A Fast Multi-tasking Solution: NMF-Theoretic Co-clustering for Gear Fault Diagnosis under Variable Working Conditions

The judgment of gear failure is based on the pitting area ratio of gear. Traditional gear pitting calculation method mainly rely on manual visual inspection. This method is greatly affected by human factors, and is greatly affected by the working experience, training degree and fatigue degree of the detection personnel, so the detection results may be biased. The non-contact computer vision measurement can carry out non-destructive testing and monitoring under the working condition of the machine, and has high detection accuracy. To improve the measurement accuracy of gear pitting, a novel multi-scale splicing attention U-Net (MSSA U-Net) is explored in this study. An image splicing module is first proposed for concatenating the output feature maps of multiple convolutional layers into a splicing feature map with more semantic information. Then, an attention module is applied to select the key features of the splicing feature map. Given that MSSA U-Net adequately uses multi-scale semantic features, it has better segmentation performance on irregular small objects than U-Net and attention U-Net. On the basis of the designed visual detection platform and MSSA U-Net, a methodology for measuring the area ratio of gear pitting is proposed. With three datasets, experimental results show that MSSA U-Net is superior to existing typical image segmentation methods and can accurately segment different levels of pitting due to its strong segmentation ability. Therefore, the proposed methodology can be effectively applied in measuring the pitting area ratio and determining the level of gear pitting.

Section: Introductionmentioning

confidence: 99%

Gear Pitting Measurement by Multi-Scale Splicing Attention U-Net

Qin

Chen

et al. 2023

“…However, an intelligent fault diagnosis based on shallow learning has certain limitations. According to the literature [1][2][3][4][5][6][7][8], an excellent diagnostic performance depends directly on the quality of the extracted features. This limitation indirectly leads us to a significant amount of energy on tedious feature extraction and feature selection.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

Shang

Gao

et al. 2021

For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

“…Gear has been used extensively in transmission system due to its large velocity ratio, strong bearing capacity, compactness and high efficiency [1][2][3][4]. Gear fault diagnosis (GFD) also becomes one of the most important research hotspots from both industrial and academic communities for ensuring the safe and efficient operation of gear transmission system.…”

Section: Introductionmentioning

confidence: 99%

Model Parameter Transfer for Gear Fault Diagnosis under Varying Working Conditions

Chen

Shen

et al. 2021

Self Cite

Gear fault diagnosis technologies have received rapid development and been effectively implemented in many engineering applications. However, the various working conditions would degrade the diagnostic performance and make gear fault diagnosis (GFD) more and more challenging. In this paper, a novel model parameter transfer (NMPT) is proposed to boost the performance of GFD under varying working conditions. Based on the previous transfer strategy that controls empirical risk of source domain, this method further integrates the superiorities of multi-task learning with the idea of transfer learning (TL) to acquire transferable knowledge by minimizing the discrepancies of separating hyperplanes between one specific working condition (target domain) and another (source domain), and then transferring both commonality and specialty parameters over tasks to make use of source domain samples to assist target GFD task when sufficient labeled samples from target domain are unavailable. For NMPT implementation, insufficient target domain features and abundant source domain features with supervised information are fed into NMPT model to train a robust classifier for target GFD task. Related experiments prove that NMPT is expected to be a valuable technology to boost practical GFD performance under various working conditions. The proposed methods provides a transfer learning-based framework to handle the problem of insufficient training samples in target task caused by variable operation conditions.