Remaining Useful Life Prediction of Airplane Engine Based on PCA–BLSTM

Ji, Shixin; Han, Xuehao; Hou, Yichun; Song, Yun Heub; Du, Qingfu

doi:10.3390/s20164537

Cited by 22 publications

(7 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rosa et al [29] introduced a generic fault prognosis framework employing LSTM-based autoencoder feature learning methods, emphasizing the semi-supervised extrapolation of reconstruction errors to address imbalanced data in an industrial context. Ji et al [30] proposed a hybrid model for accurate airplane engine failure prediction, integrating principal component analysis (PCA) for feature extraction and BiLSTM for learning the relationship between the sensor data and RUL. Peng et al [31] introduced a dual-channel LSTM neural network model for predicting the RUL of machinery, addressing challenges related to noise impact in complex operations and diverse abnormal environments.…”

Section: Related Literaturementioning

confidence: 99%

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan,

Li,

Chang

2024

Sensors

View full text Add to dashboard Cite

The accurate estimation of the remaining useful life (RUL) for aircraft engines is essential for ensuring safety and uninterrupted operations in the aviation industry. Numerous investigations have leveraged the success of the attention-based Transformer architecture in sequence modeling tasks, particularly in its application to RUL prediction. These studies primarily focus on utilizing onboard sensor readings as input predictors. While various Transformer-based approaches have demonstrated improvement in RUL predictions, their exclusive focus on temporal attention within multivariate time series sensor readings, without considering sensor-wise attention, raises concerns about potential inaccuracies in RUL predictions. To address this concern, our paper proposes a novel solution in the form of a two-stage attention-based hierarchical Transformer (STAR) framework. This approach incorporates a two-stage attention mechanism, systematically addressing both temporal and sensor-wise attentions. Furthermore, we enhance the STAR RUL prediction framework by integrating hierarchical encoder–decoder structures to capture valuable information across different time scales. By conducting extensive numerical experiments with the CMAPSS datasets, we demonstrate that our proposed STAR framework significantly outperforms the current state-of-the-art models for RUL prediction.

show abstract

Section: Related Literaturementioning

confidence: 99%

A Two-Stage Attention-Based Hierarchical Transformer for Turbofan Engine Remaining Useful Life Prediction

Fan,

Li,

Chang

2024

Sensors

View full text Add to dashboard Cite

show abstract

“…RNN is well suited to temporal data because it has the ability to recall past input information over time. Ji et al (2020) propose a hybrid model for predicting the RUL of an airplane engine called PCA-BLSTM. To achieve the RUL prediction, the PCA is first used to lower data dimension to extract features, and then combined with multilayer BLSTM can extract the internal relationship of state monitoring data.…”

Section: Related Workmentioning

confidence: 99%

STCGCN: a spatio-temporal complete graph convolutional network for remaining useful life prediction of power transformer

Xing

Ding

Zhang

et al. 2023

IJWIS

View full text Add to dashboard Cite

Purpose Remaining useful life (RUL) prediction for power transformer maintenance is a challenging task on heterogeneous data. Monitoring data of power transformers are not always compatible or in an identical format; therefore, RUL predictions traditionally work separately on different data. Moreover, chemical molecules used in RUL prediction can be transformed into each other under different conditions, thus forming a complete graph with uncertain adjacency matrix (UAM). This study aims to find and evaluate a new model to achieve better results of RUL prediction than the other baselines. Design/methodology/approach In this work, the authors propose a spatiotemporal complete graph convolutional network (STCGCN) for RUL prediction in two branches, in which daily and hourly features are extracted from correlated heterogeneous data separately. This study provides a thorough evaluation of the proposed model on real-world data and compare the proposed model with state-of-the-art RUL prediction models. Findings By using the multibranch structure and EucCos similarity aggregation, STCGCN was able to capture the dynamic spatiotemporal patterns on a variety of heterogeneous data and obtain more accurate prediction results, compared to other time series prediction methods. Originality/value In this work, the authors propose a novel multibranch structure to compute feature maps from two heterogeneous data sources efficiently and a novel similarity aggregation method to compute the spatial UAM within the complete graph. Compared with traditional time series prediction models, the model pays attention to the spatial relationships in time series data.

show abstract

“…Deep RNNs based on long short-term memory networks (LSTM) have also shown excellent results with regard to the prognostics problem [7,8,10,36,42]. Therefore, we also adopted a deep LSTM architecture as an RNN model.…”

Section: Deep Learning Prognostics Modelmentioning

confidence: 99%