2019 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第4页

Data-Driven Model Selection Study for Long-Term Performance Deterioration of Gas Turbines 燃气轮机长期性能退化的数据驱动模型选择研究

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819433

Yuan Liu, A. Banerjee, Houman Hanachi, Amar Kumar

引用次数: 1

Fault Detection and Estimation for a Class of Nonlinear Distributed Parameter Systems 一类非线性分布参数系统的故障检测与估计

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819432

H. Ferdowsi, Jia Cai, S. Jagannathan

引用次数: 1

Implementing Predictive Maintenance in a Company: Industry Insights with Expert Interviews 在公司中实施预测性维护:专家访谈的行业见解

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819406

Carolin Wagner, B. Hellingrath

{"title":"Implementing Predictive Maintenance in a Company: Industry Insights with Expert Interviews","authors":"Carolin Wagner, B. Hellingrath","doi":"10.1109/ICPHM.2019.8819406","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819406","url":null,"abstract":"The implementation of predictive maintenance as a proactive maintenance approach is gaining increasing importance in the age of digitization and the fourth industrial revolution. Various studies in the German industry have shown that the majority of companies already follow up on the topic. However, successful implementations of predictive maintenance in businesses are still a rarity. Due to the lack of knowledge and guidance during the implementation process, companies experience many difficulties for the realization of this proactive maintenance approach. Even though much research has been conducted in the fields of predictive maintenance and prognostics and health management, little attention was devoted to the design and analysis of process models for industrial applications. Common process models are theoretically derived without capturing the complexity of reality. This paper communicates the results of interviews conducted with six industry experts. In particular, experts from management consultancies are addressed with experience in multiple successful implementations. Based on the collected data, industry insights in terms of steps and phases of process models, best practices and challenges are provided.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"156 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":"115586844","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

Diagnosing Strong-fault Models with a Two-step A* Search Method 基于两步a *搜索法的强故障模型诊断

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819391

Qi Zhao, Wenfeng Zhang, Yuhao Deng, Hongbo Zhao, W. Feng

{"title":"Diagnosing Strong-fault Models with a Two-step A* Search Method","authors":"Qi Zhao, Wenfeng Zhang, Yuhao Deng, Hongbo Zhao, W. Feng","doi":"10.1109/ICPHM.2019.8819391","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819391","url":null,"abstract":"Many model-based diagnosis researches, such as conflict directed A* search, have been made on weak-fault models, which have no fault mode behavior. However, in the real world, behaviors of common fault modes are usually known. In this situation, strong-fault models are built. Compared with weak-fault models, it is difficult to diagnose strong-fault models because their mode space is greater and non-monotonic. To diagnose strong-fault models efficiently, this paper proposes a two-step A* search, based on the conflict directed A* search. In our method, the consistency over modes, observations and models, is tested by assumption-based truth maintenance system, which generates multiple conflict sets if a fault occurs. Then fault isolation and identification are accomplished separately: firstly, possibly faulty components are isolated based on the conflict sets from the truth maintenance system; then different mode combinations of the faulty components are tested to obtain the specific fault modes. A* search is employed in both steps, as indicated by the name. By separating isolation and identification in two stages, memory and time requirements are reduced significantly. In the case study, a heat control system is utilized to demonstrate the proposed approach.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"10 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":"124253679","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

A Data-Based Approach for Sensor Fault Detection and Diagnosis of Electro-Pneumatic Brake 基于数据的电气制动器传感器故障检测与诊断方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819443

Yunyou Lu, Xiaoping Fan, Dianzhu Gao, Yijun Cheng, Yingze Yang, Xiaoyong Zhang, S. Li, Jun Peng

{"title":"A Data-Based Approach for Sensor Fault Detection and Diagnosis of Electro-Pneumatic Brake","authors":"Yunyou Lu, Xiaoping Fan, Dianzhu Gao, Yijun Cheng, Yingze Yang, Xiaoyong Zhang, S. Li, Jun Peng","doi":"10.1109/ICPHM.2019.8819443","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819443","url":null,"abstract":"Sensor faults in the train electro-pneumatic brake system can cause severe performance degradation which may lead to accidents. Therefore, sensor fault detection and diagnosis of the brake are crucial for the train safety. However, the brake system is a complex integrated system with multiple operating modes, which makes the sensor fault detection and diagnosis more difficult. In this paper, we propose a data-based analytical redundancy method to detect and isolate sensor faults of the electro-pneumatic brake in the dynamic process of different operating modes. Firstly, we apply the Gradient Boosting Decision Trees regression analysis model to determine the normal quantitative correlation between two sensor measurements by training non-fault sensor measurements. Secondly, a measurement estimation is calculated by fusing multi-sensor measurements with a data fusion method that is insensitive to faulty sensor measurements. Finally, the sensor fault is detected by comparing the fusion estimation and each sensor measurement. The feasibility and effectiveness of the data-based approach are verified by experiments and simulations.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"3 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":"127464805","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: 2

Health Assessment for Crane Pumps based on Vehicle Tests using Deep Autoencoder and Metric Learning 基于深度自编码器和度量学习的车辆试验起重机泵健康评估

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819387

Dengyu Xiao, Yixiang Huang, Chengjin Qin, Haotian Shi, Chengliang Liu, Zenghai Shan

{"title":"Health Assessment for Crane Pumps based on Vehicle Tests using Deep Autoencoder and Metric Learning","authors":"Dengyu Xiao, Yixiang Huang, Chengjin Qin, Haotian Shi, Chengliang Liu, Zenghai Shan","doi":"10.1109/ICPHM.2019.8819387","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819387","url":null,"abstract":"Pump is one of the key components in a crane, which once fails will severely hurt the reliability of the hydraulic system and cause great loss. Therefore, accurate, reliable and effective crane pump health assessment must be performed. However, the research about pump health assessment still stays at the stage of bench tests, which have the limited help for the real-world pump health prognosis. In this paper, to evaluate crane pump health status and avoid the issue above, the real-world vehicle tests of several cranes with different service years are performed to acquire the pump signals during the cranes’ actual operations. Deep Autoencoder (DAE), a kind of unsupervised learning approach, which possesses the capacity to learn meaningful representations from raw signal, is used reduce the data dimension before they are sent to Mahalanobis-Taguchi System in metric learning. Mahalanobis distance (MD) is utilized to reveal the performance degradation and assess the health condition. Performances of other feature learning methods such as statistical features, EMD, MLP, CNN are tested and contrasted. Results show that the proposed approach achieves the best performance.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"235 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":"116080023","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

Transferring Random Samples in Actuator Systems for Binary Damage Detection 基于二元损伤检测的致动器系统随机样本传输

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819393

Tyler Cody, Stephen C. Adams, P. Beling, Sherwood Polter, K. Farinholt, Nathan Hipwell, Ali Chaudhry, Kennet Castillo, Ryan Meekins

引用次数: 5

A parameter adaptive data-driven approach for remaining useful life prediction of solenoid valves 电磁阀剩余使用寿命预测的参数自适应数据驱动方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819382

Xuanheng Tang, Jun Peng, B. Chen, Fu Jiang, Yingze Yang, Rui Zhang, Dianzhu Gao, Xiaoyong Zhang, Zhiwu Huang

{"title":"A parameter adaptive data-driven approach for remaining useful life prediction of solenoid valves","authors":"Xuanheng Tang, Jun Peng, B. Chen, Fu Jiang, Yingze Yang, Rui Zhang, Dianzhu Gao, Xiaoyong Zhang, Zhiwu Huang","doi":"10.1109/ICPHM.2019.8819382","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819382","url":null,"abstract":"As crucial parts of various engineering systems, solenoid valves (SVs) are of great importance and their failure may cause unexpected casualties. Accurately predicting the remaining useful life (RUL) of SVs helps making maintenance decision before they break down. It is hard to establish accurate physical model of SVs as they are characterized by complicated structure, multi-physics coupled working mechanism and complex degradation mechanisms. Different individuals may experience distincted degradation processes in various working environment. In this paper, a data-driven prognostic method is proposed for SVs. Firstly, a health index based on the dynamic driven current of SVs is constructed and an exponential model is established to characterize the degradation path. Then, the particle filter (PF) is introduced to reduce the noise of online measurement. Based on the denoised measurement, the parameters of the exponential model are adaptively updated with Bayesian estimation dynamically. Finally, the effectiveness and practicability of proposed method is validated by the designed experiments on SVs.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"45 3 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":"121033788","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

Wheel Polygonalization Identification Method Based on Fluctuation of Temperature Data and Wheel Set Dynamic Monitoring Data 基于温度数据波动和轮对动态监测数据的车轮多面化识别方法

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819425

Zejun Zheng, D. Song, X. Qi, Zilin Geng, Weihua Zhang

引用次数: 0

Intelligent Fault Diagnosis of Rolling Element Bearing Based on Convolutional Neural Network and Frequency Spectrograms 基于卷积神经网络和频谱图的滚动轴承智能故障诊断

2019 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2019-06-01 DOI: 10.1109/ICPHM.2019.8819444

Pengfei Liang, C. Deng, Jun Wu, Zhixin Yang, J. Zhu

{"title":"Intelligent Fault Diagnosis of Rolling Element Bearing Based on Convolutional Neural Network and Frequency Spectrograms","authors":"Pengfei Liang, C. Deng, Jun Wu, Zhixin Yang, J. Zhu","doi":"10.1109/ICPHM.2019.8819444","DOIUrl":"https://doi.org/10.1109/ICPHM.2019.8819444","url":null,"abstract":"Effective fault diagnosis of rolling element bearing is vital for the reliability and safety of modern industry. Although traditional intelligent fault diagnosis technology such as support vector machine, extreme learning machines and artificial neural network might achieve satisfactory accuracy, expert knowledge and manual intervention are heavily relied on in the process of feature extraction and selection. In this paper, a novel fault diagnosis method based on deep learning is proposed for rolling bearing using convolutional neural networks (CNN) and frequency spectrograms. First of all, fast Fourier transform is used to extract frequency features from raw 1-D vibration signals and convert them into 2-D frequency spectrograms. Then, the extracted 2-D frequency spectrograms are inputted into the CNN model to achieve fault diagnosis of rolling bearing, which makes full use of the strong ability of CNN in image classification. Finally, a case study is carried out to demonstrate the proposed method. The results show that it can achieve higher accuracy than traditional methods. Moreover, its performance in stability is very good as well.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"30 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":"133547070","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: 11