Probabilistic life prediction for FCG degradation process of turbine disc with small sample data

2021 ◽  
Vol 120 ◽  
pp. 105026
Author(s):  
Xiangming Jin ◽  
Yingdong Song ◽  
Xi Liu ◽  
Dianyin Hu
Keyword(s):  
Life Prediction ◽  
Degradation Process ◽  
Small Sample ◽  
Sample Data ◽  
Turbine Disc

scholarly journals Residual Life Prediction of Lithium Battery Based on Small Sample Data Sets

2021 ◽  
Vol 2030 (1) ◽  
pp. 012048
Author(s):  
Meng Zhou ◽  
Zhigang Lv ◽  
Ye Li ◽  
RuoHai Di ◽  
Hongjie Zhu ◽  
...  
Keyword(s):  
Life Prediction ◽  
Lithium Battery ◽  
Residual Life ◽  
Small Sample ◽  
Data Sets ◽  
Sample Data ◽  
Residual Life Prediction

scholarly journals Automobile tire life prediction based on image processing and machine learning technology

2021 ◽  
Vol 13 (3) ◽  
pp. 168781402110027
Author(s):  
Jianchen Zhu ◽  
Kaixin Han ◽  
Shenlong Wang
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Life Prediction ◽  
Traffic Accidents ◽  
Confusion Matrix ◽  
Small Sample ◽  
Image Feature ◽  
Engineering Practice ◽  
Learning Technology ◽  
K Nearest Neighbor

With economic growth, automobiles have become an irreplaceable means of transportation and travel. Tires are important parts of automobiles, and their wear causes a large number of traffic accidents. Therefore, predicting tire life has become one of the key factors determining vehicle safety. This paper presents a tire life prediction method based on image processing and machine learning. We first build an original image database as the initial sample. Since there are usually only a few sample image libraries in engineering practice, we propose a new image feature extraction and expression method that shows excellent performance for a small sample database. We extract the texture features of the tire image by using the gray-gradient co-occurrence matrix (GGCM) and the Gauss-Markov random field (GMRF), and classify the extracted features by using the K-nearest neighbor (KNN) classifier. We then conduct experiments and predict the wear life of automobile tires. The experimental results are estimated by using the mean average precision (MAP) and confusion matrix as evaluation criteria. Finally, we verify the effectiveness and accuracy of the proposed method for predicting tire life. The obtained results are expected to be used for real-time prediction of tire life, thereby reducing tire-related traffic accidents.

Creep Life Prediction of Aircraft Turbine Disc Alloy Using Continuum Damage Mechanics

2017 ◽  
Vol 38 (1) ◽  
pp. 25-30
Author(s):  
Yan-Feng Li ◽  
Zhisheng Zhang ◽  
Chenglin Zhang ◽  
Jie Zhou ◽  
Hong-Zhong Huang
Keyword(s):  
Numerical Analysis ◽  
Test Data ◽  
Life Prediction ◽  
Damage Mechanics ◽  
Continuum Damage Mechanics ◽  
Creep Model ◽  
Continuum Damage ◽  
Creep Life ◽  
Creep Life Prediction ◽  
Turbine Disc

Abstract This paper deals with the creep characteristics of the aircraft turbine disc material of nickel-base superalloy GH4169 under high temperature. From the perspective of continuum damage mechanics, a new creep life prediction model is proposed to predict the creep life of metallic materials under both uniaxial and multiaxial stress states. The creep test data of GH4169 under different loading conditions are used to demonstrate the proposed model. Moreover, from the perspective of numerical simulation, the test data with analysis results obtained by using the finite element analysis based on Graham creep model is carried out for comparison. The results show that numerical analysis results are in good agreement with experimental data. By incorporating the numerical analysis and continuum damage mechanics, it provides an effective way to accurately describe the creep damage process of GH4169.

Multisensor Degradation Data Fusion and Remaining Life Prediction

2017 ◽  
Author(s):  
Changxi Wang ◽  
E. A. Elsayed ◽  
Kang Li ◽  
Javier Cabrera
Keyword(s):  
Data Fusion ◽  
Prediction Accuracy ◽  
Life Prediction ◽  
Clustering Algorithm ◽  
Partial Information ◽  
Degradation Process ◽  
Sensor Data ◽  
Multiple Sensors ◽  
Degradation Data ◽  
Degradation Monitoring

Multiple sensors are commonly used for degradation monitoring. Since different sensors may be sensitive at different stages of the degradation process and each sensor data contain only partial information of the degraded unit, data fusion approaches that integrate degradation data from multiple sensors can effectively improve degradation modeling and life prediction accuracy. We present a non-parametric approach that assigns weights to each sensor based on dynamic clustering of the sensors observations. A case study that involves a fatigue-crack-growth dataset is implemented in order evaluate the prognostic performance of the unit. Results show that the fused path obtained with the proposed approach outperforms any individual sensor data and other paths obtained with an adaptive threshold clustering algorithm in terms of life prediction accuracy.

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.

scholarly journals A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform

2021 ◽  
Vol 13 (23) ◽  
pp. 4864
Author(s):  
Langfu Cui ◽  
Qingzhen Zhang ◽  
Liman Yang ◽  
Chenggang Bai
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Prediction Method ◽  
Sliding Window ◽  
Small Sample ◽  
Grey Theory ◽  
Sensing System ◽  
Performance Change ◽  
Sample Data ◽  
Platform System

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

scholarly journals Abnormal Detection to Big Data Using Deep Neural Networks

2021 ◽  
Vol 2068 (1) ◽  
pp. 012025
Author(s):  
Jian Zheng ◽  
Zhaoni Li ◽  
Jiang Li ◽  
Hongling Liu
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Risk Model ◽  
Small Sample ◽  
Small Samples ◽  
Small Data ◽  
Data Segmentation ◽  
Sample Data ◽  
Segmentation Algorithms ◽  
The Common

Abstract It is difficult to detect the anomalies in big data using traditional methods due to big data has the characteristics of mass and disorder. For the common methods, they divide big data into several small samples, then analyze these divided small samples. However, this manner increases the complexity of segmentation algorithms, moreover, it is difficult to control the risk of data segmentation. To address this, here proposes a neural network approch based on Vapnik risk model. Firstly, the sample data is randomly divided into small data blocks. Then, a neural network learns these divided small sample data blocks. To reduce the risks in the process of data segmentation, the Vapnik risk model is used to supervise data segmentation. Finally, the proposed method is verify on the historical electricity price data of Mountain View, California. The results show that our method is effectiveness.

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