Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan

Viale, Luca; Daga, Alessandro Paolo; Fasana, Alessandro; Garibaldi, Luigi

doi:10.1016/j.ymssp.2023.110154

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…This paper conducted experiments using monitoring samples from four datasets, FD001 to FD004 [26,27]. Figure 6 illustrates the degradation process of the monitoring parameters for engine 32 in the FD001 dataset as the operating cycles increase, with the final lifecycle being the actual failure time of the engine.…”

Section: Selection Of Key Parametersmentioning

confidence: 99%

Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

Wang

et al. 2023

Applied Sciences

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The accurate prediction of the remaining useful life (RUL) of aircraft engines is crucial for improving engine safety and reducing maintenance costs. To tackle the complex issues of nonlinearity, high dimensionality, and difficult-to-model degradation processes in aircraft engine monitoring parameters, a new method for predicting the RUL of aircraft engines based on the random forest algorithm and a Bayes-optimized multilayer perceptron (MLP) was proposed here. First, the random forest algorithm was used to evaluate the importance of historical monitoring parameters of the engine, selecting the key features that significantly impact the engine’s lifetime operation cycle. Then, the single exponent smoothing (SES) algorithm was introduced for smoothing the extracted features to reduce the interference of original noise. Next, an MLP-based RUL prediction model was established using a neural network. The Bayes’ online parameter updating formula was used to solve the objective function and return the optimal parameters of the MLP training model and the minimum value of the evaluation index RMSE. Finally, the probability density function of the predicted RUL value of the aircraft engine was calculated to obtain the RUL prediction results.The effectiveness of the proposed method was verified and analyzed using the C-MAPSS dataset for turbofan engines. Experimental results show that, compared with several other methods, the RMSE of the proposed method in the FD001 test set decreases by 6.1%, demonstrating that the method can effectively improve the accuracy of RUL prediction for aircraft engines.

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Section: Selection Of Key Parametersmentioning

confidence: 99%

Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

Wang

et al. 2023

Applied Sciences

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“…Traditional time-domain vibration signal fault feature extraction methods mainly include standard statistical characteristics of signals, kurtosis, skewness, envelope spectrum, and frequency spectrum, as well as their improved algorithms [6], which are used to construct various artificial feature values as inputs for fault classification algorithms through these timedomain fault feature extraction methods. The traditional fault classification algorithms are mainly represented by machine learning (ML) models such as support vector machine (SVM) [7,8], naive Bayesian (NB) algorithm [9], and k-nearest neighbor (KNN) algorithm [10,11], typically need to be combined with feature extraction methods. Therefore, traditional fault diagnosis algorithms are often limited in specific scenarios: (a) relying on manual feature extraction.…”

Section: Introductionmentioning

confidence: 99%

ASG-HOMGAT: a high-order multi-head graph attention network with adaptive small graph structure for rolling bearing fault diagnosis

Ding,

Chen,

Liu

et al. 2024

Meas. Sci. Technol.

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Traditional Euclidean spatial data processing is difficult to capture the inherent relationships of unstructured data such as bearing vibration signals. Representing vibration signals in graphical form helps to preserve their topologi-cal structure and temporal information. Secondly, most existing graph convolutional network methods are based on large graph structured data, which incurs certain memory overhead when aggregating high-order neighborhood node information and ignores important information between samples in the global graph structure. To address these issues, this paper proposes a high-order multi-head graph attention network based on an adaptive small graph struc-ture (ASG-HOMGAT) for fault diagnosis of rolling bearings. Firstly, the adaptive preprocessing layer is used to adaptively denoise and compress the one-dimensional time-domain vibration signal, generating small rule graph data with topological structure. Then, these small graph structured data samples are input into a higher-order graph neural network, which aggregates features from multiple higher-order neighborhoods to achieve richer feature repre-sentations and fully explore the intrinsic correlation between samples. Finally, these features are aggregated into a reinforced representation of graph nodes through a multi head attention mechanism, and a SoftMax classifier is used for fault classification. The experimental results show that the ASG-HOMGAT method has better performance com-pared to mainstream graph neural network diagnostic models. The code and model will be released at: https://github.com/ding-ss/ASG-HOMGAT.

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Supervised Machine Learning Algorithms for the Analysis of Ship Engine Data

Dimitriou,

Skondras,

Hitiris

et al. 2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan

Cited by 10 publications

References 35 publications

Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

Remaining Useful Life Prediction of Aircraft Turbofan Engine Based on Random Forest Feature Selection and Multi-Layer Perceptron

ASG-HOMGAT: a high-order multi-head graph attention network with adaptive small graph structure for rolling bearing fault diagnosis

Supervised Machine Learning Algorithms for the Analysis of Ship Engine Data

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