems ◽  
The Impact ◽  
Event Triggered

In this paper, the authors aimed to analyze uncertain nonlinear networked control systems (NCS) under discrete event-triggered communication scheme (DETCS), in which an integrated design methodology between robust fault detection observer and active fault-tolerant controller is proposed. Moreover, the problem of hybrid active–passive robust fault-tolerant control, which integrated passive fault-tolerant control, fault detection, and controller reconstruction, is researched. In consideration of the impact of uncertainties and network-induced delay on system performance, a new class of uncertain nonlinear NCS fault model is established based on T-S fuzzy model. By employing Lyapunov stability theory, H∞ control theory, and linear matrix inequality method, the fault detection observer and hybrid fault-tolerant controller are both appropriately designed. In addition, the sufficient condition that guaranteed the asymptotically robust stability of nonlinear NCS against any actuator failures is deduced. Finally, a numerical simulation is provided to show the effectiveness of the proposed methods.

Adaptive Stochastic Resonance Method for Early Fault Detection of Rotating Machinery Based on Alpha Stable Distribution

2014 ◽  
Vol 618 ◽  
pp. 458-462
Author(s):  
Gang Yu ◽  
Ye Chen
Keyword(s):  
Fault Detection ◽  
Stochastic Resonance ◽  
Stable Distribution ◽  
Signal To Noise Ratio ◽  
Resonance Method ◽  
Rotating Machinery ◽  
Sliding Windows ◽  
Alpha Stable Distribution ◽  
Early Fault Detection ◽  
The Impact

This paper proposes an adaptive stochastic resonance (SR) method based on alpha stable distribution for early fault detection of rotating machinery. By analyzing the SR characteristic of the impact signal based on sliding windows, SR can improve the signal to noise ratio and is suitable for early fault detection of rotating machinery. Alpha stable distribution is an effective tool for characterizing impact signals, therefore parameter alpha can be used as the evaluating parameter of SR. Through simulation study, the effectiveness of the proposed method has been verified.

Research on Fault Diagnosis of Steam Turbine Rotor Unbalance and Parallel Misalignment Based on Numerical Simulation and Convolutional Neural Network

2021 ◽  
Author(s):  
Chongyu Wang ◽  
Di Zhang ◽  
Yonghui Xie
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Fault Detection ◽  
Steam Turbine ◽  
Turbine Rotor ◽  
Detection Accuracy ◽  
Steam Turbine Rotor ◽  
Parallel Misalignment ◽  
Coupling Fault ◽  
The Impact

Abstract The steam turbine rotor is still the main power generation equipment. Affected by the impact of new energy on the power grid, the steam turbine needs to participate in peak load regulation, which will make turbine rotor components more prone to failure. The rotor is an important equipment of a steam turbine. Unbalance and misalignment are the normal state of rotor failure. In recent years, more and more attention has been paid to the fault detection method based on deep learning, which takes rotating machinery as the object. However, there is a lack of research on actual steam turbine rotors. In this paper, a method of rotor unbalance and parallel misalignment fault detection based on residual network is proposed, which realizes the end-to-end fault detection of rotor. Meanwhile, the method is evaluated with numerical simulation data, and the multi task detection of rotor unbalance, parallel misalignment, unbalanced parallel misalignment coupling faults (coupling fault called in this paper) is realized. The influence of signal-to-noise ratio and the number of training samples on the detection performance of neural network is discussed. The detection accuracy of unbalanced position is 93.5%, that of parallel misalignment is 99.1%. The detection accuracy for unbalance and parallel misalignment is 89.1% and 99.1%, respectively. The method can realize the direct mapping between the unbalanced, parallel misalignment, coupling fault vibration signals and the fault detection results. The method has the ability to automatically extract fault features. It overcomes the shortcoming of traditional methods that rely on signal processing experience, and has the characteristics of high precision and strong robustness.

A Method of Software Static Testing in the Presence of Runtime Exception

2011 ◽  
Vol 63-64 ◽  
pp. 789-794
Author(s):  
Da Hai Jin ◽  
Yun Zhan Gong ◽  
Zhao Hong Yang ◽  
Qing Xiao ◽  
Chuan Chang Liu
Keyword(s):  
Fault Detection ◽  
Finite State Machine ◽  
False Negative ◽  
Control Flow ◽  
Control Flow Graph ◽  
Flow Graph ◽  
Static Testing ◽  
Testing Method ◽  
Finite State ◽  
The Impact

Control flow graph plays an important role in software static testing based on defect patterns, while the impact of runtime exception on control flow graph is not negligible. After the runtime exception control flow graph and exception pattern Finite State Machine were defined, an algorithm for fault detection in the presence of runtime exception was proposed. Basing on FSM for exception pattern, the feasible states and its condition are iterated along the node of control flow graph, while the abnormal status, which can throw a runtime exception, will be added into control flow graph as an edge automatically. Thus the static testing method can detect more defects by the control flow graph constructed dynamically. The experiment results show that, the static testing method in the presence of runtime exception can decrease defect false negative significantly.

Thin beds, tuning, and AVO

2014 ◽  
Vol 33 (12) ◽  
pp. 1394-1396 ◽  
Author(s):  
Wes Hamlyn
Keyword(s):  
Seismic Interpretation ◽  
Avo Analysis ◽  
Seismic Amplitudes ◽  
The Impact

In this tutorial, we will explore two topics that are particularly relevant to quantitative seismic interpretation — thin-bed tuning and AVO analysis. Specifically, we will examine the impact of thin beds on prestack seismic amplitudes and subsequent effects on AVO attribute values.

scholarly journals Fault Detection for Gas Turbine Hot Components Based on a Convolutional Neural Network

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2149 ◽  
Author(s):  
Jiao Liu ◽  
Jinfu Liu ◽  
Daren Yu ◽  
Myeongsu Kang ◽  
Weizhong Yan ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Gas Turbine ◽  
Detection Sensitivity ◽  
Large Temperature ◽  
Gas Temperature ◽  
Ambient Conditions ◽  
Real World Data ◽  
The Impact

Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.

scholarly journals A Comprehensive Modeling of Centrifugal Compressor Vibrations for Early Fault Detection

2020 ◽  
Author(s):  
François Libeyre ◽  
Francis Bainier ◽  
Pascal Alas
Keyword(s):  
Fault Detection ◽  
Centrifugal Compressor ◽  
Historical Data ◽  
Flow Coefficient ◽  
Clear Correlation ◽  
Physical Parameters ◽  
Centrifugal Compressors ◽  
Usual Condition ◽  
Early Fault Detection ◽  
The Impact

Abstract In the last decade, the development of machine connectivity has made possible early fault detection with remote analysis of operating data. Solutions aiming to reduce maintenance costs and production losses due to unplanned downtimes were brought to market. These solutions provide with a model of the equipment in healthy conditions using machine learning techniques applied on historical data. During operation a warning is issued when expected and actual measurements do not match. Although these solutions have proven their value to detect abnormal behaviors, they generate a large number of alarms that require resource to be analyzed. Moreover, these solutions rely on a large number of sensors that need to work correctly both for the learning and the monitoring phase. This generates additional maintenance even though these sensors are often not essential to operate the machine. Lastly the solutions are expensive: their application is usually limited to critical machines with risks of production loss. Indeed, they are not economic for a Transmission System Operator that has ensured the availability of its network with redundancy. The objective of the authors was to focus on the monitoring of radial vibrations of centrifugal compressors. Experience proves this is one of the most critical data for early fault detection. The goal was to develop a smart modelling based on historical data using essential parameters influencing rotor-dynamics. As a result, a clear correlation was found between the operating point and the vibration level. That can be easily shown on a centrifugal compressor map. A second-degree polynomial equation was successfully tested. The model equation relies only on two compressor physics parameters: flow coefficient and speed. We discuss in the paper the impact of other essential parameters. The method has been applied on different type of centrifugal compressors, with different bearing technology (magnetic...) or shaft driving equipment (gas turbine, electric motor drive). A fault detection case study using this method is described, eg: vibration variation due to abnormal opening of an anti-surge control valve. In conclusion this method is a simple alternative to usual condition monitoring solutions. Similarly to what was described in the GT2014-25242 for a Predictive Emission Monitoring System [1], equations based on physical parameters prove to be an efficient modelling technique. Moreover, it helps monitoring teams to better understand the underlying relation between parameters. Indeed, to achieve a complete monitoring of a centrifugal compressor health, this method can be combined with first-principle performance models that use the same physical parameters.

scholarly journals Fault detection and classification in Industrial IoT in case of missing sensor data

2021 ◽  
Author(s):  
Merim Dzaferagic ◽  
Nicola Marchetti ◽  
Irene Macaluso
Keyword(s):  
Fault Detection ◽  
Monitoring System ◽  
Sensor Data ◽  
Generative Adversarial Networks ◽  
Missing Measurements ◽  
Tennessee Eastman Process ◽  
Fault Detection And Classification ◽  
Industrial Iot ◽  
Critical Components ◽  
The Impact

This paper addresses the issue of reliability in Industrial Internet of Things (IIoT) in case of missing sensors measurements due to network or hardware problems. We propose to support the fault detection and classification modules, which are the two critical components of a monitoring system for IIoT, with a generative model. The latter is responsible of imputing missing sensor measurements so that the monitoring system performance is robust to missing data. In particular, we adopt Generative Adversarial Networks (GANs) to generate missing sensor measurements and we propose to fine-tune the training of the GAN based on the impact that the generated data have on the fault detection and classification modules. We conduct a thorough evaluation of the proposed approach using the extended Tennessee Eastman Process dataset. Results show that the GAN-imputed data mitigate the impact on the fault detection and classification even in the case of persistently missing measurements from sensors that are critical for the correct functioning of the monitoring system.

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