Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets

Ramasso, Emmanuel

doi:10.36001/phme.2014.v2i1.1460

Cited by 10 publications

(1 citation statement)

References 27 publications

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“…In order to fulfil this assumption, the component or equipment is in normal condition for a longer period of time throughout its life cycle. Therefore, it is considered a segmented linear behaviour for the degradation dynamics that is applied to the C-MAPSS data from the turbofan engines, as described in Ramasso (2014). In most cases, once the RUL index assumes values around 116, the health state of the equipment starts to change at this point, so the maximum RUL is limited to about 116 in the experiments of this paper.…”

Section: Methodsmentioning

confidence: 99%

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

Simani¹,

Lam²,

Farsoni³

et al. 2023

JDSIS

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The prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Recently, the abilities of feature classification and automated extraction of neural networks in its convolutional forms have shown fascinating performance when used for estimating the remaining useful life of dynamic processes using deep learning structures. This was accomplished by putting these talents to the task of predicting how long the procedures would be beneficial. Existing network topologies, on the other hand, virtually entirely extract features at a single scale while neglecting important information at other sizes. Meanwhile, because of the architecture of a single network path, the comprehensiveness of the features discovered by these tools is limited. To address these concerns, the authors propose a network structure based on a feature fusion strategy on a parallel multiscale architecture. This structure is then utilized to compute the remaining useful life. This prototype is divided into two sections: the first is a multiscale feature extraction module designed to extract local information features, and the second is a causal convolution module designed to extract global information features by combining multi-layer causal convolution with average pooling. The multiscale feature extraction module is intended for the extraction of local information features, while the causal convolution module is intended for the extraction of global information features. Finally, the two distinct paths are joined to create a fully integrated layer. The simulations and results show that this method has the potential to improve the efficiency and accuracy of estimating the remaining useful life index. Furthermore, the advantages of the established strategy are shown by comparing the results obtained with those produced by applying cutting-edge techniques on a well-known data-set depicting a simulated turbofan engine.

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Section: Methodsmentioning

confidence: 99%

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

Simani¹,

Lam²,

Farsoni³

et al. 2023

JDSIS

View full text Add to dashboard Cite

show abstract

An ensemble model considering health index based classification for remaining useful life prediction

Han

2022

Quality & Reliability Eng

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Accurate prediction of remaining useful life (RUL) plays an important role in the formulation of maintenance strategies. However, due to the diversity of the failure mode of equipment, there are significant differences between the degradation data, which greatly affects the accuracy of RUL prediction. In this case, an ensemble prediction model considering health index-based (HI-based) classification is proposed in this paper. Firstly, the stacked autoencoder (SAE) is employed to construct the HI. Then, the time window is used to sequentially process the HI sequence, so that many data segments with the same length can be achieved. To differentiate the data with the similar degradation process, K-means and Xgboost are selected to construct offline and online data classification models respectively. Finally, according to the results of the data classification, the ensemble model that integrates multiple machine learning methods are separately trained and then adaptively used for RUL prediction. In addition, integrating multiple methods helps to improve the generalization ability of the model. The NASA C-MAPSS dataset is applied to verify the effectiveness of the proposed method, and the results show that the proposed method achieves a higher prediction accuracy and shorter computational time than other existing prediction models.

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Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes

Bai

Zhao

Rong

2019

Neural Comput & Applic

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Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets

Cited by 10 publications

References 27 publications

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes

An ensemble model considering health index based classification for remaining useful life prediction

Novel direct remaining useful life estimation of aero-engines with randomly assigned hidden nodes

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