Roller Bearing Failure Analysis using Gaussian Mixture Models and Convolutional Neural Networks

Rathore, Maan Singh; Harsha, S. P.

doi:10.1007/s11668-022-01469-8

Cited by 7 publications

(1 citation statement)

References 31 publications

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“…Among them, 𝑋 represents the j-th element in the input data, s represents the step size of the pooling layer, and 𝑀 represents the output after the pooling operation. LSTM (Long Short-Term Memory) [8] is a Recurrent Neural Network (RNN) architecture commonly used to process sequence data. LSTM networks utilize gating mechanisms to effectively control the flow and storage of information, thereby better capturing long-term dependencies and achieving significant performance improvements in many sequence modeling tasks [9] .…”

Section: Related Workmentioning

confidence: 99%

Underwater propeller fault diagnosis based on deep learning

Wei,

Liu

2024

International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023)

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When an underwater robot performs a task, the propeller is most likely to malfunction, such as being entangled by foreign objects or the blades are damaged. At present, its fault diagnosis methods have problems such as relying on manual feature extraction and using neural networks with low accuracy. Therefore, this paper proposes an integration based on an improved one-dimensional convolutional neural network (1D-CNN) and a long short-term memory network (LSTM). Thruster fault diagnosis method. By analyzing thruster data, accurate diagnosis of four different thruster faults can be achieved. A comparative experiment was conducted between the proposed model and some traditional algorithm models. The results show that the proposed method has greatly improved the test accuracy, and this method can effectively diagnose underwater robot thruster faults.

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Section: Related Workmentioning

confidence: 99%