scholarly journals Health indicator construction and remaining useful life prediction for space Stirling cryocooler

2019 ◽  
Vol 11 (12) ◽  
pp. 168781401989673
Author(s):  
Lei Song ◽  
Haoran Liang ◽  
Wei Teng ◽  
Lili Guo
Keyword(s):  
Life Prediction ◽  
Thermal Environment ◽  
Health Indicators ◽  
Degradation Process ◽  
Space Mission ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Indicator Method ◽  
Aerospace Applications ◽  
Useful Life

Stirling cryocoolers are widely used to refrigerate significant facilities in military and aerospace applications. However, under the influences of high-frequency piston motion and thermal environment deterioration, the refrigerating performance of Stirling cryocoolers will worsen inevitably, thus affecting the successful accomplishment of space mission. In this article, a methodology on assessing the performance of space Stirling cryocoolers is proposed, which involves the analysis of the failure mechanism, health indicator construction and remaining useful life prediction of the cryocooler. The potential factors affecting the refrigerating performance are discussed first. In view of these, three health indicators representing the degradation process of cryocoolers are constructed and then a multi-indicator method based on particle filter is proposed for remaining useful life prediction. Finally, the proposed method is validated by a Stirling cryocooler from one retired aircraft, and the results show that the constructed health indicators and remaining useful life prediciton approaches are effective for performance assessment of Stirling cryocooler.

Remaining useful life prediction of nonlinear degradation process based on EKF

2021 ◽  
Author(s):  
Yubing Wang ◽  
Guo Xie ◽  
Jing Yang ◽  
Yu Liu ◽  
Xinhong Hei ◽  
...  
Keyword(s):  
Life Prediction ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Useful Life

scholarly journals Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881718 ◽  
Author(s):  
Wentao Mao ◽  
Jianliang He ◽  
Jiamei Tang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Life Prediction ◽  
Short Term Memory ◽  
Degradation Process ◽  
Remaining Useful Life ◽  
Feature Representation ◽  
Short Term ◽  
Term Memory ◽  
Useful Life ◽  
Long Short Term Memory

For bearing remaining useful life prediction problem, the traditional machine-learning-based methods are generally short of feature representation ability and incapable of adaptive feature extraction. Although deep-learning-based remaining useful life prediction methods proposed in recent years can effectively extract discriminative features for bearing fault, these methods tend to less consider temporal information of fault degradation process. To solve this problem, a new remaining useful life prediction approach based on deep feature representation and long short-term memory neural network is proposed in this article. First, a new criterion, named support vector data normalized correlation coefficient, is proposed to automatically divide the whole bearing life as normal state and fast degradation state. Second, deep features of bearing fault with good representation ability can be obtained from convolutional neural network by means of the marginal spectrum in Hilbert–Huang transform of raw vibration signals and health state label. Finally, by considering the temporal information of degradation process, these features are fed into a long short-term memory neural network to construct a remaining useful life prediction model. Experiments are conducted on bearing data sets of IEEE PHM Challenge 2012. The results show the significance of performance improvement of the proposed method in terms of predictive accuracy and numerical stability.

A recurrent neural network based health indicator for remaining useful life prediction of bearings

2017 ◽  
Vol 240 ◽  
pp. 98-109 ◽  
Author(s):  
Liang Guo ◽  
Naipeng Li ◽  
Feng Jia ◽  
Yaguo Lei ◽  
Jing Lin
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Useful Life

scholarly journals PCA based health indicator for remaining useful life prediction of wind turbine gearbox

2019 ◽  
Vol 29 ◽  
pp. 31-36
Author(s):  
Sabareesh G R ◽  
Hemanth Mithun Praveen ◽  
Divya Shah ◽  
Krishna Dutt Pandey ◽  
Vamsi I
Keyword(s):  
Wind Turbine ◽  
Life Prediction ◽  
Remaining Useful Life ◽  
Health Indicator ◽  
Wind Turbine Gearbox ◽  
Useful Life

scholarly journals Outlier Modeling in Gear Bearing Using Autoencoder for Remaining Useful Life Prediction

2019 ◽  
Author(s):  
Sunny Singh ◽  
Praneet Shiv ◽  
Atif Ahmed
Keyword(s):  
Life Prediction ◽  
Health Management ◽  
Health Indicators ◽  
Remaining Useful Life ◽  
System Failure ◽  
Mechanical Signal ◽  
Long Time ◽  
Data Points ◽  
Useful Life ◽  
Bearing System

In this paper, we introduce the Prognostics and Health Management of gear bearing system using autoencoder neural networks. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. This study presents an outlier modeling method for forecasting the gear bearing system failure using the health indicators constructed from mechanical signal processing and modeling. Outlier modeling aims to find patterns in data that are significantly different from what is defined as normal. In the unsupervised outlier modeling setting, prior labels about the anomalousness of data points are not available. In such cases, the most common techniques for scoring data points for outlyingness include distance-based methods density-based methods, and linear methods. The conventional outlier modeling methods have been used for a long time to detect anomalous observations in data. However, this paper shows that autoencoders are a very competitive technique compared to other existing methods. The developed method is demonstrated using the IMS bearing data from NASA Acoustics and Vibration Database.

scholarly journals A Scheme with Acoustic Emission Hit Removal for the Remaining Useful Life Prediction of Concrete Structures

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7761
Author(s):  
Tuan-Khai Nguyen ◽  
Zahoor Ahmad ◽  
Jong-Myon Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Recurrent Neural Network ◽  
Life Prediction ◽  
Deep Neural Network ◽  
Remaining Useful Life ◽  
Support Vector ◽  
Health Indicator ◽  
Removal Process ◽  
Useful Life

In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of accidental damage. The deterioration process is portrayed by the health indicator (HI), which is automatically constructed from raw AE data with a deep neural network pretrained and fine-tuned by a stacked autoencoder deep neural network (SAE-DNN). For the deep neural network structure to perform a more accurate construction of health indicator lines, a hit removal process with a one-class support vector machine (OC-SVM), which has not been investigated in previous studies, is proposed to extract only the hits which matter the most to the portrait of deterioration. The new set of hits is then harnessed as the training labels for the deep neural network. After the completion of the health indicator line construction, health indicators are forwarded to a long short-term memory recurrent neural network (LSTM-RNN) for the training and validation of the remaining useful life prediction, as this structure is capable of capturing the long-term dependencies, even with a limited set of data. Our prediction result shows a significant improvement in comparison with a similar scheme but without the hit removal process and other methods, such as the gated recurrent unit recurrent neural network (GRU-RNN) and the simple recurrent neural network.

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