Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Kahr, Matthias; Kovács, Gabor; Loinig, Markus; Brückl, Hubert

doi:10.3390/s22072490

Cited by 21 publications

(10 citation statements)

References 42 publications

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“…Convolutional Neural Networks are artificial neural networks that are primarily used to solve image-driven pattern recognition tasks because of the design and structure of their architecture. It has been widely used in fault detection [48,49,50] because of its feature extraction capability. The idea behind convolutions is to use kernels to extract particular features from input data.…”

Section: Cnnmentioning

confidence: 99%

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Ahang¹,

Jalayer²,

Shojaeinasab³

et al. 2022

Preprint

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Bearings are one of the vital components of the rotating machines that are prone to unexpected faults. Therefore, bearing fault diagnosis and condition monitoring is essential for reducing operational costs and downtime in numerous industries. In various production conditions, bearings can be operated under a range of loads and speeds, which causes different vibration patterns associated with each fault type. Normal data is ample as systems usually work in desired conditions. On the other hand, fault data is rare, and in many conditions, there is no data recorded for the fault classes. Accessing fault data is crucial for developing data driven fault diagnosis tools that can improve both the performance and safety of operations. To this end, a novel algorithm based on Conditional Generative Adversarial Networks (CGANs) is introduced. Trained on the normal and fault data on any actual fault conditions, this algorithm generates fault data from normal data of target conditions. The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions. Several state-of-the-art classifiers and visualization models are implemented to evaluate the quality of the synthesized data. The results demonstrate the efficacy of the proposed algorithm.

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Section: Cnnmentioning

confidence: 99%

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Ahang¹,

Jalayer²,

Shojaeinasab³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Nowadays, in the conditions of digital transformation of industry, enterprises including oil and gas companies, are equipped with systems that allow the automatic collection and analysis of parameters of technological processes, equipment and energy supply systems. However, the lack of models for identifying faults and estimating the energy costs associated with the level of technical condition does not allow the full use of the data collected [38][39][40]. The main purposes for using such data today include assessing performance degradation, constructing a performance index, and predicting the remaining equipment life [41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

2023

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Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

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“…Zhang used PCA to reduce the data dimension from six to three, reducing the complexity of the back-end algorithm [17]. Using digital signal processing, feature extraction transforms the original time domain into a frequency domain or time-frequency domain signals, while fast Fourier transform (FFT) and wavelet transform (WT) [18][19][20][21][22][23][24] have been verified as effective methods. However, FFT is only suitable for stationary signals, and cannot be effectively analyzed for non-stationary signals caused by different time domains.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

Chen

Lin

et al. 2023

Applied Sciences

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Intelligent machinery fault diagnosis is one of the key technologies for the transformation and competitiveness of traditional factories. Complex production environments make it difficult to maintain good prediction performance using traditional methods. This paper proposes a deep convolutional neural network combined with an adaptive environmental noise method to achieve robust fault classification. The proposed method uses six-dimensional physical signals for data fusion and feature fusion, extracts obvious features and enhances subtle features, and uses continuous wavelets and Gramian angular fields to transform signals with different physical and frequency characteristics into time–frequency maps and two-dimensional images. The fusion technology of different signals can provide comprehensive features for fault prediction, improving upon the blind spots of traditional methods to extract features, and then perform prediction and classification through deep convolutional neural networks. In the experiment, the tool failure classification of the dental milling machine is used as a verification case. The results show that the prediction accuracy of the proposed method is nearly 100%, much better than other comparison methods. In addition, white noise was added in the experiment to verify the noise immunity of the model. The results show that the accuracy of the proposed method is 99%, which is better than other comparison methods in terms of accuracy and robustness, proving the effectiveness of the proposed method for fault diagnosis and classification.

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Condition Monitoring of Ball Bearings Based on Machine Learning with Synthetically Generated Data

Cited by 21 publications

References 42 publications

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Intelligent Machinery Fault Diagnosis Method Based on Adaptive Deep Convolutional Neural Network: Using Dental Milling Cutter Malfunction Classifications as an Example

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