Intelligent fault diagnosis of planetary gearbox based on adaptive normalized CNN under complex variable working conditions and data imbalance

Wang, Chaoge; Li, Hongkun; Zhang, Kongliang; Hu, Shaoliang; Sun, Bin

doi:10.1016/j.measurement.2021.109565

Cited by 60 publications

(22 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CNNs are widely used in the field of fault diagnosis. 24,25 The deep CNNs can extract the deep features of the input signal through the deep network, and can make effective judgments on the extracted deep features and identify the fault type. Deep CNNs are mainly composed of convolutional layers, activation layers, pooling layers, and fully connected layers.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

The dataset in the application scenario of existing fault diagnosis methods is often balanced, while the data collected under actual working conditions are often imbalanced. Directly applying existing fault diagnosis methods to this scenario will lead to poor diagnosis effect. In view of the above problems, we proposed a method called dual generative adversarial networks (DGANs) combining conditional assistance and feature enhancement. The method uses data augmentation as a basic strategy to supplement imbalanced datasets by generating high-quality data. Firstly, a new generator is designed to build the basic framework by sharing the dual-branch deconvolutional neural networks, and combining the label auxiliary information and the coral distance loss function to ensure the diversity of generated samples. Secondly, a new discriminator was designed, which is based on deep convolutional neural networks and embedded with auxiliary classifiers, further expanding the function of the discriminator. Thirdly, the self-attention module is introduced into both the generator and the discriminator to enhance deep feature learning and improve the quality of generated samples; finally, the proposed method is experimentally validated on datasets of two different testbeds. The experimental results show that the proposed method can generate fake samples with rich diversity and high quality, using these samples to supplement the imbalanced dataset, the effect of imbalanced fault diagnosis has been substantially improved. This method can be used to solve the problem of fault diagnosis in the case of sample imbalance, which often exists in actual working conditions.

show abstract

Section: Theoretical Backgroundmentioning

confidence: 99%

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

et al. 2023

Structural Health Monitoring

View full text Add to dashboard Cite

show abstract

“…It has powerful capabilities of feature representation and extraction, and its computational cost is low. CNN is widely used in image recognition, classification, detection and other tasks [87]. CNN thinks each part of the input is related, can automatically extract corresponding features from different regions in the input, and combine the features automatically.…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Zhang

Zhou

Wang

et al. 2022

IET Collab Intel Manufact

View full text Add to dashboard Cite

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides a comprehensive summary and systematic review of vibration signal-based GFD methods in recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults and their vibration signal characteristics. The authors overview and compare the common feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, and entropy. For each method, this paper introduces its idea, analyses its advantages and disadvantages, and reviews its application in GFD. Then the authors present machine learning-based methods for gear fault recognition and emphasise deep learning-based methods. Moreover, the authors compare different fault recognition methods. Finally, the authors discuss the challenges and opportunities towards data-driven GFD.

show abstract

“…Therefore, it is of great significance to realize the condition monitoring and fault diagnosis of the planetary gear reducer, which can effectively reduce the long-term shutdown of the equipment and the high maintenance cost. 1 Specifically, excessive backlash in a planetary gear reducer may lead to undesirable vibrations, resulting in degradation of equipment and premature failure. [2][3][4][5] Research on backlash fault diagnosis appears to be necessary.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

Zhang

Liu

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

View full text Add to dashboard Cite

Planetary gear reducer is widely applied in various transmission equipment, and its performance highly affects the operation of a machine. The appearance of unreasonable backlash in planetary gear reducer may lead to undesirable vibration, which may accelerate the degradation of equipment and eventually cause premature failure. In traditional condition-based monitoring (CBM), sensors such as accelerometers have been utilized to detect the fault of planetary gear. However, the complexity and integration of planetary gear limit the installation and application of vibration sensors in practice. In this case, the current signal from motor, as a convenient real-time monitoring approach, is introduced into the CBM of the planetary gear. In this paper, a fault diagnosis method based on drive motor current signal analysis (MCSA) is presented to identify the backlash fault in a planetary gear reducer. In this method, frequency domain data of the original current signal is found and used to automatically extract fault features. A deep sparse autoencoder (DSAE) extracts the required features. In particular, the Fisher criterion is introduced to evaluate the sensitivity of these features. It then selects the most effective ones for improving diagnostic accuracy as well as diagnostic efficiency. Experimental test data shows that under different load conditions, this method outperforms other typical fault diagnosis methods and exhibits the best performance.

show abstract

Intelligent fault diagnosis of planetary gearbox based on adaptive normalized CNN under complex variable working conditions and data imbalance

Cited by 60 publications

References 37 publications

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

Dual generative adversarial networks combining conditional assistance and feature enhancement for imbalanced fault diagnosis

State of the art on vibration signal processing towards data‐driven gear fault diagnosis

Fault diagnosis of planetary gear backlash based on motor current and Fisher criterion optimized sparse autoencoder

Contact Info

Product

Resources

About