A hierarchical scheme for remaining useful life prediction with long short-term memory networks

Song, Tao; Liu, Chao; Wu, Rui; Jin, Yunfeng; Jiang, Dongxiang

doi:10.1016/j.neucom.2022.02.032

Cited by 61 publications

(16 citation statements)

References 33 publications

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“…It can be seen from Figure 8 that it is the model with the most anticipated first average prediction time of all the units and adheres to the reference line of progression of the RUL in about 75% of the t eol . The MLP model visually manifested a behavior similar to the convolutional one and also presented a zone of forecast instability with high fractional RMSE but with time stamping metrics (fpt, H T (5) , and H T (20) ) later compared to the second. The LSTM model did not show a concentrated region of large prediction errors like the previous two, but it did show sparse peaks of high errors for two or three cycles in units 2, 16, 15, and 5.…”

Section: Results From Application Examplementioning

confidence: 78%

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Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

Rosa

Melani

Pereira

et al. 2022

Sensors

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This paper presents a generic framework for fault prognosis using autoencoder-based deep learning methods. The proposed approach relies upon a semi-supervised extrapolation of autoencoder reconstruction errors, which can deal with the unbalanced proportion between faulty and non-faulty data in an industrial context to improve systems’ safety and reliability. In contrast to supervised methods, the approach requires less manual data labeling and can find previously unknown patterns in data. The technique focuses on detecting and isolating possible measurement divergences and tracking their growth to signalize a fault’s occurrence while individually evaluating each monitored variable to provide fault detection and prognosis. Additionally, the paper also provides an appropriate set of metrics to measure the accuracy of the models, which is a common disadvantage of unsupervised methods due to the lack of predefined answers during training. Computational results using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) monitoring data show the effectiveness of the proposed framework.

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Section: Results From Application Examplementioning

confidence: 78%

“…The effectiveness of the proposed framework is tested using the Commercial Modular Aero Propulsion System Simulation (CMAPSS) for monitoring data, which is largely used for testing fault detection, diagnosis, and prognosis [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

Rosa

Melani

Pereira

et al. 2022

Sensors

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show abstract

“…There are many deep learning methods that have been successfully applied to intelligent fault prognostics, such as convolutional neural network (CNN) [4,5], temporal convolution network [6], long-short term memory [7,8], auto-encoder [9], recurrent neural network (RNN) [10], and gated recurrent unit (GRU) [11]. Intelligent learning models do not depend on special degradation knowledge, but they rely on sufficient historical data.…”

Section: Introductionmentioning

confidence: 99%

A novel vision transformer network for rolling bearing remaining useful life prediction

Hu,

Zhu,

Liu

et al. 2023

Meas. Sci. Technol.

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The accurate predictions of remaining useful life (RUL) have become a key and extremely challenging problem. Due to the limitations of the classical convolutional neural network (CNN) and recurrent neural network (RNN) structure, the attention mechanism has been introduced to improve feature representation of the long-term bearing degradation data. Transformer network based on attention mechanism is successfully applied in many fields and recognized as an excellent creation for deep learning models. In this paper, a novel lightweight mobile vision transformer (MobileViT) architecture based on deep networks is proposed for the RUL predictions. This new network is named prognostics separable vision Transformer (ProgSViT), which combines the separable convolution and MobileViT. In ProgSViT network, the separable convolutions are first constructed for extracting local feature from the input vibration signal, and the new vision transformer architecture is proposed to learn the global feature representations. In improved MobileViT model, the loss function is optimized, and a new training strategy is provided. Finally, the obtained features are input to the global average pool layers and the full connection layers to perform RUL estimation. Experiment results present the proposed ProgSViT network surpasses the other models in RUL predicting, which possesses higher precision and computational efficiency.

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“…Han et al [13] utilized a stacked autoencoder and RNN to predict the RUL of rolling bearings. Song et al [14] applied LSTM networks as machine RUL prediction models for processing time series and extracting recursive and non-recursive features. Zhang et al [15] presented a bidirectional gated recurrent unit (BiGRU) with temporal self-attention mechanism to predict the RUL.…”

Section: Introductionmentioning

confidence: 99%

Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

Che

Wang

et al. 2022

Meas. Sci. Technol.

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In order to reduce error accumulation caused by multistep modeling and achieve general accurate model, this paper proposes an end-to-end remaining useful life (RUL) prediction model based on multi-head self-attention bidirectional gated recurrent unit (BiGRU). Taking multivariable samples with long time series as model input and multistep RUL values as model output, the BiGRU model is constructed for continuous prediction of RUL. In addition, single-head self-attention models are applied for time series and variables of samples before or after the BiGRU, which can be fused into multi-head attention BiGRU. Aeroengine and rolling bearing are selected to testify the effectiveness of the proposed method from the system level and component level respectively. The results show that the proposed method can achieve end-to-end RUL prediction efficiently and accurately. Compared with single-head models and individual deep learning models, the prediction mean square error of the proposed method is reduced by 20%-70%.

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A hierarchical scheme for remaining useful life prediction with long short-term memory networks

Cited by 61 publications

References 33 publications

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

Semi-Supervised Framework with Autoencoder-Based Neural Networks for Fault Prognosis

A novel vision transformer network for rolling bearing remaining useful life prediction

Multi-head self-attention bidirectional gated recurrent unit for end-to-end remaining useful life prediction of mechanical equipment

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