Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments

Oh, Seokju; Han, Seugmin; Jeong, Jongpil

doi:10.3390/app11093963

Cited by 18 publications

(9 citation statements)

References 41 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2006, Hinton et al [19] introduced the concept of deep learning. Deep learning methods mainly include convolutional neural networks [20][21][22], deep belief networks [23,24], and recurrent neural networks [25,26], etc. Compared with traditional fault diagnosis methods, deep learning methods can automatically extract feature information from the original signals, and they have rapidly introduced into fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis model based on DSCB-NFAM

Zhao,

Guo

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Machine learning techniques have had great success in fault diagnosis. However, the traditional machine learning methods rely heavily on manual priori knowledge leading to poor fault diagnosis results in rolling bearing fault diagnosis. Deep learning techniques can improve the accuracy of fault intelligent diagnosis with the help of automatic extraction of fault features. In this article, a method of smart fault diagnosis for rolling bearings based on depth-separable convolutional block (DSCB)-non-local feature awareness module (NFAM) is proposed. Firstly, a DSCB is constructed, which can reduce the number of parameters and effectively alleviate the gradient disappearance phenomenon. Then a NFAM is constructed to enhance the global sensing ability while adaptively adjusting the weight of each channel. Further, a feature extraction layer is constructed to extend the fault feature of the input information. Finally, a feature pyramid approach is used to fuse the extracted information for output. The validity of the suggested method is tested on both of the bearing datasets. By comparing with several mainstream intelligent diagnosis algorithms, the results of the experiments show that the proposed method has better advancement under complex conditions such as variable load and variable noise.

show abstract

Section: Introductionmentioning

confidence: 99%

Rolling bearing fault diagnosis model based on DSCB-NFAM

Zhao,

Guo

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Motivated by the aforementioned anomaly detection task, it is necessary to sufficiently consider multi-scale features and spatiotemporal continuity, which are essential for recognising abnormal behaviours. Recently, lots of works have achieved great detection performance by using multi-scale features of images [ 11 , 12 ]. Owing to the camera position and angle, objects multi-scale features extraction can effectively improve the performance of target detection.…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal prediction and reconstruction network for video anomaly detection

et al. 2022

View full text Add to dashboard Cite

The existing anomaly detection methods can be divided into two popular models based on reconstruction or future frame prediction. Due to the strong learning capacity, reconstruction approach can hardly generate significant reconstruction errors for anomalies, whereas future frame prediction approach is sensitive to noise in complicated scenarios. Therefore, a solution has been proposed by balancing the merits and demerits of the two models. However, most methods relied on single-scale information to capture spatial features and lacked temporal continuity between the video frames, affecting anomaly detection accuracy. Thus, we propose a novel method to improve anomaly detection performance. Because of the objects of various scales in each video, we select different receptive fields to extract comprehensive spatial features by the hybrid dilated convolution (HDC) module. Meanwhile, the deeper bidirectional convolutional long short-term memory (DB-ConvLSTM) module can remember the temporal information between the consecutive frames. Experiments prove that our method can detect abnormalities in various video scenes more accurately than the state-of-the-art methods in the anomaly-detection task.

show abstract

“…Worker fatigue is caused by repetitive work. To address these issues, many studies have been conducted on automation and defect detection [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems

Jeong

2021

Applied Sciences

Self Cite

View full text Add to dashboard Cite

Currently, the development of automated quality inspection is drawing attention as a major component of the smart factory. However, injection molding processes have not received much attention in this area of research because of product diversity, difficulty in obtaining uniform quality product images, and short cycle times. In this study, we proposed a defect inspection system for injection molding in edge intelligence. Using data augmentation, we solved the data shortage and imbalance problem of small and medium-sized enterprises (SMEs), introduced the actual smart factory method of the injection process, and measured the performance of the developed artificial intelligence model. The accuracy of the proposed model was more than 90%, proving that the system can be applied in the field.

show abstract

Multi-Scale Convolutional Recurrent Neural Network for Bearing Fault Detection in Noisy Manufacturing Environments

Cited by 18 publications

References 41 publications

Rolling bearing fault diagnosis model based on DSCB-NFAM

Rolling bearing fault diagnosis model based on DSCB-NFAM

Spatio-temporal prediction and reconstruction network for video anomaly detection

CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems

Contact Info

Product

Resources

About