2016 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献_第3页

A novel method for capacity fade analysis of lithium-ion batteries based on multi-physics model 基于多物理场模型的锂离子电池容量衰减分析新方法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542834

Junfu Li, Chao Lyu, Liqiang Zhang

引用次数: 5

Bearing defect signature analysis based on a SAX-based association rule mining 基于sax关联规则挖掘的轴承缺陷特征分析

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542825

Tangbo Bai, Yulong Zhang, Xuduo Wang, Li-xiang Duan, Jinjiang Wang

{"title":"Bearing defect signature analysis based on a SAX-based association rule mining","authors":"Tangbo Bai, Yulong Zhang, Xuduo Wang, Li-xiang Duan, Jinjiang Wang","doi":"10.1109/ICPHM.2016.7542825","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542825","url":null,"abstract":"Association rule mining provides the feasibility by taking an inverse approach for bearing defect signature analysis to directly mine associations between labeled defects and defect features instead of traditional forward fault diagnosis steps. Different from the common uniform partitioning approach used in association rule mining, a novel association rule mining approach has been proposed, based on a discretization method Symbolic Aggregate approXimation (SAX). In the presented method, the extracted features from sensing measurements are discretized and transformed into symbolic sequences according to the equalized distribution of the data. Next, the association relation between discretized features and labeled defect modes (or defect severities) is dug to generate the rules. The presented method partitions data equiprobably and avoids centralization or dispersion of the data, thus achieving more effective association rules for analyzing bearing defects. Experimental studies on bearing test data reveal that the proposed method is capable of generating a number of meaningful association rules in bearing defects and outperforms the discretization methods based on equal density and equal width technique.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"833 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123925526","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

An application of sensor-based anomaly detection in the maritime industry 基于传感器的异常检测在航运业中的应用

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7811910

A. Brandsæter, Gabriele Manno, E. Vanem, I. Glad

{"title":"An application of sensor-based anomaly detection in the maritime industry","authors":"A. Brandsæter, Gabriele Manno, E. Vanem, I. Glad","doi":"10.1109/ICPHM.2016.7811910","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7811910","url":null,"abstract":"In this paper we present an application of sensor-based anomaly detection in maritime transport. The study is based on real sensor data streamed from a ship to shore, where the data is analysed through a big data analytics platform. The novelty of this work originates in the use of data from sensors covering different aspects of the ship operation, exemplified here by propulsion power, speed over ground and ship motion in four different degrees of freedom. The developed method employs Auto Associative Kernel Regression (AAKR) for signal reconstruction, and the Sequential Probability Ratio Test (SPRT) technique for anomaly detection, where different hypothesis tests looking both at mean and variance deviations have been tested. In order to compare different settings, formal state of the art performance metrics have been used. We demonstrate that the AAKR model produces good reconstructions when the observations are similar to observations represented in the training data, and for some examples of simulated anomalies, the method reveals the abnormal behaviour. As long as the parameters are tuned carefully, alarms are triggered appropriately by the SPRT.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"51 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839756","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: 15

A hydrogenerator model-based failure detection framework to support asset management 基于水轮发电机模型的故障检测框架，支持资产管理

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542867

O. Blancke, Antoine Tahan, D. Komljenovic, N. Amyot, C. Hudon, M. Lévesque

{"title":"A hydrogenerator model-based failure detection framework to support asset management","authors":"O. Blancke, Antoine Tahan, D. Komljenovic, N. Amyot, C. Hudon, M. Lévesque","doi":"10.1109/ICPHM.2016.7542867","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542867","url":null,"abstract":"Electrical utilities in North America significantly increased their installed capacities between 1960 and 1990. This ageing fleet is now forcing the producers to begin to use a holistic asset management in a more systematic way by introducing diagnostic and prognostic tools to support them in their decision-making process. For the last few decades, the Hydro-Quebec Research Institute has been working to understand ageing mechanisms and developing a diagnostic and prognostic causal graph model for hydrogenerators based on expert knowledge and diagnostic data. This paper proposes asset and asset system metrics based on graph theory to estimate the probability of detecting a failure using the number of detectable early warning signs. Proposed indicators intend to inform operators and decision makers on the failure detection probability for each individual asset and to identify critical failure detection of assets at an asset system level. An analysis has been carried out on a real hydropower plant for each of its sixteen hydrogenerators. Some results will be presented and critical failure detection rates for hydrogenerators will be identified. A framework will be proposed to improve asset management.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129112209","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

Generating feature sets for fault diagnosis using denoising stacked auto-encoder 利用去噪堆叠自编码器生成故障诊断特征集

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542865

Raghuveer Thirukovalluru, Sonal Dixit, R. K. Sevakula, N. Verma, A. Salour

{"title":"Generating feature sets for fault diagnosis using denoising stacked auto-encoder","authors":"Raghuveer Thirukovalluru, Sonal Dixit, R. K. Sevakula, N. Verma, A. Salour","doi":"10.1109/ICPHM.2016.7542865","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542865","url":null,"abstract":"Recent advancements in sensor technologies and data driven model based techniques have made intelligent diagnostic systems prominent in machine maintenance frameworks of industries. The performance of such systems immensely relies upon the quality of features extracted and the classifier model learned. Traditionally features were handcrafted, where engineers would manually design them with statistical parameters and signal transforms based energy distribution analysis. Recently, deep learning techniques have shown new ways of obtaining useful feature representation that provide state of the art results in image and speech processing applications. This paper first presents a brief survey of traditional handcrafted features and later presents a short analysis of handcrafted features v/s features learned by deep neural networks (DNN), for doing fault diagnosis. The DNN based features in this paper were generated in 3 phases: 1) extracted handcrafted features using traditional techniques 2) initialized the weights of DNN by learning de-noising sparse auto-encoders with the handcrafted features in unsupervised fashion and 3) applied two generic fine tuning heuristics that tailor DNN's weights to give good classification performance. The experimentation and analysis were performed on 5 datasets: one each on Air compressor monitoring, Drill bit monitoring and Steel plate monitoring, and two on bearing fault monitoring data. The results clearly show the prospects of DNN obtaining good feature representations and good classification performance. Further, it also finds that Fast Fourier Transform based features with DNN are more suited for Support Vector Machine as classifier than Random Forest.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131063423","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: 84

Integrated model-based control and health management for industrial gas turbines 基于模型的工业燃气轮机综合控制与健康管理

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542820

V. Panov

引用次数: 4

Surface acoustic wave vibration sensors for measuring aircraft flutter 测量飞机颤振的表面声波振动传感器

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542855

W. Wilson, J. P. Moore, P. Juarez

引用次数: 6

A data-driven prognostics approach for RUL based on principle component and instance learning 基于主成分和实例学习的规则学习数据驱动预测方法

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542815

Yongxiang Li, Jianming Shi, Wang Gong, Xiaodong Liu

引用次数: 14

Diagnostic reasoning framework combining fuzzy logic and dempster-shafer theory 结合模糊逻辑和dempster-shafer理论的诊断推理框架

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542863

Anna Sztyber, J. M. Kóscielny

引用次数: 3

Identification of a roller screw for diagnosis of flight control actuator 用于飞控执行机构诊断的滚柱螺杆辨识

2016 IEEE International Conference on Prognostics and Health Management (ICPHM) Pub Date : 2016-06-20 DOI: 10.1109/ICPHM.2016.7542839

Romain Breuneval, G. Clerc, B. Nahid-Mobarakeh, B. Mansouri, Alexandre Guyamier

{"title":"Identification of a roller screw for diagnosis of flight control actuator","authors":"Romain Breuneval, G. Clerc, B. Nahid-Mobarakeh, B. Mansouri, Alexandre Guyamier","doi":"10.1109/ICPHM.2016.7542839","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542839","url":null,"abstract":"The condition based maintenance is an increasing challenge for flight control systems. In this paper, a methodology for the diagnosis of a roller screw in an electromechanical actuator is proposed. As this component is critical, its diagnosis is essential to use it on aircrafts. The methodology is based on the extraction of features by identifying a model of the actuator. First, a specific waveform, made of increasing steps of speed, is run on the actuator. Then, the measurements are processed to reduce the noise and the bias of the different sensors. In order to accelerate the identification, an equivalent point is calculated for each step of the waveform. Then, the identification is realized and the identified parameters are gathered in a feature vector. Finally, a model including backlash and deformation of the stem is used to validate the approach and to generate a set of data. The aging is simulated by making assumptions on the evolution of parameters. Classification is made by using k-Nearest Neighbors (kNN). Performances of the algorithm on this application are evaluated in terms of precision and robustness.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"35 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115707958","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