A novel autoencoder model is proposed for machine health monitoring. LSTM unit is used to capture sequential information from multi-sensor time series data. Convolution calculation is utilized for noise reduction and feature extraction. Health index (HI) is generated based on reconstruction error of run-to-fail data.
This paper proposes a novel autoencoder (AE), called long short-term memory convolutional autoencoder (LSTMCAE), where LSTM and convolutional units are embedded ...
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Sep 14, 2023 · The LSTM-based deep stacked sequence-to-sequence autoencoder is specifically designed to learn deep discriminative features from the time-series ...
A competitive learning-based approach to long-term prognosis of machine health status is presented, showing that the developed technique is more accurate in ...
Mar 31, 2021 · Health condition monitoring of machines based on long short-term memory convolutional autoencoder ... A long short-term memory (LSTM) unit in ...
Jan 27, 2021 · We propose a framework for RM condition monitoring and anomaly detection based on Long short-term memory (LSTM). arXiv:2101.11539v1 [cs.LG] ...
May 18, 2024 · In this context, a predictive maintenance method is proposed based on the combination of LSTM-Autoencoders and a Transformer encoder in order to ...
Here, the first study about a empirical evaluation of LSTMs-based machine health monitoring ... vibration based fault trends and recurrent neural networks,” J ...
In this article, we propose a DL-based approach for activity recognition with smartphone sensor data, i.e., accelerometer and gyroscope data. Convolutional ...
An autoencoder model-based method for condition monitoring of rotating machines by using an anomaly detection approach that learns the characteristics of a ...