Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery
By Lahiru Jayasinghe, Tharaka Samarasinghe, Chau Yuen, Shuzhi Sam Ge. 2018
69 citations recorded by the source corpus.
Accurately estimating the remaining useful life (RUL) of industrial machinery is beneficial in many real-world applications. Estimation techniques have mainly utilized linear models or neural network based approaches with a focus on short term time dependencies. This paper, introduces a system model that incorporates temporal convolutions with both long term and short term time dependencies. The proposed network learns salient features and complex temporal variations in sensor values, and predicts the RUL. A data augmentation method is used for increased accuracy. The proposed method is compared with several state-of-the-art algorithms on publicly available datasets. It demonstrates promising results, with superior results for datasets obtained from complex environments.
How researchers see this paper
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- Billy Pik Lik Lau
- Marakkalage Sumudu Hasala
- Chau Yuen
- Yuren Zhou
- Naveed Ul L. Hassan
- Lahiru Jayasinghe
- Meng Zhang
- Samith Abeywickrama
Citations in the corpus
- A survey of data fusion in smart city applications (2019, background): “Thus, preventive maintenance has been studied in [124], [125], [175], [176] and attempts to predict the remaining useful life (RUL) of a machine accurately.”
- RF-based Direction Finding of UAVs Using DNN (2017, background): “A single switching cycle is equivalent to N activations, starting from the first antenna to the N − th antenna.”