Electric Motor Dataset, Paper, and Code

Electric Motor Dataset, Paper, and Code

Research and Dataset Oliver Mey 1

• paper: Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors
• zotero: meyConditionMonitoringDrive2021 link DOI
• code deepinsights-analytica/mdpi-arci2021-paper
• dataset Fordatis - Forschungsdaten-Repositorium der Fraunhofer-Gesellschaft, available at drive
• sensor: vibration, acoustic
• motor: induction motor
• fault: bearing
• status: tried

Research and Dataset Oliver Mey 2

• paper meyVibrationMeasurementsRotating2020 meyMachineLearningBasedUnbalance2020
• code deepinsights-analytica/ieee-etfa2020-paper: Machine Learning Based Unbalance Detection of a Rotating Shaft Using Vibration Data
• dataset Fordatis - Forschungsdaten-Repositorium der Fraunhofer-Gesellschaft, available at drive
• kaggle: Vibration Analysis on Rotating Shaft | Kaggle
• sensor: vibration, speed
• motor: BLDC
• fault: imbalance
• status: tried

Research and Dataset Oliver Mey 3

• paper Explainable AI Algorithms for Vibration Data-Based Fault Detection: Use Case-Adadpted Methods and Critical Evaluation
• code o-mey/xai-vibration-fault-detection
• dataset Fordatis - Forschungsdaten-Repositorium der Fraunhofer-Gesellschaft, available at drive
• sensor: vibration, speed
• motor: BLDC

Research and Dataset 4

• paper Sounding Industry: Challenges and Datasets for Industrial Sound Analysis
• code
• dataset IDMT-ISA-ELECTRIC-ENGINE - Fraunhofer IDMT
• sensor: acoustic
• motor: BLDC

Research and Dataset 5: PMSM Temperature also for Torque Estimation

• Kaggle Electric Motor Temperature | Kaggle
• Paper 1: Estimating Electric Motor Temperatures With Deep Residual Machine Learning
• Github 1: upb-lea/deep-pmsm: Estimate intrinsic Permanent Magnet Synchronous Motor temperatures with deep recurrent and convolutional neural networks.
• Paper 2: Thermal neural networks: Lumped-parameter thermal modeling with state-space machine learning
• Github 2: wkirgsn/thermal-nn: Thermal Neural Networks. Application to an electric motor.
• Arxiv: Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
• Paper 3: Investigation of long short-term memory networks to temperature prediction for permanent magnet synchronous motors
• Github 3: wkirgsn/mawk-thesis: Masterthesis - Predict a permanent magnet synchronous motor's components' temperature with LSTM/GRU recurrent neural networks using Chainer and particle swarm optimization.
• sensor: temperature (multiple points), voltage, speed, current, torque
• motor: PMSM

Research and Dataset 6

• paper 1: Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)
• paper 2: Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)
• kaggle: Identifying the Physics Behind an Electric Motor | Kaggle
• sensor: current
• motor: PMSM

Research and Dataset 7

• kaggle: Torque Characteristics of a Permanent Magnet Motor | Kaggle
• sensor: torque, current, voltage, angle
• motor: PMSM

Research and Dataset 8

• mendeley data: Data for: Feature Extraction of Rotor Fault Based on EEMD and Curve Code - Mendeley Data
• paper: Feature extraction of rotor fault based on EEMD and curve code
• sensor: vibration
• fault: contact rubbing, unbalance misalignment

Research and Dataset 9

• mendeley data: Vibration, Acoustic, Temperature, and Motor Current Dataset of Rotating Machine Under Varying Load Conditions for Fault Diagnosis - Mendeley Data
• data in brief: Vibration and Current Dataset of Three-Phase Permanent Magnet Synchronous Motors with Stator Faults - ScienceDirect
• sensor: vibration, acoustic, temperature, current
• motor: pmsm
• fault: inter-turn short circuits and inter-coil short circuits

Research and Dataset 10

• dataset: Konstruktions- und Antriebstechnik (KAt)  -  Data Sets and Download (Universität Paderborn)
• paper: A novel deep learning approach for intelligent fault diagnosis applications based on time-frequency images | SpringerLink
• code: ozggultekin/MultisensoryDataFusionWithSTFT
• sensor: speed, torque, radial load, and temperature
• fault: bearing