2017 Prognostics and System Health Management Conference (PHM-Harbin)最新文献_第8页

Deep neural network for manufacturing quality prediction 制造质量预测的深度神经网络

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079165

Yun Bai, Chuan Li, Zhenzhong Sun, Haibin Chen

{"title":"Deep neural network for manufacturing quality prediction","authors":"Yun Bai, Chuan Li, Zhenzhong Sun, Haibin Chen","doi":"10.1109/PHM.2017.8079165","DOIUrl":"https://doi.org/10.1109/PHM.2017.8079165","url":null,"abstract":"Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this issue, a deep neural network (DNN), consisting of a deep belief network (DBN) in the bottom and a regression layer on the top, is proposed in this paper. The DBN uses a greedy algorithm for unsupervised feature learning. It could learn effective features for manufacturing quality prediction in an unsupervised pattern which has been proven to be effective for many fields. Then the learned features are inputted into the regression tool, and the quality predictions are obtained. One type of manufacturing system with multi-parameter is investigated by the proposed DNN model. The experiments show that the DNN has good performance of the deep architecture, and overwhelms the peer shallow models. It is recommended from this study that the deep learning technique is more promising in manufacturing quality prediction.","PeriodicalId":281875,"journal":{"name":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127499098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 24

Research on vibration fatigue of carbon fibre-reinforced composites under non-Gaussian random load 非高斯随机载荷作用下碳纤维增强复合材料振动疲劳研究

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079154

Zhengwei Fan, Yu Jiang, Shufeng Zhang, J. Tao, Xun Chen

引用次数: 7

Incipient fault feature extraction of main bearing by iterative squared envelope analysis 基于迭代平方包络分析的主轴承早期故障特征提取

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079183

Ming An-bo, Zhang Wei, He Hao-hao, Xie Xin-yu, Chu Fu-lei

引用次数: 2

Influence of element parameters on SV-wave EMAT phased arrays directivity 元件参数对sv波EMAT相控阵指向性的影响

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079155

Bao Liang, G. Zhai, Kaican Wang, Jin-Xu Yang

引用次数: 1

Comparison study on general methods for modeling lifetime data with covariates 用协变量对寿命数据建模的一般方法的比较研究

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079122

H. Liao, Samira Karimi

{"title":"Comparison study on general methods for modeling lifetime data with covariates","authors":"H. Liao, Samira Karimi","doi":"10.1109/PHM.2017.8079122","DOIUrl":"https://doi.org/10.1109/PHM.2017.8079122","url":null,"abstract":"Lifetime data with covariates (e.g., temperature, humidity, and electric current) are frequently seen in science and engineering. An important example is accelerated life testing (ALT) data. In ALT, test units of a product are exposing to severer-than-normal conditions to expedite product failure. The resulting lifetime and/or censoring data with covariates are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and the life-stress relationship selected cannot adequately describe the underlying failure process, the resulting reliability prediction will be misleading. This paper develops a new method for modeling lifetime data with covariates using phase-type (PH) distributions and a general life-stress relationship formulation. A numerical study is presented to compare the performance of this method with a mixture of Weibull distributions model. This general method creates a new direction for modeling and analyzing lifetime data with covariates for situations where the data-generating mechanisms are unknown or difficult to analyze using existing parametric ALT models and statistical tools.","PeriodicalId":281875,"journal":{"name":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116552037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonlinear time transformation-based vibration signal reconstruction for blade tip-timing under rotation speed fluctuations 转速波动下基于非线性时间变换的叶片叶尖定时振动信号重建

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079152

Chi Zhan, Zhongsheng Chen, Zheng Hu, Yongmin Yang, Haifeng Hu

{"title":"Nonlinear time transformation-based vibration signal reconstruction for blade tip-timing under rotation speed fluctuations","authors":"Chi Zhan, Zhongsheng Chen, Zheng Hu, Yongmin Yang, Haifeng Hu","doi":"10.1109/PHM.2017.8079152","DOIUrl":"https://doi.org/10.1109/PHM.2017.8079152","url":null,"abstract":"Blade tip-timing (BTT) is a promising method to monitor blade vibrations of aero engines. Since blade tip-timing signals are typically under-sampled, how to reconstruct vibration characteristics from under-sampled signals is a big challenge. Existing signal reconstruction methods are mainly based on the assumption of constant rotation speeds and equally mounted sensors. However, fluctuation of blade rotation speed is inevitable in engineering applications. In this case, the BTT sampling process should be non-uniform, which causes conventional reconstruction methods to be unavailable. In order to solve this problem, this paper presents an improved under-sampled reconstruction method under rotation speed fluctuations by using three equally-mounted BTT sensors. Firstly, speed fluctuations are defined as random variations of sampling times. Here, both Gaussian and uniform distributions are considered. Then, a nonlinear time transformation equation is proposed to project BTT sampling times to another timeline so that non-uniform BTT sampling times become uniform ones. The key is to fit the discrete-time time variations to a continuous-time function and linear interpolation is used here. Based on the above uniform sampled signals, an interpolation function using the six-order B-spline function is built to reconstruct vibration signals. Finally, numerical simulations are done to verify the proposed method. The results demonstrate that this method can eliminate the effects of rotation speed fluctuations and improve the accuracy of BTT signal reconstruction under rotation speed fluctuations.","PeriodicalId":281875,"journal":{"name":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121141855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A method of reliability synthetic modeling considering multi-feature for APMS 考虑多特征的APMS可靠性综合建模方法

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079243

Sha Qin, Fuqiu Li, Xiaopeng Li, Wenming Zhou, Wei Zhang, Lu Chen

引用次数: 0

A big data framework for spacecraft prognostics and health monitoring 用于航天器预测和健康监测的大数据框架

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079320

Xiao Zhang, Panfeng Wu, Chao Tan

引用次数: 3

A method for fault diagnosis of electronic products considering sub-health state 考虑亚健康状态的电子产品故障诊断方法

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079287

W. An

引用次数: 0

Application of EWT AR model and FCM clustering in rolling bearing fault diagnosis EWT AR模型和FCM聚类在滚动轴承故障诊断中的应用

2017 Prognostics and System Health Management Conference (PHM-Harbin) Pub Date : 2017-07-01 DOI: 10.1109/PHM.2017.8079270

Jipu Li, R. Zhao, Linfeng Deng

{"title":"Application of EWT AR model and FCM clustering in rolling bearing fault diagnosis","authors":"Jipu Li, R. Zhao, Linfeng Deng","doi":"10.1109/PHM.2017.8079270","DOIUrl":"https://doi.org/10.1109/PHM.2017.8079270","url":null,"abstract":"A fault diagnosis method is proposed, which is based on Empirical Wavelet Transform (EWT), Auto-Regressive (AR) model and Fuzzy C-Mean clustering (FCM) clustering algorithm, in order to solve the problem of fault category is difficult to identify of rolling bearing fault signal. In this method, the original signal of the rolling bearing is decomposed by the EWT, and several AM-FM components are obtained. The AR model is established for each AM-FM component, and the original feature subset is constructed. Then, through the correlation analysis, the four AM-FM components are extremely correlated with the original vibration signal are selected and their AR models are established. Construction of high-dimensional feature subsets based on the auto-regressive parameters of AR model. Finally, using the Locality Preserving Projection (LPP) algorithm to reduce the dimension and enter the low-dimensional feature subset to the FCM clustering, in order to achieve fault diagnosis of bearings. Experiments show that the fault identification method which is proposed in this paper has certain advantages and the fault recognition effect is better.","PeriodicalId":281875,"journal":{"name":"2017 Prognostics and System Health Management Conference (PHM-Harbin)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116454464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1