Temporal Convolutional Network Based Regression Approach for Estimation of Remaining Useful Life

Li, Rongze; Chu, Zhengtian; Jin, Wangkai; Wang, Yaohua; Hu, Xiao

doi:10.1109/icphm51084.2021.9486528

Cited by 8 publications

(8 citation statements)

References 16 publications

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“…In the current walking part bolt posture and quantity detection system, according to Al-Kahwati [1], a typical belt conveyor system includes component-level degradation models, estimation schemes for the remaining useful life and degradation rate, and vision-based detection of hazardous objects. Li, RZ et al [11] proposed a method for estimating TCN in RUL, which showed excellent results.…”

Section: Related Workmentioning

confidence: 99%

“…In general, 𝑁1=𝑁2 take a semi-supervised method to learn a classifier f(𝑥 𝑖 )-> 𝑦 𝑖 , to make the classification accuracy as high as possible. In this paper, three types of loss functions are used, as shown in equation (9), equation (10), and equation (11). There are three main types of loss functions, namely the classification loss function 𝐿 𝑐𝑙𝑠 , the defined loss of intra-class and between class ratio 𝐿 𝑟 , and the probability distribution loss function of two fully connected layers 𝐿 𝑓𝑐 .…”

Section: Tsml-net For Condition Monitoringmentioning

confidence: 99%

“…In fact, the smaller the ratio between the distance between classes and the distance between classes, the better. As a general rule, the equation is as follows (11):…”

Section: Tsml-net For Condition Monitoringmentioning

confidence: 99%

See 2 more Smart Citations

Camera-based PHM method in rotating machinery equipment micro-action scenarios

An¹,

liu²,

Hao³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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The health operation of rotating machinery guarantees safety of the project. To ensure a good operating environment, current subway equipment inspections frequency is high, resulting in a waste of resources. Small abnormal changes in mechanical equipment will also contribute to the development of mechanical component defects, which will ultimately lead to the failure of the equipment. Therefore, mechanical equipment defects should be detected and diagnosed as soon as possible. Through the use of graphic processing and deep learning, this paper proposes Rmcad Framework with three aspects: condition monitoring, anomaly detection, defect early warning. Using a network algorithm, this paper proposes an improved model that has the characteristics of two-stream and multi-loss functions, which improves the accuracy of detection. Additionally, a defect warning method is constructed to improve the perception ability of equipment before failure occurs and reduce the frequency of frequent maintenance by detecting anomalies according to the degree of opening.

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Section: Related Workmentioning

confidence: 99%

Section: Tsml-net For Condition Monitoringmentioning

confidence: 99%

See 1 more Smart Citation

Camera-based PHM method in rotating machinery equipment micro-action scenarios

An¹,

liu²,

Hao³

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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show abstract

“…In recent research, it was shown that TCN often outperforms LSTM as it is using both the advantages of an RNN, by processing high-level features, as well as the low-level feature computation from Convolutional Neural Networks (CNN) [29][30][31][32]. By using a radically different network architecture compared to RNNs, TCN is also overcoming any gradient vanishing problems, that can occur in deep networks with very long input sequences [33]. The two main features of a TCN are:…”

Section: Temporal Convolutional Networkmentioning

confidence: 99%

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Ruttner¹,

Hohensinn²,

D’Aronco³

et al. 2022

Preprint

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<p>Global Navigation Satellite System (GNSS) long-term residual height time series exhibit signals related to environmental influences. These can partly b explained through environmental surface loads, which are described with physical models. In this work, a model is computed to connect the GNSS residuals with raw meteorological parameters. A Temporal Convolutional Network (TCN) is trained on 206 GNSS stations in central Europe, and applied to 68 test stations in the same area. The resulting Root Mean Square (RMS) error reduction is on average 0.8% lower for the TCN modeled time series, compared to using physical models for the reduction. In a further experiment, the TCN is trained on the GNSS time series after reducing those by the surface loading models. The aim is a further increase of RMS reduction, which is achieved with 2.7% on average, resulting in an overall mean reduction of 28.6%. The results suggest that with meteorological features as input data, TCN modeled reductions are able to compete with reductions derived from physical models. Trained on the residuals reduced by environmental loading models, the TCN is able to slightly increase the overall reduction of variations in the GNSS station position time series.</p>

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Section: Temporal Convolutional Networkmentioning

confidence: 99%

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

et al. 2021

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Long-term Global Navigation Satellite System (GNSS) height residual time series contain signals that are related to environmental influences. A big part of the residuals can be explained by environmental surface loadings, expressed through physical models. This work aims to find a model that connects raw meteorological parameters with the GNSS residuals. The approach is to train a Temporal Convolutional Network (TCN) on 206 GNSS stations in central Europe, after which the resulting model is applied to 68 test stations in the same area. When comparing the Root Mean Square (RMS) error reduction of the time series reduced by physical models, and, by the TCN model, the latter reduction rate is, on average, 0.8% lower. In a second experiment, the TCN is utilized to further reduce the RMS of the time series, of which the loading models were already subtracted. This yields additional 2.7% of RMS reduction on average, resulting in a mean RMS reduction of 28.6% overall. The results suggests that a TCN, using meteorological features as input data, is able to reconstruct the reductions almost on the same level as physical models. Trained on the residuals, reduced by environmental loadings, the TCN is still able to slightly increase the overall reduction of variations in the GNSS station position time series.

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Temporal Convolutional Network Based Regression Approach for Estimation of Remaining Useful Life

Cited by 8 publications

References 16 publications

Camera-based PHM method in rotating machinery equipment micro-action scenarios

Camera-based PHM method in rotating machinery equipment micro-action scenarios

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

Modeling of Residual GNSS Station Motions through Meteorological Data in a Machine Learning Approach

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