A Remaining Useful Life Prediction Method with Automatic Feature Extraction for Aircraft Engines

Deng, Kunyuan; Zhang, Xiaoyong; Cheng, Yijun; Zheng, Zhiyong; Fu, Jiang; Liu, Weirong; Peng, Jun

doi:10.1109/trustcom/bigdatase.2019.00097

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Among these data-driven models, different types of deep learning (DL) models have shown the most promising results in RUL estimation. Most common DL architectures for RUL estimation include Convolutional Neural Networks (CNNs) (Zhu, Chen, Peng, 2018), (Li, ding, and Sun, 2018), (Ren, Sun, and Wang 2018), Recurrent Neural Networks (RNNs) (Zhang, Xiong, Hem and Pecht, 2018), (Song, Li, Peng, and Liu, 2018), (Deng, Zhang, Cheng, Zheng, Jiang, Liu, and Peng, 2019), Transformers (Ding and Jia 2021), Auto-Encoders (AE) (Ren, Sun, Cui, and Zhang, 2018) and Deep Belief Networks(DBN) (Zhang, Lim, Qin, and Tan, 2017). Figure 2.…”

Section: Prognostic Modelsmentioning

confidence: 99%

Operational Prognostic Model Evaluation

Prakash,

Vuckovic,

Amin

2023

PHMAP_CONF

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Prognostic analytic models have become a viable way to reduce operational interruptions when sufficient timely data is available. This work describes a set of evaluation metrics which can characterize model performance as a degradation estimate and as a decision enabler. The model accuracy over time is assessed against a correlation with the remaining useful life. This yields both a prediction accuracy and confidence interval. The decision can be based on the level of confidence around the prediction, which is based on both how far into the future the event is predicted and how well the current health and its deterioration is estimated. With an effective means of evaluating prognostic models, better benchmarks can be established to communicate model effectiveness and appropriately schedule routine service.

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Section: Prognostic Modelsmentioning

confidence: 99%

Operational Prognostic Model Evaluation

Prakash,

Vuckovic,

Amin

2023

PHMAP_CONF

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“…The features extracted by these two networks are then combined and further processed by an additional LSTM network and a fully connected layer which predicts the RUL. Deng et al (2019) propose a method based on the combination of stacked SAEs and a GRU model. The AE is used for automatic feature extraction and the GRU is used to model the mapping from the features extracted by the AE to the RUL values.…”

Section: Hybridmentioning

confidence: 99%

“…Hybrid approaches have been also applied in the context of fault prognosis. For instance, the literature contains examples of AE + RNN ( Lal Senanayaka et al, 2018 ; Deng et al, 2019 ) and CNN + RNN ( Zhao et al, 2017 ; Mao et al, 2018 ; Li et al, 2019b ) combinations. In Zhao et al (2017) sensory data from milling machine cutters are processed by a novel technique combining a CNN component and an LSTM network.…”

Section: Artificial Intelligence-based Prognostic and Health Managemementioning

confidence: 99%

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Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

Biggio

Kastanis

2020

Front. Artif. Intell.

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Prognostic and Health Management (PHM) systems are some of the main protagonists of the Industry 4.0 revolution. Efficiently detecting whether an industrial component has deviated from its normal operating condition or predicting when a fault will occur are the main challenges these systems aim at addressing. Efficient PHM methods promise to decrease the probability of extreme failure events, thus improving the safety level of industrial machines. Furthermore, they could potentially drastically reduce the often conspicuous costs associated with scheduled maintenance operations. The increasing availability of data and the stunning progress of Machine Learning (ML) and Deep Learning (DL) techniques over the last decade represent two strong motivating factors for the development of data-driven PHM systems. On the other hand, the black-box nature of DL models significantly hinders their level of interpretability, de facto limiting their application to real-world scenarios. In this work, we explore the intersection of Artificial Intelligence (AI) methods and PHM applications. We present a thorough review of existing works both in the contexts of fault diagnosis and fault prognosis, highlighting the benefits and the drawbacks introduced by the adoption of AI techniques. Our goal is to highlight potentially fruitful research directions along with characterizing the main challenges that need to be addressed in order to realize the promises of AI-based PHM systems.

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“…where x j i and x j i denote the j-th feature value of the i-th sample before and after standardization, respectively.x j and σ j denote the mean and standard deviation of the j-th feature values of all samples, respectively [28].…”

Section: Feature Extraction and Dimensionality Reductionmentioning

confidence: 99%

A Semi-Supervised Tri-CatBoost Method for Driving Style Recognition

et al. 2020

Self Cite

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Driving style recognition plays a key role in ensuring driving safety and improving vehicle traffic efficiency. With the development of sensing technology, data-driven methods are more widely uesd to recognize driving style. However, adequately labeling data is difficult for supervised learning methods, while the classification accuracy is not sufficiently approved for unsupervised learning methods. This paper proposes a new driving style recognition method based on Tri-CatBoost, which takes CatBoost as base classifier and effectively utilizes the semi-supervised learning mechanism to reduce the dependency on data labels and improve the recognition ability. First, statistical features were extracted from the velocity, acceleration and jerk signals to fully characterize the driving style. The kernel principal component analysis was used to perform nonlinear feature dimension reduction to eliminate feature coupling. CatBoost is an ensemble of symmetric decision trees whose symmetry structure endows it fewer parameters, faster training and testing, and a higher accuracy. Then, a Tri-Training strategy is employed to integrate the base CatBoost classifiers and fully exploit the unlabeled data to generate pseudo-labels, by which the base CatBoost classifiers are optimized. To verify the effectiveness of the proposed method, a large number of experiments are performed on the UAH DriveSet. When the labeling ratio is 50%, the macro precision of Tri-CatBoost is 0.721, which is 15.7% higher than that of unsupervised K-means, 1.6% higher than that of supervised GBDT, 3.7% higher than that of Self-Training, 0.7% higher than that of Co-training, 1.5% higher than that of random forest, 6.7% higher than that of decision tree, and 4.0% higher than that of multilayer perceptron. The macro recall of Tri-CatBoost is 0.744, which is also higher than other methods. The experimental results fully demonstrate the superiority of this work in reducing label dependency and improving recognition performance, which indicates that the proposed method has broad application prospects.

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A Remaining Useful Life Prediction Method with Automatic Feature Extraction for Aircraft Engines

Cited by 5 publications

References 12 publications

Operational Prognostic Model Evaluation

Operational Prognostic Model Evaluation

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

A Semi-Supervised Tri-CatBoost Method for Driving Style Recognition

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