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27 results for “Fault detection”
ADAPTIVE FAULT DETECTION ON LIQUID PROPULSION SYSTEMS WITH VIRTUAL SENSORS: ALGORITHMS AND ARCHITECTURES
Prior to the launch of STS-119 NASA had completed a study of an issue in the flow control valve (FCV) in the Main Propulsion System of the Space Shuttle using an adaptive learning method known as Virtual Sensors. Virtual Sensors are a class of algorithms that estimate the value of a time series given other potentially nonlinearly correlated sensor readings. In the case presented here, the Virtual Sensors algorithm is based on an ensemble learning approach and takes sensor readings and control signals as input to estimate the pressure in a subsystem of the Main Propulsion System. Our results indicate that this method can detect faults in the FCV at the time when they occur. We use the standard deviation of the predictions of the ensemble as a measure of uncertainty in the estimate. This uncertainty estimate was crucial to understanding the nature and magnitude of transient characteristics during startup of the engine. This paper overviews the Virtual Sensors algorithm and discusses results on a comprehensive set of Shuttle missions and also discusses the architecture necessary for deploying such algorithms in a real-time, closed-loop system or a human-in-the-loop monitoring system. These results were presented at a Flight Readiness Review of the Space Shuttle in early 2009.
A knowledge-based system approach for sensor fault modeling, detection and mitigation
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications
Sensor faults continue to be a major hurdle for sys- tems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally dif- ficult to seed particular sensor faults. Therefore, research is un- derway to better understand the different fault modes seen in sen- sors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper il- lustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vi- bration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classi- fier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and dis- ambiguation efficacy with respect to severity of fault conditions.
SubsurfaceBreaks v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces: Input and Processed Data
<p>This companion dataset relates to the manuscript "<strong>SubsurfaceBreaks</strong> <strong>v. 1.0: A supervised detection of fault-related structures on triangulated models of subsurface homoclinal interfaces"</strong>, by Michał Michalak, Christian Gerhards and Peter Menzel.</p> <p>There are several groups of files:</p> <ul> <li>a file with parameters (params.txt) of the generated homoclinal interfaces (slopes) such as dip angle, dip direction, level of noise).</li> <li>files 0-999 are generated using the code from GitHub. (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_synthetic_subsurface_slopes) for generating synthetic slopes. Every slope is in a separate file (.txt files) and it is possible to upload the slope to ParaView for further inspection: Delaunay triangulation, normal vectors and dip vectors have their own .vtu files. The .txt files (0-999) can be uploaded for training using the Python script (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_subsurface_slopes_training_testing_evaluating_revision.ipynb).</li> <li>KSH_input.txt corresponds to real data from Kraków-Silesian Homocline. Every row corresponds to a point representing a geological horizon separating Middle Jurassic geological units: Kościeliska sandstones from ore-bearing clays. This data set can be used to calculate geometric attributes using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes).</li> <li>KSH_input_output_0 corresponds to an output file from processing the KSH_input.txt file using the code from GitHub (https://github.com/michalmichalak997/SubsurfaceBreaks/blob/main/Broken_real_subsurface_slopes). This file should be uploaded to the Python script to identify fault-related features on a real subsurface slope.</li> </ul>
Switched capacitor filter transient responses for fault detection
<h2>Dataset Description</h2> <p>This dataset comprises both measured and simulated responses of second-order switched capacitor filters designed for failure detection. The study encompasses two types of filters:</p> <ul> <li> Overdamped</li> <li> Underdamped</li> </ul> <p>The measured data is segregated into two subsets:</p> <h3>Training Data</h3> <p>The training data exclusively contains non-failure responses. It serves as the foundation for model training and validation.</p> <h3>Test Data</h3> <p>The test data includes both failure and non-failure responses. Each response is labeled to indicate whether it is a non-failure or failure response, specifying which capacitor has been modified and its new value.</p> <p>For the simulated data, it is exclusively utilized for testing purposes. For each simulated case (% deviation of the nominal value), a metadata file named "*_metadata.csv" is provided. Each row in this file meticulously outlines the characteristics of the response corresponding to the elements in the accompanying .npy file.</p>
Conveyor Belt Idlers Acoustic Data set for Fault detection
<p>This dataset collecting the acoustic signal from 135 idlers of conveyor in real mine by using inspection robot. More information in details about this data is provide in this paper " <strong>Inspection Robotic UGV Platform and the Procedure for an Acoustic Signal-Based Fault Detection in Belt Conveyor Idler</strong>"</p> <p> </p>
vibro-acoustic data from test rig for fault detection
<p>This data set is include acoustic and vibration signal collected from test rig for fault detection purpose. More information in details about this data set is provided in following paper :</p> <p>"</p> <p><strong>Analysis of the vibro-acoustic data from test rig -comparison of acoustic and vibrational method</strong></p> <p>"</p>
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