2016 IEEE International Conference on Prognostics and Health Management (ICPHM)最新文献

{"title":"Research on online detection and location of multi-conductor cables' faults","authors":"Mao Jianmei, Wang Li, Huang Suyang","doi":"10.1109/ICPHM.2016.7542837","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542837","url":null,"abstract":"Due to the existence of signal junctions in multi-conductor cables, on-line detection using spread spectrum time domain reflectometry(SSTDR) method is prone to a misjudgment, and the amplitude of reflected signal is too small to be detected. In this paper, trappers with proper installation are used to realize directional transmission of detection signal, which can eliminate the effect of signal converging. And a multi-channel time-sharing cycle detection method is proposed to simplify the detector. Simulations and experiments indicate that multi-conductor cables' fault can be detected successfully.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"13 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":"123894246","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}

{"title":"PHM functions maturation","authors":"Audrey Dupont, J. Masse","doi":"10.1109/ICPHM.2016.7542848","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542848","url":null,"abstract":"Snecma has been developing Prognostic and Health Monitoring (PHM) functions to monitor different sub-systems of an aircraft engine. To gain maturity and to take more accurate decisions, algorithms need a reality check on the significance of their results. Algorithms have been deployed on in service fleets of engines, getting access to large amounts of data. Nevertheless, probabilities of failure are very low. Thus there are not enough degradation cases collected to compute with accuracy performance metrics, such as Probability of False Alarm (PFA) and Probability of Detection (PoD), for each algorithm. To address this issue, healthy indicators distributions are used to set detection alarm thresholds. Those thresholds are first checked on healthy data and on rare degradations. Algorithms shall indeed raise no alarm on healthy cases and detect all rare degradations. This allows to alarm the operator only when a health indicator is changing. On the base of following observed failure mechanisms, simulations can help to compute with accuracy performance metrics.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"5 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":"115713447","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}

{"title":"Forecasting the health of gas turbine components through an integrated performance-based approach","authors":"Elias Tsoutsanis, N. Meskin","doi":"10.1109/ICPHM.2016.7542829","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542829","url":null,"abstract":"In this study, we present an integrated method for detecting and forecasting the health of gas turbine components as degraded over time. An advanced model-based real time performance adaptation approach is developed for detecting the degradation of engine components via a dynamic engine model that is built in Simulink. The detected health parameters of the engine component are then implemented in a discrete window-based analysis by a regression method in order to forecast their evolution. The proposed approach is tested for an engine with increased flexibility that characterizes modern gas turbine operations. The results demonstrate the promising capabilities of our advanced proposed method for accurate and efficient detection and forecast of the health of gas turbine compressors as degraded over time.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"4683 4 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":"127576551","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}

{"title":"An improved correlation-based anomaly detection approach for condition monitoring data of industrial equipment","authors":"S. Zhong, Hui Luo, Lin Lin, Xu-yun Fu","doi":"10.1109/ICPHM.2016.7542850","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542850","url":null,"abstract":"An improved latent correlation anomaly detection (LCAD) method is proposed to detect anomalies from condition monitoring datasets of industrial equipment. Above all, original data were segmented to various work cycles. Then, latent correlation vector (LCV) was used to denote the latent correlation among different parameters. Based on a latent correlation probabilistic model (LCPM), an anomaly detection function (ADF) is formulated to determine the state of equipment. In order to compare this method with previously reported anomaly detection methods, simulated datasets were constructed to evaluate the effectiveness of this method. Another experiment was also conducted to test the applicability of this method based on real flight datasets. Both experiments demonstrated superior accuracy and much lower missing alarm rates of this improved LCAD method.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"103 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":"126371119","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}

{"title":"Support vector data description for machinery multi-fault classification with unbalanced datasets","authors":"Li-xiang Duan, Mengyun Xie, Tangbo Bai, Jinjiang Wang","doi":"10.1109/ICPHM.2016.7542846","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542846","url":null,"abstract":"In mechanical fault diagnosis area, fault samples are often difficult to obtain, so the number of fault samples is far less than that of normal samples which leads to the unbalanced dataset issues. A novel model combining SVDD (Support Vector Data Description) and binary tree (BT) based on Mahalanobis distance is put forward to address the multi-classification problems under unbalanced datasets. The idea of the proposed method is to divide the original samples into a series of subsets by adopting binary tree, and then build classifier by describing the boundary of the target via SVDD. The proposed method has emphatically studied on: 1) Separability measure based on Mahalanobis distance. It represents the separability degree which takes the unbalanced degree and distance between each class into account, and takes the advantages of considering the relations among all the features of the datasets by the definition of Mahalanobis distance, it is helpful to determine the structure of the binary tree. 2) Train classifiers by using SVDD. Choose the target class according to the order of binary tree. The proposed method can be applied to multi-classification problems with unbalanced datasets issues. To validate this methodology, samples from unbalanced rotor are employed for experiment. Then, the experimental result compared with other methods is presented showing that the proposed methodology has a better performance and higher classification accuracy on multi-classification problems under unbalanced datasets.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"3 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":"130677443","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}

{"title":"Uncertainty analysis of phased mission systems with probabilistic timed automata","authors":"Zhaoguang Peng, Yu Lu, Alice Miller","doi":"10.1109/ICPHM.2016.7542823","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542823","url":null,"abstract":"A phased mission is one in which the requirements may alter over time. We present a novel approach to analyse phased mission systems using probabilistic timed automata (PTA). We show how to construct PTA models which allow one to reflect system uncertainty, and how to analyse these models using the PRISM probabilistic model checker. We illustrate our approach via a simple case study, namely path planning for a Mars exploration rover, since the mission of the rover can be expected to be an instance of phased mission systems.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"22 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":"123774478","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}

{"title":"Study on multi-event opportunistic maintenance decision-making model based on condition","authors":"Quanlei Wu, C. Lv, Dong Zhou, Yaoyao Wang, Dequan Yu","doi":"10.1109/ICPHM.2016.7542854","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542854","url":null,"abstract":"Because of the maintenance time of different maintenance events have varying degrees flexibility, and different occasion of maintenance will bring different impact on the maintenance and use of the system. Therefore, firstly, this paper analyzes the characteristics of hard time maintenance event and degenerate maintenance event. Secondly, it analyzes the influence of different maintenance events in different maintenance time. Thirdly, it based on opportunistic maintenance decision-making theory, according to the dependence between components in the system, integrating multiple repair events, aiming at minimized maintenance cost, builds multi-events opportunistic maintenance decision model based on health status. Finally, this model is solved by the optimization algorithm to find the optimal maintenance time and the combination of the components.","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":"125770028","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}

{"title":"Micromechanics modeling of skin panel with pitting corrosion for aircraft structural health monitoring","authors":"X. Yu, Fangyi Wan, Yingnan Guo","doi":"10.1109/ICPHM.2016.7542831","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542831","url":null,"abstract":"Due to the increasing requirements from Aircraft Structural Health Monitoring, it is significantly meaningful to research the impact of pitting corrosion on the mechanical property of aircraft principle structural element. In this paper, micromechanics model of pitting corrosion based on Eshelby-Mori-Tanaka approach has been constructed to analyze the effect of pitting corrosion on structural stiffness. The main aims of this study are to determine the effective stiffness of aircraft skin panel structure under various degree and shape parameter of pitting corrosion. A simple accelerating experiment reveals that aircraft structural aluminum alloy materials are vulnerable to pitting corrosion in severe conditions. The Finite Element models are built to investigate the local stress-strain distribution with three different pits (hemi-spherical, cylinder and box). The effective stiffness of the skin panel with 2024 aluminum alloy or 7075 aluminum alloy has been discussed based on micromechanics model. The results show that structural effective stiffness could significantly decrease with gradual increment of degree of pitting. The variations of structural effective stiffness are relatively bigger for deep hemi-ellipsoidal etch pit (λ>1), yet their changes are comparatively smaller for flat hemi-ellipsoidal etch pit (λ<;1). For the local strain around the corrosion pit, the strain around flat hemi-ellipsoidal etch pit (λ<;1) is comparatively bigger than deep hemi-ellipsoidal etch pit (λ>1). Moreover when value of λ is approximately 0.3726, the stiffness within etch pit is close to zero. The works presented in this paper can provide a preliminary corrosion prediction model for structural corrosion monitoring.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"126 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":"121622967","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}

{"title":"Prognostics by interacting multiple model estimator","authors":"Yanjun Yan, M. Mallick, James Z. Zhang, Jie Liu","doi":"10.1109/ICPHM.2016.7542822","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542822","url":null,"abstract":"In prognostics, the modeling of the failure models is complicated even for a single component, such as fatigue crack growth. For a complex system, there are a large number of components, and hence the failing models can be even more complicated due to diverse sub-systems and their components. The remaining useful life (RUL) of the system, as a whole, depends on many factors and there are often sudden changes in its progression pattern. The interacting multiple model (IMM) estimator is a filtering technique that tracks multiple models and reports the probability of each model. The information fusion ability of IMM with a built-in probabilistic metric is highly desirable in failure model tracking and higher level fusion. A general framework is proposed to describe the system health by a health index, then the RUL can be evaluated as the current health value divided by the degradation rate of the health index at that moment. Within the general framework of a health index, an IMM estimator is proposed to identify the failure models and evaluate both the values and the confidence interval of the RUL. Simulations on various health degradation models are carried out to illustrate the effectiveness of the IMM based RUL estimation. Specifically, the RUL sub-models can be with nearly constant degradation rate, with accelerated growing degradation rate, or some drastic break-down due to environmental changes such as a hard failure. In simulations, the truth is known, and hence the performance of the RUL estimator can be precisely assessed. This paper has not only proposed a fusion scheme to handle various failure models, but also presented the data generation procedure of health index in various situations. Such data sets can be used as benchmarks to compare various prognostics techniques.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"4 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":"122297307","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}

{"title":"ANN based RUL assessment for copper-aluminum wirebonds subjected to harsh environments","authors":"P. Lall, Shantanu Deshpande, L. Nguyen","doi":"10.1109/ICPHM.2016.7542851","DOIUrl":"https://doi.org/10.1109/ICPHM.2016.7542851","url":null,"abstract":"Copper (Cu) wire bonding is new alternative to traditional Gold (Au) wirebonds. Since Cu is not as inert as Au, material selection in the package plays key role in reliability of packages. Researchers have reported individual effect of the variables such as pH value, ionic contamination, and filler content of EMC etc. on reliability of Cu wirebonds. However, since all these parameters have combined effect on reliability, understanding of joint effect of all parameters on reliability of Cu wirebond is necessary for smooth transition to Cu wirebond system. In this paper, predictive model for life prediction of copper wirebond system based on neural network is presented. A set of parts, molded with eight different EMC's were subjected to high temperature environment (temperature range of 150°C-225°C). Resistance, IMC change and shear strength change were monitored during this study. Resistance spectroscopy was used for accurate resistance measurement. Dage 2400PC was used to calculate change in shear strength. Parts were cross-sectioned and polished along Cu-Al interface using SEM and EDX system after the failure. Relation between resistance changes with change in shear strength was established. 20% change in resistance was considered as failure threshold. All parts were tested till failure. Evolution of resistance was considered as leading indicator of failure. Variable selection for the model was done using principle component analysis. Scree plot was used to identify and retain influential variables in the model and to ignore non-significant variables. The shortlisted variables along with resistance evolution and time-to-failure data were used to build predictive model. Neural network regression model was trained with input feature vectors. Supervised learning was used during training. Feedforward multilayer network was trained using Bayesian regularization in conjuncture with Levenberg Marquardt algorithm. Self-validation and cross validations were performed multiple times to avoid overfitting of the data. Prediction model will be able to predict remaining useful life when environmental conditions, properties of EMC and current state of leading indicator are known. This model will provide, educated estimation of remaining useful life (RUL) for Cu wirebonded molded packages, at desired operating condition.","PeriodicalId":140911,"journal":{"name":"2016 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"38 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":"134070446","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}