Fault detection for vaccine refrigeration via convolutional neural networks trained on simulated datasets

Abhiraman, Bhaskar; Fotis, Riley; Eskin, Leo D.; Rubin, Harvey

doi:10.1016/j.ijrefrig.2022.12.019

Cited by 9 publications

(1 citation statement)

References 19 publications

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“…Additionally, convolutional neural networks have strong advantages in terms of training capacity and hardware support, and are therefore widely adopted in various fields [11][12][13]. Due to the limitations of convolutional operations, the perception ability of convolutional networks is severely weakened when depth features are extracted.…”

Section: Introductionmentioning

confidence: 99%

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Lang,

Wang

2024

Eng. Res. Express

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Pipeline leakage detection is an integral part of pipeline integrity management. CombiningAE with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold.Finally, the pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals, and automatically learns and acquires the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy.The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

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Section: Introductionmentioning

confidence: 99%