A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Avendano, Diego Nieves; Vandermoortele, Nathan; Soete, Colin; Moens, Pieter; Ompusunggu, Agusmian Partogi; Deschrijver, Dirk; Hoecke, Sofie Van

doi:10.3390/s22041590

Cited by 16 publications

(8 citation statements)

References 33 publications

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“…In general, our method has obvious advantages, which can predict the RUL of cutting bearings dynamically and accurately. To further illustrate the effectiveness of the proposed method, the performance of the proposed method is compared with that of the other three methods (SSDM, LSMM, MCNM) [23,27] and [28]. Figure 13 shows the comparison results.…”

Section: Resultsmentioning

confidence: 99%

“…Among the above data-driven prediction methods [19][20][21][22], the degradation prediction method has attracted wide attention from engineers due to its simple mathematical analysis process [23]. Degradation models can be divided into linear degradation models (LDMs) and exponential degradation models [24].…”

Section: Introductionmentioning

confidence: 99%

“…However, it is difficult to find the incipient diagnosis of roadheaders underground due to the complex operating conditions. Nieves Avendano et al [23] obtained a semi-supervised approach improved by the degradation model (SSDM). However, the SSDM approach is only demonstrated with publicly available data, and its effects are unknown in real experiments.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A novel exponential degradation approach for predicting the remaining useful life of roadheader bearings

Liu¹,

Liu²,

Dai³

et al. 2022

Meas. Sci. Technol.

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Based on the optimized exponential-degradation model (OEDM), a novel approach for predicting the remaining useful life (RUL) of roadheader bearings under different working conditions was proposed in this study. Specifically, the exponential process was used to construct the degradation process of a single performance characteristic under variable operating conditions, the Generalized Expectation Maximization was employed to estimate model parameters, and the proposed degradation model was updated after new data was available. In the traditional exponential degradation method, the hyperparameters are only optimized, which leads to low calculation accuracy under severe working conditions. In the proposed method, the Bayesian algorithm and the Drift Brownian motion algorithm were respectively employed to optimize hyperparameters and stochastic parameters to ensure the high accuracy of the prediction results. Besides, degradation characteristics were combined with sensory data acquired through condition monitoring were used to continuously update the remaining useful life in the proposed degradation model. Finally, the effectiveness of the proposed model was verified by the simulation case and the case study. The results show that compared with the linear Degradation Model (LDM) and general exponential degradation model (GEDM), the proposed OEDM performs well in practical applications and has a higher prediction accuracy. This study provides a reference for predictive maintenance of critical parts of tunneling machinery and cost reduction of tunneling.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel exponential degradation approach for predicting the remaining useful life of roadheader bearings

Liu¹,

Liu²,

Dai³

et al. 2022

Meas. Sci. Technol.

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show abstract

“…Obtaining precise labels based on real-time bearing conditions can be exceedingly challenging in practical settings, thus making the semi-supervised approach a valuable asset, effectively utilizing datasets where only a fraction have labels. In this paper, considering the pragmatic utility of the outcome, we delve deeper into and apply this latter approach [39] [40] [41].…”

Section: Bearing Fault Classificationmentioning

confidence: 99%

Fault Classification in Predictive Maintenance

Roehrich,

Raffaele

2023

Preprint

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In the field of predictive maintenance, “smart condition monitoring” extends beyond merely assessing the present state of manufacturing equipment. In today's landscape, businesses and research endeavors strive for deeper insights into equipment conditions, seeking to uncover potential causes behind issues or anticipate the remaining lifespan. This paper examines an approach focused on classifying anomalous behavior of bearings and map them to historically similar fault patterns. This study aims to contribute valuable insights to the field of condition monitoring and predictive maintenance practices.

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“…To ensure cost-effective operation and management of aircraft pulsation production lines, prognostics and health management (PHM) have gained increasing attentions. By continuously monitoring and analyzing the running status parameters of equipment, it is possible to detect equipment degradation states, enabling the estimation of the remaining useful life (RUL) and maximizes the operational availability and security of equipment [2,3].…”

Section: Introductionmentioning

confidence: 99%

A Model Based on CNN-LSTM for the Remaining Life Prediction of Equipment in Aircraft Assembly Pulsation Production Line

Liu,

Xiao,

Wang

et al. 2024

J. Phys.: Conf. Ser.

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The aircraft assembly pulsation production line is an advanced and efficient assembly method widely used in aircraft manufacturing. However, equipment malfunctions would occur and can disrupt production takt, affecting the production efficiency. Therefore, accurately predicting the remaining useful life (RUL) of equipment is very crucial. To meet the requirements of both prediction accuracy and efficiency for RUL model used in aircraft pulsation production line, this work proposed a model combining convolutional neural network (CNN) and long short-term memory (LSTM) for RUL prediction. Optimization was performed for the number of neurons in the proposed model with genetic algorithm (GA) to regulate the prediction accuracy and efficiency. Two public datasets representing typical equipment in the pulsation production line were used to validate the proposed model. The results show that the proposed model outperforms the traditional model with substantial improvements in the fitness function of 19.8%, and 30.2% for the two testing datasets. These findings demonstrate the effectiveness of the proposed model in enhancing the accuracy and efficiency of RUL prediction.

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A Semi-Supervised Approach with Monotonic Constraints for Improved Remaining Useful Life Estimation

Cited by 16 publications

References 33 publications

A novel exponential degradation approach for predicting the remaining useful life of roadheader bearings

A novel exponential degradation approach for predicting the remaining useful life of roadheader bearings

Fault Classification in Predictive Maintenance

A Model Based on CNN-LSTM for the Remaining Life Prediction of Equipment in Aircraft Assembly Pulsation Production Line

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