Ecarrnet: An Efficient Lstm Based Ensembled Deep Neural Network Architecture for Railway Fault Detection

Eunus, Salman Ibne; Hossain, Shahriar; Ridwan, A.  E. M.; Adnan, Ashik; Islam, Md. Saiful; Karim, Dewan  Ziaul; Rabiul, Md. Golam

doi:10.2139/ssrn.4515106

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…The rail defect dataset utilized in this article is obtained from the Kaggle website's Railway Track Fault Detection public dataset [18]. The dataset comprises 1400 images of rail fasteners, sleepers, and rail surface defects.…”

Section: Data Augmentationmentioning

confidence: 99%

“…If the parameter is true, the residual structure is utilized to add the principal branch and residual branch feature maps, the CSP structure can optimise the repetition of the gradient while reducing the computational complexity; the SPPF module serially computes three feature maps of the same scale using maximum pooling, thereby reducing the image scale progressively. Neck adopt the architecture of PAN [18], with the bottom layer feature map containing rich image semantic information due to upsampling from the top down by feature pyramid network (FPN), and the top layer features containing image location information due to downsampling from the bottom up by PAN. FPN interacts with PAN to effectively fuse feature map information at different depths.…”

Section: Yolov5 Network Architecturementioning

confidence: 99%

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ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Gao,

Gu,

Yin

et al. 2024

Meas. Sci. Technol.

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To solve the problems of easy miss and false detection on rail surface defects caused by small size, dense target, and high similarity between features and background, this paper proposed an improved detection algorithm in complex background. First, the conventional convolution of YOLOv5 backbone network is replaced with omni-dimensional dynamic convolution (ODConv), which improves the feature extraction capability of the network without increasing the computational cost; second, to improve the model’s performance in detecting tiny objects, a two-layer context augmentation module (CAM) is introduced into the path aggregation network (PAN) structure; finally, the traditional non-maximum suppression (NMS) algorithm is replaced by the Soft-NMS algorithm in the network post-processing to reduce the false-alarm and miss-rate. The experimental results on the Railway Track Fault Detection public dataset show that the OD-YOLO (OD stands for ODConv) and C-PAN (CAM module is introduced into PAN) structures could achieve better performance in the same type of improved algorithms; compared with the baseline algorithm YOLOv5, the ODCS-YOLO (OD stands for ODConv, C stands for CAM and S stands for Soft-NMS) algorithm improves the precision by 12.4%, the recall by 3.6%, the map50 by 8.6% and the GFLOPs is reduced by 0.6. Compared with seven classical object detection algorithms, the ODCS-YOLO algorithm achieves the highest detection accuracy, which makes it able to meet the real-time detection requirements of rail surface defects in real working conditions. The ODCS-YOLO model provides certain technical support for the defects detection and a new method for the detection of dense small objects.

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Section: Data Augmentationmentioning

confidence: 99%

Section: Yolov5 Network Architecturementioning

confidence: 99%

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

Gao,

Gu,

Yin

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Rustam et al [36] propose a method for detecting railway track faults using machine learning and deep learning models, in which acoustic data are analyzed to identify different types of railway track faults. Eunus et al [37] combine convolutional autoencoders, a ResNet-based RNN, and LSTM to analyze images of railway tracks for detection. Wang et al [38] applied single-layer LSTM to model fixed-axis gearbox vibration signals and used the residual to detect a bore crack.…”

Section: Introductionmentioning

confidence: 99%

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

Chen,

Liu,

Fan

et al. 2024

Machines

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The timely detection of faults that occur in industrial machines and components can avoid possible catastrophic machine failure, prevent large financial losses, and ensure the safety of machine operators. A solution to tackle the fault detection problem is to start with modeling the condition monitoring signals and then examine any deviation of real-time monitored data from the baseline model. The newly developed deep long short-term memory (LSTM) neural network has a high nonlinear flexibility and can simultaneously store long- and short-term memories. Thus, deep LSTM is a good option for representing underlying data-generating processes. This paper presents a deep-LSTM-based fault detection method. A goodness-of-fit criterion is innovatively used to quantify the deviation between the baseline model and the newly monitored vibrations as opposed to the mean squared value of the LSTM residual used in many reported works. A railway suspension fault detection case is studied. Benchmark studies have shown that the deep-LSTM-based fault detection method performs better than the vanilla-LSTM-based and linear-autoregression-model-based methods. Using the goodness-of-fit criterion, railway suspension faults can be better detected than when using the mean squared value of the LSTM residual.

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Ecarrnet: An Efficient Lstm Based Ensembled Deep Neural Network Architecture for Railway Fault Detection

Cited by 2 publications

References 16 publications

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

ODCS-YOLO detection algorithm for rail surface defects based on omni-dimensional dynamic convolution and context augmentation module

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

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