Effect of data preprocessing methods and hyperparameters on accuracy of ball bearing fault detection based on deep learning

Kim, Dong Wook; Lee, Eun Sung; Jang, Woong Ki; Kim, Byeong Hee; Seo, Young Ho

doi:10.1177/16878132221078494

Cited by 6 publications

(3 citation statements)

References 25 publications

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“…(2) Data preprocessing Data preprocessing includes signal processing and dimension transformation. For example, Dong Wook Kim et al [10] studied the effect of data preprocessing methods and super parameters on rolling bearing fault detection accuracy in deep learning. The higher diagnostic accuracy of the 2D image data format of the convolutional neural network was confirmed by one-dimensional and two-dimensional conversion of the data.…”

Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

Song

Wang

et al. 2023

Applied Sciences

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As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency.

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Section: Introductionmentioning

confidence: 99%

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

Song

Wang

et al. 2023

Applied Sciences

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“…In the context of ball bearing fault diagnosis, vibrations are the most common signals used to train ML algorithms to detect ball bearing faults in IMs [9,28]. Experimentally, these vibrations are collected at a certain sampling frequency (SF) using accelerometers, often placed on the housing of the ball bearing in the IM [29]. A higher SF allows for finer precision in acquiring vibration data, but the price of these sensors also increases along with the SF [27].…”

Section: Introductionmentioning

confidence: 99%

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers

El Bouharrouti,

Morinigo-Sotelo,

Belahcen

2023

Machines

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Vibration signals carry important information about the health state of a ball bearing and have proven their efficiency in training machine learning models for fault diagnosis. However, the sampling rate and frequency resolution of these acquired signals play a key role in the detection analysis. Industrial organizations often seek cost-effective and qualitative measurements, while reducing sensor resolution to optimize their resource allocation. This paper compares the performance of supervised learning classifiers for the fault detection of bearing faults in induction machines using vibration signals sampled at various frequencies. Three classes of algorithms are tested: linear models, tree-based models, and neural networks. These algorithms are trained and evaluated on vibration data collected experimentally and then downsampled to various intermediate levels of sampling, from 48 kHz to 1 kHz, using a fractional downsampling method. The study highlights the trade-off between fault detection accuracy and sampling frequency. It shows that, depending on the machine learning algorithm used, better training accuracies are not systematically achieved when training with vibration signals sampled at a relatively high frequency.

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“…In this quickly evolving technological environment, enterprises throughout the world have expanded the number of methods on their factory floors in order to collect data that might provide them with important insights into their operations [2]. This data-driven strategy helps to acquire knowledge about the machine, and its effective AI analysis may significantly improve operational efficiencies, preventive maintenance, and quality control [3]. Organizations all around the globe are turning to smarter technology as they see the clear advantages of low-cost solutions like AI.…”

Section: Introductionmentioning

confidence: 99%

A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

Zim¹,

Ashraf²,

Iqbal³

et al. 2022

Preprint

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Rolling bearings are the most crucial components of rotating machinery. Identifying defective bearings in a timely manner may prevent the malfunction of an entire machinery system. The mechanical condition monitoring field has entered the big data phase as a result of the fast advancement of machine parts. When working with large amounts of data, the manual feature extraction approach has the drawback of being inefficient and inaccurate. Data-driven methods like the Deep Learning method have been successfully used in recent years for mechanical intelligent fault detection. Convolutional neural networks (CNNs) were mostly used in earlier research to detect and identify bearing faults. The CNN model, however, suffers from the drawback of having trouble managing fault-time information, which results in a lack of classification results. In this study, bearing defects have been classified using a state-of-the-art Vision Transformer (ViT). Bearing defects were classified using Case Western Reserve University (CWRU) bearing failure laboratory experimental data. The research took into account 13 distinct kinds of defects under 0-load situations in addition to normal bearing conditions. Using the short-time Fourier transform (STFT), the vibration signals were converted into 2D time-frequency images. The 2D time-frequency images are used as input parameters for the ViT. The model achieved an overall accuracy of 98.8%.

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Effect of data preprocessing methods and hyperparameters on accuracy of ball bearing fault detection based on deep learning

Cited by 6 publications

References 25 publications

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

Bearing Fault Diagnosis Method Based on Deep Learning and Health State Division

Multi-Rate Vibration Signal Analysis for Bearing Fault Detection in Induction Machines Using Supervised Learning Classifiers

A Vision Transformer-Based Approach to Bearing Fault Classification via Vibration Signals

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