Early Fault Classification in Rotating Machinery With Limited Data Using TabPFN

Magadán, L.; Roldán-Gómez, J.; Granda, J. C.; Suárez, F. J.

doi:10.1109/jsen.2023.3331100

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…Moreover, it is important to adapt the structure of the deep network to the changing conditions of the training process such as available data packets. Only in the absence of a strict dependence on the precision of the diagnostic system on the size and quality of the available information is it possible to ensure correct fault detection [51]. During the experimental study, structures with three convolutional layers and one classifier layer were analyzed.…”

Section: Cnn-based Diagnostic System For Bearing Faultsmentioning

confidence: 99%

Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network

Skowron,

Frankiewicz,

Jarosz

et al. 2024

Electronics

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Currently, great emphasis is being placed on the electrification of means of transportation, including aviation. The use of electric motors reduces operating and maintenance costs. Electric motors are subjected to various types of damage during operation, of which rolling bearing defects are statistically the most common. This article focuses on presenting a diagnostic tool for bearing conditions based on mechanic vibration signals using convolutional neural networks (CNN). This article presents an alternative to the well-known classical diagnostic tools based on advanced signal processing methods such as the short-time Fourier transform, the Hilbert–Huang transform, etc. The approach described in the article provides fault detection and classification in less than 0.03 s. The proposed structures achieved a classification accuracy of 99.8% on the test set. Special attention was paid to the process of optimizing the CNN structure to achieve the highest possible accuracy with the fewest number of network parameters.

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Section: Cnn-based Diagnostic System For Bearing Faultsmentioning

confidence: 99%

Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network

Skowron,

Frankiewicz,

Jarosz

et al. 2024

Electronics

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Enhanced Fault Classification in Bearings: A Multi-Domain Feature Extraction Approach with LSTM-Attention and LASSO

Hatipoğlu,

Süpürtülü,

Yılmaz

2024

Arab J Sci Eng

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In various engineering fields, bearings are crucial for the operation of rotating machinery. Therefore, the early and precise detection of bearing failures is essential to prevent mechanical issues and maintain optimal machinery performance. This study proposes a fault classification framework based on multi-domain feature extraction, the least absolute shrinkage and selection operator method, long-short term memory, and the self-attention mechanism. Fifteen time-domain, five frequency-domain, and four chaotic-domain features are extracted from the raw data. To validate the model's accuracy and stability, datasets from the Hanoi University of Science and Technology (HUST), a newly published dataset, and Case Western Reserve University (CWRU) were utilized. Experimental validation using open-source bearing datasets demonstrates that the proposed framework can be effectively deployed, highlighting its potential as a fundamental pillar in the field of intelligent manufacturing. The findings show that our model achieves an F1-score of 99.903% for the test set with nine selected features across 24, encompassing all five bearing categories within the HUST dataset. Furthermore, its application to the CWRU dataset yielded comparable metrics, reaching a 98.742% F1-score with eight selected features among 24 features. The objective is to achieve successful prediction outcomes with a reduced number of parameters and to emphasize the significance of incorporating chaotic features into the process for data sets characterized by chaotic processes.

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GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

Bagci Das,

Das

2024

J. Vib. Eng. Technol.

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Purpose As the concept of Industry 4.0 is introduced, the significance of Smart Fault Diagnosis in the industry is increased. Therefore, it is essential to develop accurate, robust, and lightweight intelligent fault diagnosis approach that can be executed in real-time even with embedded systems. Additionally, it is preferable to use a single method for multi-purposes such as the fault detection, identification, and severity assessment. This study proposed a new approach called GaBoT for fault diagnosis of rotating machinery to satisfy those requirements. Method The proposed approach adopted the concept of the ensemble of ensembles by boosting random forest. The statistical features of discrete wavelet transform were considered since they are easy and fast to obtain. Model optimization was conducted by employing genetic algorithm to alleviate the computational load without decreasing the model performance. The proposed approach has been validated by unseen data from an experimental dataset including shaft, rotor, and bearing faults. Results The experimental results indicate that the proposed approach can effectively find the fault type with 99.85% accuracy. Besides, it successfully determines the fault severity by accuracy values between 96.45 and 99.72%. GABoT can also determine the imbalance severity in the presence of three bearing faults. Conclusion Employing GA eliminated most of the redundant features and reduced the model execution time consumption. The results yielded that GABoT is a highly accurate model, and can be utilized in real-time fault diagnosis of rotating machinery.

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Early Fault Classification in Rotating Machinery With Limited Data Using TabPFN

Cited by 3 publications

References 27 publications

Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network

Detection and Classification of Rolling Bearing Defects Using Direct Signal Processing with Deep Convolutional Neural Network

Enhanced Fault Classification in Bearings: A Multi-Domain Feature Extraction Approach with LSTM-Attention and LASSO

GABoT: A Lightweight Real-Time Adaptable Approach for Intelligent Fault Diagnosis of Rotating Machinery

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