Novel motor fault detection scheme based on one-class tensor hyperdisk

He, Ziming; Zeng, Yuting; Shao, Haidong; Hu, Hongwei; Xu, Xiaoqiang

doi:10.1016/j.knosys.2023.110259

Cited by 26 publications

(10 citation statements)

References 34 publications

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“…In other words, this set of classifiers can be directly characterized by the GCN output, i.e. H (2) ∈ R C×D .…”

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

“…The learned multi-label classifier H (2) is then used to recognize the fault feature vector x i , extracted by the feature learning module, to obtain the predicted label vector: ŷi = H (2) x i (5) where ŷi = ŷ1 i , ŷ2 i , . .…”

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

“…ŷc i is the predicted probability corresponding to the cth single fault, obtained by the dot product operation between the cth row vector (i.e. the feature vector of the cth single-fault label node) of H (2) and the fault feature vector x i . Then, ŷc i is artificially set to one if it is greater than a threshold (typically 0.5); otherwise, it is set to zero.…”

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

“…working environments, operating conditions, and running time), however, the components inside gearboxes (e.g. gears, bearings, and shafts) may experience various fault types [1,2]. These faults will affect the operation safety and reliability of gearboxes and even the whole rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A compound fault diagnosis model for gearboxes using correlation information between single faults

Zeng,

Wang,

Cheng

et al. 2023

Meas. Sci. Technol.

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Gearboxes are key components of rotating machinery. Performing intelligent fault diagnosis of gearboxes with condition-based monitoring information helps to make reliable decisions on equipment operation and maintenance. Besides single faults, compound faults also are common failure forms of gearboxes. Conventional intelligent diagnosis models (known as single-label models) generally treat a compound fault as a new fault type, ignoring the correlations between the compound fault and the corresponding single faults. To overcome this problem, multi-label learning has been introduced and developed into multi-label models. It is also possible that different single faults are not independent but correlated with each other. Existing multi-label models, however, usually ignore this aspect. Therefore, exploiting the correlation information between single faults can further improve multi-label models. To this end, every single fault is treated as a label node , resulting in a label graph. The feature vector of each label node is initialized by the word embedding of the corresponding single-fault label. All the word embeddings are mapped using graph convolutional networks (GCN) into the parameter vectors of a set of interdependent binary linear classifiers that can directly perform multi-label classification on health categories. Meanwhile, the adjacency matrix of the label graph is adaptively learned by self-attention (SA) from node feature vectors. In this way, a novel multi-label model based on SA and GCN (referred to as SA-GCN) is proposed for compound fault diagnosis of gearboxes. SA-GCN mainly consists of a ResNet-based fault feature learning module, an SA-based adjacency matrix learning module, and a GCN-based multi-label classifier learning module. The application results on two gearbox cases show that SA-GCN outperforms conventional single-label models as well as state-of-the-art multi-label models in terms of both the diagnostic accuracy of compound faults and the overall diagnostic accuracy. Moreover, the effects of internal modules and hyperparameters on SA-GCN are also investigated.

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“…In other words, this set of classifiers can be directly characterized by the GCN output, i.e. H (2) ∈ R C×D .…”

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

Section: Multi-label Classifier Learning Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A compound fault diagnosis model for gearboxes using correlation information between single faults

Zeng,

Wang,

Cheng

et al. 2023

Meas. Sci. Technol.

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show abstract

“…Non-contact measurement technology mainly uses computer vision to detect the yarn roll. It can be divided into monocular vision method, stereo vision method, and deep learning method [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ].…”

Section: Related Workmentioning

confidence: 99%

The Detection of Yarn Roll’s Margin in Complex Background

Wang

Shi

et al. 2023

Sensors

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Online detection of yarn roll’s margin is one of the key issues in textile automation, which is related to the speed and scheduling of bobbin (empty yarn roll) replacement. The actual industrial site is characterized by uneven lighting, restricted shooting angles, diverse yarn colors and cylinder yarn types, and complex backgrounds. Due to the above characteristics, the neural network detection error is large, and the contour detection extraction edge accuracy is low. In this paper, an improved neural network algorithm is proposed, and the improved Yolo algorithm and the contour detection algorithm are integrated. First, the image is entered in the Yolo model to detect each yarn roll and its dimensions; second, the contour and dimensions of each yarn roll are accurately detected based on Yolo; third, the diameter of the yarn rolls detected by Yolo and the contour detection algorithm are fused, and then the length of the yarn rolls and the edges of the yarn rolls are calculated as measurements; finally, in order to completely eliminate the error detection, the yarn consumption speed is used to estimate the residual yarn volume and the measured and estimated values are fused using a Kalman filter. This method overcomes the effects of complex backgrounds and illumination while being applicable to different types of yarn rolls. It is experimentally verified that the average measurement error of the cylinder yarn diameter is less than 8.6 mm, and the measurement error of the cylinder yarn length does not exceed 3 cm.

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A new framework for intelligent fault diagnosis of spiral bevel gears with unbalanced data

Wei

Han

et al. 2023

Appl Intell

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Novel motor fault detection scheme based on one-class tensor hyperdisk

Cited by 26 publications

References 34 publications

A compound fault diagnosis model for gearboxes using correlation information between single faults

A compound fault diagnosis model for gearboxes using correlation information between single faults

The Detection of Yarn Roll’s Margin in Complex Background

A new framework for intelligent fault diagnosis of spiral bevel gears with unbalanced data

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