Datasets Comparison

--Contact email: m.najafi@nit.ac.ir --Babol Noshirvani University of Technology This is thermal images (IRT) dataset in the context of condition monitoring of electrical equipment--Induction Motors. All artificially generated defects are internal faults and depend on neither external pieces nor failure in initial setup components. For the induction motor, 8 different cases of short circuit failures in the stator windings, stuck rotor fault, and cooling fan failure are taken into account Thermal image acquisition is done at the workbench by a Dali-tech T4/T8 infrared thermal image camera at an Electrical Machines Laboratory at the environment temperature of 23°. To pave the way for future research or testing AI systems, the IR-image dataset has been developed and it has been made publicly available for use by researchers in this field. Regarding the reservation of the BNUT rights, referencing this page--doi--is a desideratum. In this work, a dataset of thermographic images representing 11 conditions for 3-Phase induction motors were introduced. Specifications of Camera and equipment which used for this very dataset are shown in tables 1, 2, and 3 in the Readme_InductionMotor.pdf.

--Applying this dataset is allowed just by considering the Contributors' citation for any academic or other purposes. You can Cite the related paper: M. Najafi, Y. Baleghi, S. A. Gholamian and S. Mehdi Mirimani, "Fault Diagnosis of Electrical Equipment through Thermal Imaging and Interpretable Machine Learning Applied on a Newly-introduced Dataset," 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1-7, doi: 10.1109/ICSPIS51611.2020.9349599.

Babol Noshirvani University of Technology

Thermography, Non-Destructive Testing, Thermal Imaging, Induction Machine, System Fault Detection, Non-Invasive Diagnostics

Creative Commons Attribution 4.0 International

--Contact email: m.najafi@nit.ac.ir --Babol Noshirvani University of Technology -- 40 Ground Truths are added to the dataset in order to conduct the evaluation. The annotations have been made by trained personnel and are considered to be accurate and reliable. This is a thermal image dataset specifically focused on condition monitoring of electrical equipment, specifically induction motors. The dataset includes artificially generated internal faults, such as short circuit failures in the stator windings, stuck rotor faults, and cooling fan failures. The thermal images were acquired using a Dali-tech T4/T8 infrared thermal image camera in an Electrical Machines Laboratory, with an ambient temperature of 23°. This dataset has been made publicly available for use by researchers in the field of AI system development and testing. To respect the rights of BNUT and authors, referencing the page's DOI and related paper DOI is necessary. paper title: Fault Diagnosis of Electrical Equipment through Thermal Imaging and Interpretable Machine Learning Applied on a Newly-introduced Dataset DOI: 10.1109/ICSPIS51611.2020.9349599 The specifications of the camera and equipment used to create this dataset are detailed in Tables 1, 2, and 3 in the Readme_InductionMotor.pdf file. In this work, a dataset of thermographic images representing 11 conditions for 3-Phase induction motors was introduced. Specifications of Camera and equipment used for this very dataset is shown in tables 1, 2, and 3 in the Readme_InductionMotor.pdf.

--Applying this dataset is allowed just by considering the Contributors' citation for any academic or other purposes. You can Cite the related paper: M. Najafi, Y. Baleghi, S. A. Gholamian and S. Mehdi Mirimani, "Fault Diagnosis of Electrical Equipment through Thermal Imaging and Interpretable Machine Learning Applied on a Newly-introduced Dataset," 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), Mashhad, Iran, 2020, pp. 1-7, doi: 10.1109/ICSPIS51611.2020.9349599.

Babol Noshirvani University of Technology

Feature Selection, Thermography, Non-Destructive Testing, Thermal Imaging, Induction Machine, System Fault Detection, Explainable Artificial Intelligence, Fault Diagnosis

Creative Commons Attribution 4.0 International