2017 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献

Performance analysis of a twin shaft Industrial Gas Turbine at fouling conditions 双轴工业燃气轮机污垢工况性能分析

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998334

S. Cruz-Manzo, S. Maleki, Yu Zhang, V. Panov, A. Latimer

引用次数: 2

Machine remaining useful life prediction considering unit-to-unit variability 考虑单元间可变性的机器剩余使用寿命预测

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998313

Naipeng Li, Y. Lei, Ningbo Li, Jing Lin

{"title":"Machine remaining useful life prediction considering unit-to-unit variability","authors":"Naipeng Li, Y. Lei, Ningbo Li, Jing Lin","doi":"10.1109/ICPHM.2017.7998313","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998313","url":null,"abstract":"Remaining useful life (RUL) prediction of machinery plays a significant role for predictive maintenance, thus attracting more and more attentions in recent years. Stochastic process model-based methods are widely used in the RUL prediction of machinery. One of the major issues in the stochastic process model-based methods is that how to deal with the unit-to-unit variability during the RUL prediction process. Traditional methods generally handle this issue by introducing a unit-to-unit variability parameter into the model expression and estimate the parameter using the maximum likelihood estimation (MLE) algorithm. There exist two major limitations in the traditional methods. 1) The degradation processes are assumed to be dependent on only the age, which restricts their implementation in the cases of the state-dependent degradation processes. 2) They do not discuss the influence of the unit-to-unit variability in the RUL prediction processes systematically. To deal with these two limitations, a new RUL prediction method based on age- and state-dependent stochastic process models is proposed in this paper. In the proposed method, a generalized expression of the age- and stage-dependent stochastic process models is generated. An enhanced MLE algorithm is developed to estimate the model parameters according to the measurements of the available training units. And the unit-to-unit variability parameter is updated according to the real-time measurements of the testing unit. The effectiveness of the proposed method is demonstrated using a numerical simulation dataset of fatigue crack-growth.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133399795","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

Air filter diagnostics & prognostics in naturally aspired engines 自然吸气式发动机的空气滤清器诊断和预测

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998306

F. Landolsi, H. Jammoussi, I. Makki

引用次数: 1

Real-time predictive maintenance for wind turbines using Big Data frameworks 利用大数据框架对风力涡轮机进行实时预测性维护

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998308

Mikel Canizo, E. Onieva, Angel Conde, Santiago Charramendieta, Salvador Trujillo

引用次数: 128

An improved fusion prognostics method for remaining useful life prediction of bearings 一种用于轴承剩余使用寿命预测的改进融合预测方法

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998300

Biao Wang, Y. Lei, Naipeng Li, Jing Lin

{"title":"An improved fusion prognostics method for remaining useful life prediction of bearings","authors":"Biao Wang, Y. Lei, Naipeng Li, Jing Lin","doi":"10.1109/ICPHM.2017.7998300","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998300","url":null,"abstract":"The remaining useful life (RUL) prediction of bearings has emerged as a critical technique for providing failure warnings in advance, reducing costly unscheduled maintenance and enhancing the reliability of bearings. Recently, a fusion prognostics method combining exponential model and relevance vector machine (RVM) has been proposed and applied to the RUL prediction of bearings. This fusion prognostics method integrates the advantages of RVM and exponential model and so has better prediction performance than other exponential model-based methods. However, selecting the appropriate value of kernel parameter is very difficult for this fusion prognostics method because of the lack of an explicit prior knowledge. which reduces the prediction accuracy of the fusion prognostics method and affects its generalization performance. To solve this problem, an improved fusion prognostics method is proposed in this paper. In the improved fusion prognostics method, RVM regressions with different kernel parameter values are first applied to obtaining different sparse datasets. Then, using the exponential model of bearing degradation, the different degradation curves are got by fitting the obtained sparse datasets and the Fréchet distance is employed to select the optimum degradation curve from those fitted curves. Finally, the RUL is predicted by extrapolating the selected degradation curve to reach the failure threshold. To verify the superiority of the proposed method compared with the original fusion prognostics method, a real bearing degradation data is used for the RUL prediction. The results show that the improved fusion prognostics method outperforms the original fusion prognostics method in the RUL prediction of bearings.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126677406","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: 14

Enhancement of prognostic models for short-term degradation of gas turbines 燃气轮机短期退化预测模型的改进

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998307

Houman Hanachi, C. Mechefske, Jie Liu, A. Banerjee, Ying Chen

{"title":"Enhancement of prognostic models for short-term degradation of gas turbines","authors":"Houman Hanachi, C. Mechefske, Jie Liu, A. Banerjee, Ying Chen","doi":"10.1109/ICPHM.2017.7998307","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998307","url":null,"abstract":"Deposition and congestion of foulants in the compressor section of gas turbine engines (GTE) degrades the compressor and leads to performance deterioration of the GTE at the system level. Compressor fouling may develop over a short time, but it is recoverable by washing and cleaning. Reliable prediction of the fouling as a function of time is helpful for planning compressor wash services. In this work, the fouling state is parametrized as the relative change of the ratio of the compressor mass flow and efficiency against ideal conditions. A regression-based prognostic model is developed to predict the fouling state as a function of time. In the next step, an adaptive neuro-fuzzy inference system (ANFIS) is developed that considers the rate of humidity condensation at the inlet of the compressor for the prognostic model. The performance of the developed models is evaluated with recorded operating data from a GTE in a power plant. The study shows that enhancement of the prognostic model may be accomplished by taking into account the effects of humidity on the rate of fouling and results in an improvement in the prognostic accuracy.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128246469","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

A comparative study of helicopter planetary bearing diagnosis with vibration and acoustic emission data 基于振动和声发射数据的直升机行星轴承诊断比较研究

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998336

Linghao Zhou, F. Duan, D. Mba, Faris Elasha

引用次数: 7

Real-time monitoring of machines using Open Platform Communication 使用开放式平台通信对机器进行实时监控

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998316

N. Verma, Teena Sharma, Seetaram Maurya, D. Singh, A. Salour

{"title":"Real-time monitoring of machines using Open Platform Communication","authors":"N. Verma, Teena Sharma, Seetaram Maurya, D. Singh, A. Salour","doi":"10.1109/ICPHM.2017.7998316","DOIUrl":"https://doi.org/10.1109/ICPHM.2017.7998316","url":null,"abstract":"Open Platform Communication (OPC) also known as Object Linking and Embedding (OLE) for Process Control (OPC) is the platform for communication in automation and manufacturing fields among the group of networked devices. OPC standard supports the group of specifications known as OPC-Data Access (OPC-DA), OPC-Historical Data Access (OPC-HDA) and OPC-Unified Architecture (OPC-UA). In this paper, the vibration and acoustic data of an air-compressor have been recorded through accelerometer and microphone, respectively. The data is continuously monitored and archived through OPC. The sensing units are placed on predefined sensitive positions of air-compressor to record the data. The MATLAB based platform interacts with OPC server for reading and writing of data acquired from field devices. The recorded data is used for real-time monitoring and fault recognition in air-compressor. The National Instruments (NI) based four channel Data Acquisition (DAQ) module, channel input module: NI9234 and USB chassis: NIcDAQ-9171, is used for connection of sensing units. For experimental analysis, a Matrikon OPC server (2.0.0.5529) is used. MATLAB and OPC server with necessary features have been installed on the operating system to monitor the air-compressor.","PeriodicalId":124147,"journal":{"name":"2017 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115084625","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: 8

Advanced correlation-based anomaly detection method for predictive maintenance 基于相关性的预测性维修高级异常检测方法

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998309

Pushe Zhao, M. Kurihara, J. Tanaka, T. Noda, Shigeyoshi Chikuma, T. Suzuki

引用次数: 39

Lithium-ion battery remaining useful life prediction with Deep Belief Network and Relevance Vector Machine 基于深度信念网络和相关向量机的锂离子电池剩余使用寿命预测

2017 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2017-06-19 DOI: 10.1109/ICPHM.2017.7998298

Guangquan Zhao, Guohui Zhang, Yuefeng Liu, Bin Zhang, Cong Hu

引用次数: 56