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12 results for “fault diagnosis”
MATLAB codes for : "Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach".
<p>The package contains all the materials needed to reproduce the findings of our paper. The paper is published by MDPI Applied Sciences journal and its details are as follow.</p> <p>Berghout, T.; Benbouzid, M. Diagnosis and Prognosis of Faults in High-Speed Aeronautical Bearings with a Collaborative Selection Incremental Deep Transfer Learning Approach. <em>Appl. Sci.</em> <strong>2023</strong>, <em>13</em>, 10916. https://doi.org/10.3390/app131910916</p> <p>1) Please you need to download the dataset from original link provided by introductory paper (Please read the above paper to find out about the datset used).<br> 2) Put the data in folders "RawData" for both experments.<br> 3) Please run the files for each experiment as provided, in alphabetical order.</p>
Comprehensive Dataset for Fault Detection and Diagnosis in Inverter-Driven PMSM Systems
<p><span>#</span><span> New in Version 3.0</span> The dataset has been reorganized for improved accessibility and clarity:</p> <ul> <li>/code: Contains all source code (C++ and Python) for data acquisition and processing</li> <li>/metadata: Contains sensor specifications and fault definitions</li> <li>/processed_data: Contains the processed and derived features</li> <li>/raw_data: Contains: <ul> <li>Raw sensor measurements</li> <li>Fault scenario data</li> <li>Normal operation data</li> <li>Thermistor calibration data</li> </ul> </li> <li>/visualizations: Contains data visualization outputs</li> </ul> <p><br>This dataset contains multi-sensor measurements from an inverter-driven PMSM system under various fault conditions. It includes: </p> <ul> <li>10,892 samples across 9 operational conditions</li> <li>8 raw sensor measurements</li> <li>15 derived features</li> <li>Data collected at 10 Hz sampling rate</li> <li> Fault scenarios including open-circuit, short-circuit, and overheating conditions</li> </ul> <p> Keywords:</p> <ul> <li>PMSM</li> <li>Fault Detection</li> <li>Inverter Faults</li> <li>Motor Drive Systems</li> <li>Experimental Data</li> <li>Machine Learning</li> </ul>
MATLAB Codes for: Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation
<p>The following MATLAB codes belong to the paper following paper which has been publication in MDPI Machines. This repository includes all the necessary MATLAB scripts and functions used in the research for diagnosing faults in drones using advanced acoustic emission analysis and optimization techniques. The dataset used in this paper is referenced in the article. Please check the publication for the dataset reference. Download the dataset, decompress it, and place it in the same repository as these codes to ensure proper functionality. For any queries or further information, please refer to this paper.</p> <p>Berghout, Tarek, and Mohamed Benbouzid. 2024. "Fault Diagnosis in Drones via Multiverse Augmented Extreme Recurrent Expansion of Acoustic Emissions with Uncertainty Bayesian Optimisation" <em>Machines</em> 12, no. 8: 504. https://doi.org/10.3390/machines12080504 </p> <p> </p>
Online appendix for "Leveraging Execution Trace with ChatGPT: A Case Study on Automated Fault Diagnosis" (New Ideas and Emerging Results Track in ICSME 2023)
<p>All the prompts we prepared for ChatGPT and the fault diagnosis results</p> <ul> <li>prompt_*: Prompt for ChatGPT <ul> <li>prompt_ChatGPT_setup_*.txt: Prompts to setup ChatGPT before starting the question to ChatGPT</li> <li>The other prompts: Prompts input to ChatGPT for fault diagnosis</li> </ul> </li> <li>result_*: Response from ChatGPT</li> <li>without_trace: Prompt or result when execution trace is not entered in ChatGPT</li> <li>with_trace: Prompt or result when execution trace is entered in ChatGPT</li> </ul>
Squirrel-Cage Induction Motor Fault Diagnosis Dataset
<p>The <strong>Squirrel Cage Induction Motor Fault Diagnosis Dataset</strong> is a multi-sensor data collection gathered to expand research on anomaly detection, fault diagnosis, and predictive maintenance, mainly using non-invasive methods such as thermal observation or vibration measurement. The measurements were gathered using an advanced <em>Wrocław University of Science and Technology</em> laboratory designed to simulate and study motor defects. The collected dataset is licensed under a <em>Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International</em> License.</p> <p>Available data:</p> <ul> <li>thermal images</li> </ul> <p>The example dataset utilization is presented in the GitHub repository: <a href="https://github.com/MatPiech/motor-fault-diagnosis/tree/main">motor-fault-diagnosis</a></p> <p>Related publications:</p> <ul> <li><a href="https://ein.org.pl/Unraveling-Induction-Motor-State-through-Thermal-Imaging-and-Edge-Processing-A-Step,170114,0,2.html">Unraveling Induction Motor State through Thermal Imaging and Edge Processing: A Step towards Explainable Fault Diagnosis</a></li> </ul>
Efficient sub-pixel fully connected neural network: an intelligent fault diagnosis method for signal resolution enhancement
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Adaptive Online Fault Diagnosis in Robot Swarms
<p>Contained are the data sets generated and analysed for the paper 'Adaptive Online Fault Diagnosis in Robot Swarms' by James O'Keeffe, Danesh Tarapore, Alan G. Millard and Jon Timmis</p>
Data from: Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN
Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence leads to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results shows the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.
Data from: Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN
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Methods for Probabilistic Fault Diagnosis: An EPS Case Study
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions. **Reference:** B. W. Ricks and O. J. Mengshoel, "Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study." In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 – October 1, 2009. **BibTex Reference:** @inproceedings{ricks09methods, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study}, booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)}, address = {San Diego, CA}, month = sep, year = {2009} }
Probabilistic Fault Diagnosis in Electrical Power Systems
Electrical power systems play a critical role in spacecraft and aircraft. This paper discusses our development of a diagnostic capability for an electrical power system testbed, ADAPT, using probabilistic techniques. In the context of ADAPT, we present two challenges, regarding modelling and real-time performance, often encountered in real-world diagnostic applications. To meet the modelling challenge, we discuss our novel high-level specification language which supports auto-generation of Bayesian networks. To meet the real-time challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuits typically have small footprints and are optimized for the real-time avionics systems found in spacecraft and aircraft. Using our approach, we present how Bayesian networks with over 400 nodes are auto-generated and then compiled into arithmetic circuits. Using real-world data from ADAPT as well as simulated data, we obtain average inference times smaller than one millisecond when computing diagnostic queries using arithmetic circuits that model our real-world electrical power system. Reference: O. J. Mengshoel, A. Darwiche, K. Cascio, M. Chavira, S. Poll, and S. Uckun, “Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft”, In Proc. of the Twentieth Innovative Applications of Artificial Intelligence, Conference (IAAI-08), Chicago, IL, 2008. BibTex Reference: @inproceedings{mengshoel08diagnosing, author = {Mengshoel, O. J. and Darwiche, A. and Cascio, K. and Chavira, M. and Poll, S. and Uckun, S.}, title = {Diagnosing Faults in Electrical Power Systems of Spacecraft and Aircraft}, booktitle = {Proceedings of the Twentieth Innovative Applications of Artificial Intelligence Conference (IAAI-08)}, pages = {1699--1705}, address = {Chicago, IL}, year = {2008} }
Model-Based Fault Detection and Diagnosis System for NASA Mars Subsurface Drill Prototype
The Drilling Automation for Mars Environment (DAME) project, led by NASA Ames Research Center, is aimed at developing a lightweight, low-power drill prototype that can be mounted on a Mars lander and be capable of drilling down several meters below the Mars surface for conducting geology and astrobiology research. The DAME drill system incorporates a large degree of autonomy - from quick diagnosis of system state and fault conditions to making the appropriate recovery actions - while also striving to achieve as many of the operational objectives as possible. This paper outlines, on a general level, the overall DAME architecture, equipment, and autonomy package. The main focus, however, is on describing the model-based fault detection and diagnosis system, including the modeling approach, the fault modes handled, and the diagnostic algorithms. The results of the latest field tests, conducted in 2006 in Haughton Crater on Devon Island (a Mars analogue site in Canadian Arctic), are also discussed.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.