2019 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第3页

Gearbox Fault Diagnostics Using Deep Learning with Simulated Data 基于模拟数据的深度学习齿轮箱故障诊断

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819423

Ozhan Gecgel, S. Ekwaro-Osire, J. Dias, Abdul Serwadda, Fisseha M. Alemayehu, Abraham Nispel

引用次数: 17

Research on the Propagation of Defects in Assembly Process Based on SIR Epidemic Model 基于SIR流行模型的装配过程缺陷传播研究

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819418

Mengyao Wu, W. Dai, Yu Zhao

引用次数: 2

Quantifying the Impact of Prognostic Distance on Average Cost per Cycle 量化预测距离对每周期平均成本的影响

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819408

Saikath Bhattacharya, L. Fiondella, Saurabh Saxena, M. Pecht

{"title":"Quantifying the Impact of Prognostic Distance on Average Cost per Cycle","authors":"Saikath Bhattacharya, L. Fiondella, Saurabh Saxena, M. Pecht","doi":"10.1109/ICPHM.2019.8819408","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819408","url":null,"abstract":"Prognostics and health management (PHM) is transforming reliability engineering with methods to enhance safety by accurately estimating end of useful life, thereby recommending maintenance of critical components and systems to manage cost. Previous studies emphasized degradation modeling and algorithms to improve state of health predictions. However, most of these techniques focused on improving the accuracy of predictions within a single maintenance interval, while fewer studies considered the effectiveness of alternative degradation models over multiple successive maintenance intervals. This paper develops a measure based on concepts from maintenance theory to provide a framework to objectively compare the effectiveness of existing and future battery degradation models over multiple maintenance intervals. The approach quantifies the impact of prognostic distance on average cost per cycle during the lifetime of a system. The approach is applied to state of health prediction for lithium-ion batteries, which are widely used in various mission-critical systems. The results indicate that the approach can be used to select a prognostic distance that minimizes average cost. The approach can thus evaluate models to select a suitable prognostic distance.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"40 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114046958","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

Unsupervised Anomaly Detection Using Variational Auto-Encoder based Feature Extraction 基于变分自编码器特征提取的无监督异常检测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819434

Rong Yao, Chongdang Liu, Linxuan Zhang, Peng Peng

引用次数: 38

Diagnosis Method for Hydro-generator Rotor Fault Based on Stochastic Resonance 基于随机共振的水轮发电机转子故障诊断方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819379

Junqing Li, Luo Wang, Yonggang Li

{"title":"Diagnosis Method for Hydro-generator Rotor Fault Based on Stochastic Resonance","authors":"Junqing Li, Luo Wang, Yonggang Li","doi":"10.1109/ICPHM.2019.8819379","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819379","url":null,"abstract":"The rotor of hydro-generator is in the state of rotary vibration. Rotor faults is a common fault in hydrogenerators. The fault is not easy to detect in the early stage, but with the development of the fault, it will pose a threat to the safe operation of hydro-generator. Many faults will change the vibration of generator rotor. In order to detect small fault signals, time frequency compression stochastic resonance method (FCSR) is proposed. This method uses vibration noise to enhance weak fault signal characteristics. The frequency range of stochastic resonance can be improved by the time-frequency compression algorithm. The algorithm eliminates the limitation of the system on the measurement signal frequency and extends the stochastic resonance system to the whole frequency band. In addition, according to the rotor vibration of the hydro-generator, the range of the relevant parameters of the method is improved. The stochastic resonance method is used to reduce the noise of hydrogenerator rotor vibration signal and improve the signal-to-noise ratio of the signal. This is conducive to the extraction of rotor fault feature vectors. The results show that the method can accurately identify the abnormal vibration of hydro-generator and has high rotor early fault diagnosis accuracy.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134040137","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: 3

A Novel Bearing Health Indicator Construction Method Based on Ensemble Stacked Autoencoder 一种基于集成堆叠自编码器的轴承健康指示器构建方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819405

Pengfei Lin, Jizhong Tao

引用次数: 19

A Hybrid Deep Learning Based Approach for Remaining Useful Life Estimation 基于混合深度学习的剩余使用寿命估计方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819435

Khaled Akkad, D. He

引用次数: 3

Electronic Circuit Diagnosis with No Data 无数据的电子电路诊断

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-17 DOI: 10.1109/ICPHM.2019.8819424

Varun Khemani, M. Azarian, M. Pecht

{"title":"Electronic Circuit Diagnosis with No Data","authors":"Varun Khemani, M. Azarian, M. Pecht","doi":"10.1109/ICPHM.2019.8819424","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819424","url":null,"abstract":"Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems. This research presents an approach in which fault diagnosis can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also employs the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in the literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches to diagnose circuit faults. This simulation-based fusion approach is a holistic framework for all types of analog electronic circuits.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115060364","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

Early gear tooth crack detection based on singular value decomposition 基于奇异值分解的齿轮齿裂早期检测

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819417

Yuejian Chen, M. Zuo

引用次数: 1

Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability 有限可用数据条件下为预测生成实值故障数据

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819392

G. Ranasinghe, A. Parlikad

{"title":"Generating Real-valued Failure Data for Prognostics Under the Conditions of Limited Data Availability","authors":"G. Ranasinghe, A. Parlikad","doi":"10.1109/ICPHM.2019.8819392","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819392","url":null,"abstract":"Data-driven prognostics solutions underperform under the conditions of limited failure data availability since the number of failure data samples is insufficient for training prognostics models effectively. In order to address this problem, we present a novel methodology for generating real-valued failure data which allows training datasets to be augmented so that the number of failure data samples is increased. In contrast to existing data generation techniques which duplicate or randomly generate data, the proposed methodology is capable of generating new and realistic failure data samples. To this end, we utilised the conditional generative adversarial network and auxiliary information pertaining to the failure modes. The proposed methodology is evaluated in a real-world case study involving the prediction of air purge valve failures in heavy trucks. Two prognostics models are developed using gradient boosting machine and random forest classifiers. It is shown that when these models are trained on the augmented training dataset, they outperform the best prognostics solution previously proposed in the literature for the case study by a large margin. More specifically, costs due to breakdowns and false alarms are reduced by 44%.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"89 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":"129273468","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: 9