In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based...In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.展开更多
The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necess...The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.展开更多
Surface integrity is the major factor impacting on the operation quality, service life and reliability of the aeroengine components. The surface integrity of aeroengine component is damaged by the failures such as cra...Surface integrity is the major factor impacting on the operation quality, service life and reliability of the aeroengine components. The surface integrity of aeroengine component is damaged by the failures such as crack,deformation, oxidation, corrosion, erosion, and microstructural degeneration. It adopts advanced remanufacturing technologies to restore or improve the surface integrity and regenerate these high value parts. This paper firstly puts forward the concept, namely surface integrity remanufacturing for aeroengine components, and its connotation. The key remanufacturing technologies have been developed to repair the components with surface damages. Ultimately, some application examples of surface integrity remanufacturing technologies as well as their effects in aeroengine maintenance are introduced. The discarded components have been reused and their service lives have been extended and their reliability has been increased by implementing surface integrity remanufacturing. It has realized "The Repaired Components Outpacing the New Ones", material saving, energy saving, and emission reduction.展开更多
基金supported by Jiangsu Social Science Foundation(No.20GLD008)Science,Technology Projects of Jiangsu Provincial Department of Communications(No.2020Y14)Joint Fund for Civil Aviation Research(No.U1933202)。
文摘In order to directly construct the mapping between multiple state parameters and remaining useful life(RUL),and reduce the interference of random error on prediction accuracy,a RUL prediction model of aeroengine based on principal component analysis(PCA)and one-dimensional convolution neural network(1D-CNN)is proposed in this paper.Firstly,multiple state parameters corresponding to massive cycles of aeroengine are collected and brought into PCA for dimensionality reduction,and principal components are extracted for further time series prediction.Secondly,the 1D-CNN model is constructed to directly study the mapping between principal components and RUL.Multiple convolution and pooling operations are applied for deep feature extraction,and the end-to-end RUL prediction of aeroengine can be realized.Experimental results show that the most effective principal component from the multiple state parameters can be obtained by PCA,and the long time series of multiple state parameters can be directly mapped to RUL by 1D-CNN,so as to improve the efficiency and accuracy of RUL prediction.Compared with other traditional models,the proposed method also has lower prediction error and better robustness.
基金supported by the National Natural Science Foundation of China under Grant No.60672184
文摘The vibration signals of an aeroengine are a very important information source for fault diagnosis and condition monitoring. Considering the nonstationarity and low repeatability of the vibration signals, it is necessary to find a corresponding method for feature extraction and fault recognition. In this paper, based on Independent Component Analysis （ICA） and the Discrete Hidden Markov Model （DHMM）, a new fault diagnosis approach named ICA-DHMM is proposed. In this method, ICA separates the source signals from the mixed vibration signals and then extracts features from them, DHMM works as a classifier to recognize the conditions of the aeroengine. Compared with the DHMM, which use the amplitude spectrum of mixed signals as feature parameters, experimental results show this method has higher diagnosis accuracy.
文摘Surface integrity is the major factor impacting on the operation quality, service life and reliability of the aeroengine components. The surface integrity of aeroengine component is damaged by the failures such as crack,deformation, oxidation, corrosion, erosion, and microstructural degeneration. It adopts advanced remanufacturing technologies to restore or improve the surface integrity and regenerate these high value parts. This paper firstly puts forward the concept, namely surface integrity remanufacturing for aeroengine components, and its connotation. The key remanufacturing technologies have been developed to repair the components with surface damages. Ultimately, some application examples of surface integrity remanufacturing technologies as well as their effects in aeroengine maintenance are introduced. The discarded components have been reused and their service lives have been extended and their reliability has been increased by implementing surface integrity remanufacturing. It has realized "The Repaired Components Outpacing the New Ones", material saving, energy saving, and emission reduction.
