Multi-fault recognition of gear based on wavelet image fusion and deep neural network

He, Hong; Zhao, Shuanfeng; Guo, Wei; Wang, Yuan; Wang, Pengfei

doi:10.1063/5.0066581

Cited by 5 publications

(2 citation statements)

References 35 publications

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“…The commonly used analysis methods include short-time Fourier transform, wavelet packet analysis, etc. [23]. Consider its advantages in handling non-stationary time-varying signals [24], the paper uses wavelet packet analysis to extract the time-frequency characteristics of the signal.…”

Section: ) Feature Extractionmentioning

confidence: 99%

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Zhang

Liu

Xu³

et al. 2022

IEEE Access

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Under the background of Gaussian colored environmental magnetic noise, for the problem that a supervised learning algorithm has high requirements on samples and poor practicability, a magnetic anomaly detection method based on feature fusion and isolation forest (IForest) algorithm is proposed. The method uses different feature algorithms to extract the statistical features, time-frequency features and fractal features of the signal, reduces the dimensionality of the features by principal component analysis (PCA) and generates feature fusion tensors. Finally the IForest algorithm is used to achieve target detection. The simulation and experimental results show that the method has a higher detection rate under different SNR of Gaussian color noise, which is approximately 5%-18% higher than that of the traditional feature detection algorithm. This method can train an effective detection model with only a small number of negative samples. Compared with the fully connected neural network (FCN) model trained with unbalanced samples, the detection rate increases by approximately 5%-12%, and it takes less time.

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Section: ) Feature Extractionmentioning

confidence: 99%

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Zhang

Liu

Xu³

et al. 2022

IEEE Access

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“…Contemporary applications of wavelet analysis and image diagnostic methods have demonstrated remarkable accuracy in classifying gear faults, highlighting their significance in the field of intelligent gear fault identification [3]. The image diagnostic method, which is a crucial technique, enables the identification and classification of faults through deep learning [4].…”

Section: Introductionmentioning

confidence: 99%

Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN

Hu,

Zhao,

Huang

et al. 2023

Processes

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Traditional methods for identifying gear faults typically require a substantial number of faulty samples, which in reality are challenging to obtain. To tackle this challenge, this paper introduces a sophisticated approach for intelligent gear fault identification, utilizing discrete wavelet decomposition and an enhanced convolutional neural network (CNN) optimized for scenarios with limited sample data. Initially, the features of the sample signal are extracted and enhanced using discrete wavelet decomposition. Subsequently, the refined signal is transformed into a two-dimensional image through a Markov transition field, preparing it for improved two-dimensional CNN training. Finally, the refined network model is applied to assess the gear fault dataset, achieving a training accuracy of 97% and a classification accuracy of 88.33%. This demonstrates the method’s feasibility and effectiveness in identifying gear faults with limited sample data.

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Rotating machinery anomaly detection using data reconstruction generative adversarial networks with vibration energy analysis

Liu

Wang

et al. 2022

AIP Advances

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Rotating machines, such as engines, turbines, or gearboxes, are widely used in modern society. Their mechanical components, such as rotors, bearings, or gears, are the main parts, and any failure in them can lead to a complete shutdown of the rotating machinery. Anomaly detection in such critical systems is essential for the healthy operation of rotating machinery. As the requirement of obtaining sufficient fault data of rotating machinery is challenging to satisfy, a new anomaly detection model is proposed for rotating machinery, which can achieve anomaly detection without fault samples. The model combines vibration energy features, adversarial learning mechanism, and long short-term memory to develop a novel anomaly detection model based on data reconstruction. The model was validated using two different datasets. The proposed model achieves the highest accuracy of 98.90% for anomaly detection under different working conditions, outperforming similar models.

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Multi-fault recognition of gear based on wavelet image fusion and deep neural network

Cited by 5 publications

References 35 publications

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Magnetic Anomaly Detection Method Based on Feature Fusion and Isolation Forest Algorithm

Intelligent and Small Samples Gear Fault Detection Based on Wavelet Analysis and Improved CNN

Rotating machinery anomaly detection using data reconstruction generative adversarial networks with vibration energy analysis

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