Triaxial bearing vibration dataset of induction motor under varying load conditions

Soother, Dileep Kumar; Mehran, Sanaullah; Shaikh, Muhammad Zakir; Hussain, Majid; Chowdhry, Bhawani Shankar; Hussain, Tanweer

doi:10.1016/j.dib.2022.108315

Cited by 9 publications

(4 citation statements)

References 4 publications

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“…This section presents the outcomes of utilising the proposed method for motor fault classification using a DNN with optimised XGBoost parameters using the features shown in Table 3: the statistical features and t-SNE components (deep learning features). The XGBoost parameters used in the system are maximum depth, learning rate, and n-estimators, considered the three primary hyperparameters [34]: the CWRU dataset and Induction Motor Triaxial bearing vibration dataset from Kumar et al [35] have been further used to verify the effectiveness of the proposed method.…”

Section: Experimental Datasets and Results Analysismentioning

confidence: 99%

“…The XGBoost parameters used in the system are maximum depth, learning rate, and n‐estimators, considered the three primary hyperparameters [34]: the CWRU dataset and Induction Motor Triaxial bearing vibration dataset from Kumar et al. [35] have been further used to verify the effectiveness of the proposed method.…”

Section: Experimental Datasets and Results Analysismentioning

confidence: 99%

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Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting

Lee,

Maceren

2024

IET Electric Power Appl

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Intelligent motor fault diagnosis in industrial applications requires identifying key characteristics to differentiate various fault types effectively. Solely relying on statistical features cannot guarantee high classification accuracy, while complex feature extraction techniques can pose challenges for industry practitioners. Conversely, advanced feature extraction may not ensure that the model effectively learns these features for classification. A feature fusion approach that combines statistical and deep learning features to address these challenges is proposed. Since statistical features form the foundation for general feature extraction, statistical and deep learning features are combined using Extreme Gradient Boosting (XGBoost) algorithm with Particle Swarm Optimization (PSO). The PSO algorithm automates parameter tuning for XGBoost. A deep neural network (DNN) adaptively extracts hidden features, improving bearing fault classification precision using t‐SNE representation. Results successfully prove the DNN's ability to classify diverse motor faults using deep learning features. Thus, integrating statistical features with XGBoost further enhances DNN's performance. To ensure robustness, the proposed method has been compared with different motor fault classification methods and validated across different motor fault datasets, showcasing improved classification accuracy and robust performance, even amidst varying noise levels. This approach represents a promising advancement in intelligent fault diagnosis within industrial contexts.

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Section: Experimental Datasets and Results Analysismentioning

confidence: 99%

Section: Experimental Datasets and Results Analysismentioning

confidence: 99%

Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting

Lee,

Maceren

2024

IET Electric Power Appl

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“…They serve as practical introductions for students and researchers to signal processing, fault detection, and predictive maintenance. For instance, the Squirrel Cage Induction Motor Fault Diagnosis Dataset is a comprehensive multisensor data collection, compiled to advance research in anomaly detection, fault diagnosis, and predictive maintenance, primarily employing non-invasive methods like thermal observation or vibration measurement [44,45]. Figure 5 shows two MATLAB/Simulink block diagrams, one of a DC permanent magnet motor and the other one of an AC induction motor; these models can be used to collect data that can be used to train the SOM to detect normal and abnormal conditions.…”

Section: Resultsmentioning

confidence: 99%

A Low-Cost Microcontroller-Based Normal and Abnormal Conditions Classification Model for Induction Motors Using Self-Organizing Feature Maps (SOFM)

Ponce,

Anthony,

Deshpande

et al. 2023

Energies

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Digital twins have provided valuable information for making effective decisions to ensure high efficiency in the manufacturing process using virtual models. Consequently, AC electric motors play a pivotal role in this framework, commonly employed as the primary electric actuators within Industry 4.0. In addition, classification systems could be implemented to identify normal and abnormal operating conditions in electric machines. Moreover, the execution of such classification systems in low-cost digital embedded systems is crucial, enabling continuous monitoring of AC electric machines. Self-Organized Maps (SOMs) offer a promising solution for implementing classification systems in low-cost embedded systems due to their ability to reduce system dimensionality and visually represent the model’s features, so local digital systems can be used as classification systems. Therefore, this paper aims to investigate the utilization of SOMs for classifying operating conditions in AC electric machines. Furthermore, when integrated into an embedded system, SOMs detect abnormal conditions in AC electric machines. A trained SOM is deployed on a C2000 microcontroller to exemplify the proposed approach. It should be noted that the proposed structure can be adapted for implementation with different systems in the context of Industry 4.0.

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“…· Considering dataset for fault diagnosis often requires large amounts of effort and time consumption, this dataset can provide a useful dataset in the fault diagnosis research field [1] . Therefore, this dataset can be used to analyze the condition of motor under various motor powers with different severity of motor stator faults [ 2 , 3 ].…”

Section: Value Of the Datamentioning

confidence: 99%

Vibration and current dataset of three-phase permanent magnet synchronous motors with stator faults

et al. 2023

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Triaxial bearing vibration dataset of induction motor under varying load conditions

Cited by 9 publications

References 4 publications

Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting

Induction motor bearing fault classification using deep neural network with particle swarm optimization‐extreme gradient boosting

A Low-Cost Microcontroller-Based Normal and Abnormal Conditions Classification Model for Induction Motors Using Self-Organizing Feature Maps (SOFM)

Vibration and current dataset of three-phase permanent magnet synchronous motors with stator faults

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