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- W3211367073 abstract "To increase plant equipment efficiency and reduce plant operation costs, as well as to address the problem of industrial critical equipment health diagnosis, we suggest developing a deep learning-based LSTM model by merging data from pressure sensors while they are operational. By modifying the learning rate parameter of the model and judging the correctness of the model with different parameter values by integrating the data after pre-processing, the LSTM model with a learning rate of 0.006, a number of nodes in the hidden layer of 8, and a number of neurons of 2 was selected. The ideal model is used to predict the remaining life of the pressure sensor, demonstrating that the model has the ability to predict with high accuracy and provides effective strategies and maintenance plans for plant equipment inspection in conjunction with health degradation photos." @default.
- W3211367073 created "2021-11-22" @default.
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- W3211367073 date "2021-07-23" @default.
- W3211367073 modified "2023-10-16" @default.
- W3211367073 title "A Model for Pressure Sensor Health Diagnosis and Prediction Using a LSTM Algorithm" @default.
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- W3211367073 doi "https://doi.org/10.1145/3480001.3480015" @default.
- W3211367073 hasPublicationYear "2021" @default.
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