2019 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第5页

Data-driven prognostics of remaining useful life for milling machine cutting tools 铣床刀具剩余使用寿命的数据驱动预测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819400

Yen-Chun Liu, Yuan-Jen Chang, Sheng-Liang Liu, Szu-Ping Chen

引用次数: 12

Unsupervised Fault Detection in Varying Operating Conditions 变工况下的无监督故障检测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819383

Gabriel Michau, Olga Fink

{"title":"Unsupervised Fault Detection in Varying Operating Conditions","authors":"Gabriel Michau, Olga Fink","doi":"10.1109/ICPHM.2019.8819383","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819383","url":null,"abstract":"Training data-driven approaches for complex industrial system health monitoring is challenging. When data on faulty conditions are rare or not available, the training has to be performed in a unsupervised manner. In addition, when the observation period, used for training, is kept short, to be able to monitor the system in its early life, the training data might not be representative of all the system normal operating conditions. In this paper, we propose five approaches to perform fault detection in such context. Two approaches rely on the data from the unit to be monitored only: the baseline is trained on the early life of the unit. An incremental learning procedure tries to learn new operating conditions as they arise. Three other approaches take advantage of data from other similar units within a fleet. In two cases, units are directly compared to each other with similarity measures, and the data from similar units are combined in the training set. We propose, in the third case, a new deep-learning methodology to perform, first, a feature alignment of different units with an Unsupervised Feature Alignment Network (UFAN). Then, features of both units are combined in the training set of the fault detection neural network.The approaches are tested on a fleet comprising 112 units, observed over one year of data. All approaches proposed here are an improvement to the baseline, trained with two months of data only. As units in the fleet are found to be very dissimilar, the new architecture UFAN, that aligns units in the feature space, is outperforming others.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131935846","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: 17

A Physics-based prognostics approach for Tidal Turbines 基于物理的潮汐涡轮机预测方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819376

Fraser Ewing, P. Thies, J. Shek, C. Bittencourt

{"title":"A Physics-based prognostics approach for Tidal Turbines","authors":"Fraser Ewing, P. Thies, J. Shek, C. Bittencourt","doi":"10.1109/ICPHM.2019.8819376","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819376","url":null,"abstract":"Tidal Stream Turbines (TST) have the potential to become an important part of the sustainable energy mix. One of the main hurdles to commercialization is the reliability of the turbine components. Literature from the Offshore Wind sector has shown that the drive train and particularly the Pitch System (PS) are areas of frequent failures and downtime. The Tidal energy sector has much higher device reliability requirements than the wind industry because of the inaccessibility of the turbines. For Tidal energy to become commercially viable it is therefore crucial to make accurate reliability assessments to assist component design choices and to inform maintenance strategy. This paper presents a physics-based prognostics approach for the reliability assessment of Tidal Stream Turbines (TST) during operation. Measured tidal flow data is fed into a turbine hydrodynamic model to generate a synthetic loading regime which is then used in a Physics of Failure model to predict component Remaining Useful Life (RUL). The approach is demonstrated for the failure critical Pitch System (PS) bearing unit of a notional horizontal axis TST. It is anticipated that the approach developed here will enable device/project developers, technical consultants and third party certifiers to undertake robust reliability assessments both during turbine design and operational stages.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619138","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

Application of Deep Learning for Fault Diagnostic in Induction Machine’s Bearings 深度学习在感应电机轴承故障诊断中的应用

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819421

Nastaran Enshaei, F. Naderkhani

{"title":"Application of Deep Learning for Fault Diagnostic in Induction Machine’s Bearings","authors":"Nastaran Enshaei, F. Naderkhani","doi":"10.1109/ICPHM.2019.8819421","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819421","url":null,"abstract":"Recent developments in sensor technologies and advances in communication systems have resulted in deployment of a large number of sensors for collecting condition monitoring (CM) data in order to monitor health condition of a manufac-tring/industrial system. Efficient utilization of sensory data leads to highly accurate results in system fault diagnostics/prognostics. The exponential growth of CM data poses significant analytical challenges, due to their high variety, high dimensionality and high velocity rendering conventional health monitoring tools impractical. In this regard, the paper proposes a deep learning-based framework for fault diagnosis of an induction machine’s bearing based on real data set provided by Case Western Reserve University bearing data center. In particular, we focus on deep bidirectional long short-term memory (BiD-LSTM) networks fed with raw signals for fault diagnosis to address drawbacks of conventional machine learning (ML) solutions such as support vector machines. A numerical example is provided to illustrate the complete procedure of the proposed framework, which shows the great potentials of the BiD-LSTM for detection of different types of the bearing fault with high accuracy. The effectiveness of the proposed model is demonstrated through a comparison with a recently developed deep neural network (DNN) that considers temporal coherence for the same data set. The results indicate that the proposed framework provides considerably improved performance in comparison to its counterparts.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120944089","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: 10

Data-driven Prognosis of Fatigue-induced Delamination in Composites using Optical and Acoustic NDE methods 基于光学和声学无损检测方法的复合材料疲劳诱发分层数据驱动预测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819426

P. Banerjee, R. Palanisamy, M. Haq, L. Udpa, Y. Deng

{"title":"Data-driven Prognosis of Fatigue-induced Delamination in Composites using Optical and Acoustic NDE methods","authors":"P. Banerjee, R. Palanisamy, M. Haq, L. Udpa, Y. Deng","doi":"10.1109/ICPHM.2019.8819426","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819426","url":null,"abstract":"With increasing use of fiber reinforced polymer (FRP) composites in several industries such as aviation, automotive and construction, effective reliability analysis of composites has become imperative in recent years. Periodic inspection by robust non-destructive evaluation (NDE) techniques and accurate health prognosis is essential for condition-based maintenance (CBM) of the safety-critical components and structures. Prediction of future damage level in composites often becomes challenging due to lack of physics-based damage growth models for unknown materials which leaves us to rely solely on the NDE data for prognosis. In this study, delamination growth in glass fiber reinforced polymer (GFRP) joints, under Mode I cyclic loading, was monitored by guided waves(GW) using miniature surface-mounted piezoelectric wafers(PZT). Data-driven prognosis techniques such as Kalman filter and particle filter were implemented on the indirect CBM data obtained from GW signals to predict future delamination area and validated against optical transmission scans (OTS) of damaged samples. A comparison of data-driven prognosis methods with static regression versus dynamic update of estimated parameters is presented in this paper. Results show that even when a simple logarithmic fit is assumed, use of NDE data to estimate function parameters in a stochastic framework outperforms the static regression approach leading to a robust sensor-aided reliability analysis.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127090266","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: 5

Distributed Computational Architecture for Industrial Motion Control and PHM Implementation 工业运动控制的分布式计算体系结构与PHM实现

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8844228

Shrinivas Kulkarni, A. Guha, Suhas Dhakate, T.R. Milind

引用次数: 2

ICPHM 2019 Committees ICPHM 2019委员会

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/icphm.2019.8819372

引用次数: 0

Operating state evaluation of smart electricity meter based on data fusion method 基于数据融合方法的智能电表运行状态评估

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819431

Dan Xu, Jiaolan He, You Li

引用次数: 1

Deep Recurrent Convolutional Neural Network for Remaining Useful Life Prediction 基于深度循环卷积神经网络的剩余使用寿命预测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819440

Meng Ma, Z. Mao

{"title":"Deep Recurrent Convolutional Neural Network for Remaining Useful Life Prediction","authors":"Meng Ma, Z. Mao","doi":"10.1109/ICPHM.2019.8819440","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819440","url":null,"abstract":"Remaining Useful Life (RUL) prediction of rotating machinery plays a critical role in Prognostics and Health Management (PHM). Data-driven methods for RUL estimation have been widely developed because they don’t depend on much prior knowledge of the system. Recurrent neural network (RNN) is capable of modeling sequential data, which has been investigated for RUL prediction with statistical features of vibration signals in time domain and frequency domain. The drawback of utilizing statistical features is the ignorance of time-frequency information, which is critical in RUL prediction because the vibration signals are non-stationary when the fault occurs. To solve this problem, a novel deep architecture, named deep recurrent convolutional neural network (DRCNN) is proposed. By incorporating convolutional operation in the process of state transition of RNN, the spatial information in time-frequency domain can be automatically learned from the vibration signals, which contributes to the improvement of prediction performance. With convolutional operation in RNN, both spatial information in time-frequency domain and previous information are employed for RUL prediction. Furthermore, by stacking recurrent convolutional neural network layer by layer, the deep architecture can learn high-level features in the time-frequency domain. Finally, experimental analysis of RUL prediction using vibration signals of run-to-failure tests are carried out. Compared with the results of conventional deep RNN method, the proposed method shows its effectiveness and superiority.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128966657","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: 16

An Operation Condition-Matched Similarity Method for Remaining Useful Life Estimation with Dynamic Sample Fusion 基于动态样本融合的剩余使用寿命估算操作条件匹配相似性方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819381

Yuxuan Yang, Zhanbao Gao, Shu Zhang, Xu Long Li

{"title":"An Operation Condition-Matched Similarity Method for Remaining Useful Life Estimation with Dynamic Sample Fusion","authors":"Yuxuan Yang, Zhanbao Gao, Shu Zhang, Xu Long Li","doi":"10.1109/ICPHM.2019.8819381","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819381","url":null,"abstract":"Remaining useful life (RUL) estimation is a key technology in prognostics and health management (PHM). Considering the problem that operating condition (OC) is easily overlooked and sample fusion is mainly determined by experience, this paper presents an OC-matched similarity method with dynamic sample fusion. The method contains two main stages, including training stage for obtaining the library of OC-based degradation models and the trained parameter for dynamic sample fusion, and testing stage for RUL prediction. In the training stage, we extract the sensor data on account of the linear correlation coefficient and expand the library of degradation models through adding OC information. Then, cross-validation is implemented to train the parameter for dynamic sample fusion and parameter is optimally selected by minimizing the target function. When estimating RUL of test data, OC-matched similarity is measured by calculating the distance between test data and OC-matched model. Eventually, RUL is estimated by the weighted average of each sample based on the similarity measurement. This method is validated by the 2008 PHM Conference Challenge Data, which contains both sensor measurements and operating settings. The results have suggested significant improvement comparing with traditional similarity method.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"433 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115934588","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