Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036361
A. Mosallam, K. Medjaher, N. Zerhouni
In this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL. The proposed method extracts monotonic trends from offline sensor signals, which are used to build reference models. From online signals the method represents the uncertainty about the current status, using discrete Bayesian filter. Finally, the method predicts RUL of the monitored component using integrated method based on K-nearest neighbor (k-NN) and Gaussian process regression (GPR). The performance of the algorithm is demonstrated using two real data sets from NASA Ames prognostics data repository. The results show that the algorithm obtain good results for both application.
{"title":"Integrated Bayesian framework for remaining useful life prediction","authors":"A. Mosallam, K. Medjaher, N. Zerhouni","doi":"10.1109/ICPHM.2014.7036361","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036361","url":null,"abstract":"In this paper, a data-driven method for remaining useful life (RUL) prediction is presented. The method learns the relation between acquired sensor data and end of life time (EOL) to predict the RUL. The proposed method extracts monotonic trends from offline sensor signals, which are used to build reference models. From online signals the method represents the uncertainty about the current status, using discrete Bayesian filter. Finally, the method predicts RUL of the monitored component using integrated method based on K-nearest neighbor (k-NN) and Gaussian process regression (GPR). The performance of the algorithm is demonstrated using two real data sets from NASA Ames prognostics data repository. The results show that the algorithm obtain good results for both application.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126740366","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036382
N. Arunachalam, L. Vijayaraghavan
Assessing the grinding wheel surface condition during dressing is very important in order to decide about the number of dressing passes required to retain the cutting ability of the grinding wheel and also to reduce the wastage of the grinding wheel material. The dressing process removes the loaded particles and brings out the new grains in order to retain the cutting ability of the grinding wheel. The selection of correct dressing parameters and the condition of the dresser are very important to carryout proper dressing. In this work, an attempt has been made to arrive out the number of dressing passes required to dress the grinding wheel based on the texture features of the images of the grinding wheel. The single point diamond dressing was carried out with selected dressing variables. After each pass the images of the grinding wheel was captured in the same location by properly positioning the grinding wheel. Then the images were analyzed and the evaluated texture parameters were used to indicate the condition of the grinding wheel.
{"title":"Assessment of grinding wheel conditioning process using machine vision","authors":"N. Arunachalam, L. Vijayaraghavan","doi":"10.1109/ICPHM.2014.7036382","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036382","url":null,"abstract":"Assessing the grinding wheel surface condition during dressing is very important in order to decide about the number of dressing passes required to retain the cutting ability of the grinding wheel and also to reduce the wastage of the grinding wheel material. The dressing process removes the loaded particles and brings out the new grains in order to retain the cutting ability of the grinding wheel. The selection of correct dressing parameters and the condition of the dresser are very important to carryout proper dressing. In this work, an attempt has been made to arrive out the number of dressing passes required to dress the grinding wheel based on the texture features of the images of the grinding wheel. The single point diamond dressing was carried out with selected dressing variables. After each pass the images of the grinding wheel was captured in the same location by properly positioning the grinding wheel. Then the images were analyzed and the evaluated texture parameters were used to indicate the condition of the grinding wheel.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115448703","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036405
A. Hochstein, Hyung-Il Ahn, Y. Leung, M. Denesuk
Regime switching vector autoregressive (RSVAR) models are typically used to model changing dependency structures of multivariate time series. These changing regimes are represented by using a first-order Markov process where the transition distribution reflects the probabilities of moving to one of the other regime in the subsequent time step. Instead of representing the state of the system at different points in time, we extend this framework by using an explicit time representation that allows us to query against probability distributions of when particular regime changes take place. In contrast to continuous time based approaches such as continuous time Bayesian networks or continuous time Markov processes, we do not rely on intensity matrices that describe trajectories of consecutive states. Here we define regime changes as events and understand time as context of an event. This allows us to integrate dependencies at different time granularities while being able to perform inference in a decomposed way. As a consequence, we can efficiently consider higher-order effects stretching across a large number of consecutive regimes. The underlying assumption is that timely evolution of variables between regime switches is completely captured by the VAR model or possibly a set of VAR models with varying measuring rates and that there is a representative set of multiple time series exhibiting similar higher-order regime dynamics. In this paper we show how such dynamics can be learned integrative with learning RSVAR model parameters and how the regime dynamics can be considered in the RSVAR inference procedures. We demonstrate the benefits of our approach based on a simple scenario. Further, an application to a typical prognostics scenario is presented, leading to the highest score in the IEEE PHM 2014 Data Challenge for the industrial track.
{"title":"Switching vector autoregressive models with higher-order regime dynamics Application to prognostics and health management","authors":"A. Hochstein, Hyung-Il Ahn, Y. Leung, M. Denesuk","doi":"10.1109/ICPHM.2014.7036405","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036405","url":null,"abstract":"Regime switching vector autoregressive (RSVAR) models are typically used to model changing dependency structures of multivariate time series. These changing regimes are represented by using a first-order Markov process where the transition distribution reflects the probabilities of moving to one of the other regime in the subsequent time step. Instead of representing the state of the system at different points in time, we extend this framework by using an explicit time representation that allows us to query against probability distributions of when particular regime changes take place. In contrast to continuous time based approaches such as continuous time Bayesian networks or continuous time Markov processes, we do not rely on intensity matrices that describe trajectories of consecutive states. Here we define regime changes as events and understand time as context of an event. This allows us to integrate dependencies at different time granularities while being able to perform inference in a decomposed way. As a consequence, we can efficiently consider higher-order effects stretching across a large number of consecutive regimes. The underlying assumption is that timely evolution of variables between regime switches is completely captured by the VAR model or possibly a set of VAR models with varying measuring rates and that there is a representative set of multiple time series exhibiting similar higher-order regime dynamics. In this paper we show how such dynamics can be learned integrative with learning RSVAR model parameters and how the regime dynamics can be considered in the RSVAR inference procedures. We demonstrate the benefits of our approach based on a simple scenario. Further, an application to a typical prognostics scenario is presented, leading to the highest score in the IEEE PHM 2014 Data Challenge for the industrial track.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115544967","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036365
Kesheng Wang, D. Luo, Yaohui Wang, Q. He
Order tracking analysis is normally treated as a method to deal with rotating machine vibration signals. The effectiveness of the technique for rotating machine fault diagnostics has been widely recognized. However, the use of the order tracking to non-rotating mechanism has not yet been exploited. The applications of order tracking logic to non-rotating mechanisms may greatly enhance the diagnostic abilities. In this paper, the fundamental rationale of order tracking analysis is commented and its suitability of the order tracking rationale to non-rotating mechanism is explored in an experimental cantilever beam setup. The comparisons between proposed signal processing method to traditional time and frequency methods are discussed. An improved prospective to understand non-rotating mechanism is proposed with the help of order tracking basics.
{"title":"Application of order tracking rationale to non-rotating cantilever beam analysis","authors":"Kesheng Wang, D. Luo, Yaohui Wang, Q. He","doi":"10.1109/ICPHM.2014.7036365","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036365","url":null,"abstract":"Order tracking analysis is normally treated as a method to deal with rotating machine vibration signals. The effectiveness of the technique for rotating machine fault diagnostics has been widely recognized. However, the use of the order tracking to non-rotating mechanism has not yet been exploited. The applications of order tracking logic to non-rotating mechanisms may greatly enhance the diagnostic abilities. In this paper, the fundamental rationale of order tracking analysis is commented and its suitability of the order tracking rationale to non-rotating mechanism is explored in an experimental cantilever beam setup. The comparisons between proposed signal processing method to traditional time and frequency methods are discussed. An improved prospective to understand non-rotating mechanism is proposed with the help of order tracking basics.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129526675","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036400
P. Lall, Shantanu Deshpande, L. Nguyen, M. Murtuza
Gold wire bonding has been widely used as first-level interconnect in semiconductor packaging. The increase in the gold price has motivated the industry search for alternative to the gold wire used in wire bonding and the transition to copper wire bonding technology. Potential advantages of transition to Cu-Al wire bond system includes low cost of copper wire, lower thermal resistivity, lower electrical resistivity, higher deformation strength, damage during ultrasonic squeeze, and stability compared to gold wire. However, the transition to the copper wire brings along some trade-offs including poor corrosion resistance, narrow process window, higher hardness, and potential for cratering. Formation of excessive Cu-Al intermetallics may increase electrical resistance and reduce the mechanical bonding strength. Current state-of-art for studying the Cu-Al system focuses on accumulation of statistically significant number of failures under accelerated testing. In this paper, a new approach has been developed to identify the occurrence of impending apparently-random defect fall-outs and pre-mature failures observed in the Cu-Al wirebond system. The use of intermetallic thickness, composition and corrosion as a leading indicator of failure for assessment of remaining useful life for Cu-al wirebond interconnects has been studied under exposure to high temperature and temperature-humidity. Damage in wire bonds has been studied using x-ray Micro-CT. Microstructure evolution was studied under isothermal aging conditions of 150°C, 175°C, and 200°C till failure. Activation energy was calculated using growth rate of intermetallic at different temperatures. Effect of temperature and humidity on Cu-Al wirebond system was studied using Parr Bomb technique at different elevated temperature and humidity conditions (110°C/100%RH, 120°C/100%RH, 130°C/100%RH) and failure mechanism was developed. The present methodology uses evolution of the IMC thickness, composition in conjunction with the Levenberg-Marquardt algorithm to identify accrued damage in wire bond subjected to thermal aging. The proposed method can be used for quick assessment of Cu-Al parts to ensure manufactured part consistency through sampling.
{"title":"Prognostic indicators for Cu-Al wirebond degradation under operation at elevated temperature and combined temperature humidity","authors":"P. Lall, Shantanu Deshpande, L. Nguyen, M. Murtuza","doi":"10.1109/ICPHM.2014.7036400","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036400","url":null,"abstract":"Gold wire bonding has been widely used as first-level interconnect in semiconductor packaging. The increase in the gold price has motivated the industry search for alternative to the gold wire used in wire bonding and the transition to copper wire bonding technology. Potential advantages of transition to Cu-Al wire bond system includes low cost of copper wire, lower thermal resistivity, lower electrical resistivity, higher deformation strength, damage during ultrasonic squeeze, and stability compared to gold wire. However, the transition to the copper wire brings along some trade-offs including poor corrosion resistance, narrow process window, higher hardness, and potential for cratering. Formation of excessive Cu-Al intermetallics may increase electrical resistance and reduce the mechanical bonding strength. Current state-of-art for studying the Cu-Al system focuses on accumulation of statistically significant number of failures under accelerated testing. In this paper, a new approach has been developed to identify the occurrence of impending apparently-random defect fall-outs and pre-mature failures observed in the Cu-Al wirebond system. The use of intermetallic thickness, composition and corrosion as a leading indicator of failure for assessment of remaining useful life for Cu-al wirebond interconnects has been studied under exposure to high temperature and temperature-humidity. Damage in wire bonds has been studied using x-ray Micro-CT. Microstructure evolution was studied under isothermal aging conditions of 150°C, 175°C, and 200°C till failure. Activation energy was calculated using growth rate of intermetallic at different temperatures. Effect of temperature and humidity on Cu-Al wirebond system was studied using Parr Bomb technique at different elevated temperature and humidity conditions (110°C/100%RH, 120°C/100%RH, 130°C/100%RH) and failure mechanism was developed. The present methodology uses evolution of the IMC thickness, composition in conjunction with the Levenberg-Marquardt algorithm to identify accrued damage in wire bond subjected to thermal aging. The proposed method can be used for quick assessment of Cu-Al parts to ensure manufactured part consistency through sampling.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132259031","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036378
Yu Qi, Wang Fangyi, Guo Yingnan, W. Cui, Cui Weimin
Fault injection technologies, which are important component of prognostic and health management (PHM) system and being paid more and more attention, have been applied to estimate the health status of aerocraft, with aiming to enhance the safety and reduce the maintenance costs. This paper presents a method that can inject faults such as joint failures and skin damages into a finite element model of wing structure for overcoming the difficulty in simulating an actual structural fault. The fault modes of wing structure are analyzed and its influence can be reflected in the finite element (FE) model by controlling the command stream. The parameters such as linear displacement, angular displacement, constraint force and maximal node stress can be used to identify the corresponding fault mode. A Fault injection system, the integration of the fault injection controller compiled by Visual C++ with the finite element analysis (FEA) software ANSYS, is established. User can set parameters including fault mode, location and extent on the interactive interface of the software. With the command stream generated by fault injection controller, the FE model in normal or abnormal condition can be set up for simulating. The results for representative cases show that the outputs from the FEA software coincide with those analyzed through the theory of structural mechanics.
{"title":"A model-based fault injection system for aerocraft wing structure","authors":"Yu Qi, Wang Fangyi, Guo Yingnan, W. Cui, Cui Weimin","doi":"10.1109/ICPHM.2014.7036378","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036378","url":null,"abstract":"Fault injection technologies, which are important component of prognostic and health management (PHM) system and being paid more and more attention, have been applied to estimate the health status of aerocraft, with aiming to enhance the safety and reduce the maintenance costs. This paper presents a method that can inject faults such as joint failures and skin damages into a finite element model of wing structure for overcoming the difficulty in simulating an actual structural fault. The fault modes of wing structure are analyzed and its influence can be reflected in the finite element (FE) model by controlling the command stream. The parameters such as linear displacement, angular displacement, constraint force and maximal node stress can be used to identify the corresponding fault mode. A Fault injection system, the integration of the fault injection controller compiled by Visual C++ with the finite element analysis (FEA) software ANSYS, is established. User can set parameters including fault mode, location and extent on the interactive interface of the software. With the command stream generated by fault injection controller, the FE model in normal or abnormal condition can be set up for simulating. The results for representative cases show that the outputs from the FEA software coincide with those analyzed through the theory of structural mechanics.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114703835","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036406
J. Kimotho, T. Meyer, W. Sextro
Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.
{"title":"PEM fuel cell prognostics using particle filter with model parameter adaptation","authors":"J. Kimotho, T. Meyer, W. Sextro","doi":"10.1109/ICPHM.2014.7036406","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036406","url":null,"abstract":"Application of prognostics and health management (PHM) in the field of Proton Exchange Membrane (PEM) fuel cells is emerging as an important tool in increasing the reliability and availability of these systems. Though a lot of work is currently being conducted to develop PHM systems for fuel cells, various challenges have been encountered including the self-healing effect after characterization as well as accelerated degradation due to dynamic loading, all which make RUL predictions a difficult task. In this study, a prognostic approach based on adaptive particle filter algorithm is proposed. The novelty of the proposed method lies in the introduction of a self-healing factor after each characterization and the adaption of the degradation model parameters to fit to the changing degradation trend. An ensemble of five different state models based on weighted mean is then developed. The results show that the method is effective in estimating the remaining useful life of PEM fuel cells, with majority of the predictions falling within 5% error. The method was employed in the IEEE 2014 PHM Data Challenge and led to our team emerging the winner of the RUL category of the challenge.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123981896","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036392
T. R. Walsh, S. Alhloul, M. Hajimorad
This paper addresses the use of traditional PHM techniques for estimating the remaining useful life (RUL) of a transmission line in a typical power grid. The sensor of interest is the Phasor Measurement Unit (PMU) which provides sampled data in the form of a synchrophasor. A Synchrophasor is a GPS based time stamped measurement of voltage and current at different physical locations on the grid. It is the fact that the measurements are time stamped to a common clock that allows one to obtain the absolute phase difference between measurements at different points in the grid. It is this phase difference that determines the health of the grid. Too large of a phase difference can result in voltage collapse and a subsequent blackout. In this paper, we propose a model for describing the aforementioned phase difference and obtain estimates of the model parameters. We subsequently use our model to formulate an equation that estimates the RUL of a given transmission line. This technique is applied to three different cases of phase difference that one might observe on an actual power grid, with estimates of the RUL presented for each case.
{"title":"Estimating the remaining useful life of power grid transmission lines using synchrophasor data","authors":"T. R. Walsh, S. Alhloul, M. Hajimorad","doi":"10.1109/ICPHM.2014.7036392","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036392","url":null,"abstract":"This paper addresses the use of traditional PHM techniques for estimating the remaining useful life (RUL) of a transmission line in a typical power grid. The sensor of interest is the Phasor Measurement Unit (PMU) which provides sampled data in the form of a synchrophasor. A Synchrophasor is a GPS based time stamped measurement of voltage and current at different physical locations on the grid. It is the fact that the measurements are time stamped to a common clock that allows one to obtain the absolute phase difference between measurements at different points in the grid. It is this phase difference that determines the health of the grid. Too large of a phase difference can result in voltage collapse and a subsequent blackout. In this paper, we propose a model for describing the aforementioned phase difference and obtain estimates of the model parameters. We subsequently use our model to formulate an equation that estimates the RUL of a given transmission line. This technique is applied to three different cases of phase difference that one might observe on an actual power grid, with estimates of the RUL presented for each case.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134421471","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036386
Z. Bluvband, S. Porotsky, Shimon Tropper
The article describes the Classification and Regression procedures, developed and successfully used for Critical Zone Recognition. One of the main tasks of the Prognostics and Health Management is the Failure Prognostics, specifically to provide predictive information regarding Remaining Useful Life (RUL) of device using prognostic systems. But sometimes it is necessary to get inflexible answer for closed type question: Is current device within critical zone or not? In other words, is RUL of device less than pre-defined Critical Value or not? To solve this problem, two approaches may be considered: · Regression Approach: to predict RUL value and compare results with critical value · Classification Approach: to recognize directly entering the critical zone In general, Classification Approach is more preferred for recognition tasks, but some aspects of the approach prevent to get an evident answer. Two models, based on modifications of the SVM method - SVC (Support Vector Classification) and SVR (Support Vector Regression) are proposed for consideration. Suggested methodology and algorithms were verified on the NASA Aircraft Engine database (http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/). Numerical examples, based on this database, have been also considered.
{"title":"Critical Zone Recognition: Classification vs. regression","authors":"Z. Bluvband, S. Porotsky, Shimon Tropper","doi":"10.1109/ICPHM.2014.7036386","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036386","url":null,"abstract":"The article describes the Classification and Regression procedures, developed and successfully used for Critical Zone Recognition. One of the main tasks of the Prognostics and Health Management is the Failure Prognostics, specifically to provide predictive information regarding Remaining Useful Life (RUL) of device using prognostic systems. But sometimes it is necessary to get inflexible answer for closed type question: Is current device within critical zone or not? In other words, is RUL of device less than pre-defined Critical Value or not? To solve this problem, two approaches may be considered: · Regression Approach: to predict RUL value and compare results with critical value · Classification Approach: to recognize directly entering the critical zone In general, Classification Approach is more preferred for recognition tasks, but some aspects of the approach prevent to get an evident answer. Two models, based on modifications of the SVM method - SVC (Support Vector Classification) and SVR (Support Vector Regression) are proposed for consideration. Suggested methodology and algorithms were verified on the NASA Aircraft Engine database (http://ti.arc.nasa.gov/tech/dash/pcoe/prognostic-data-repository/). Numerical examples, based on this database, have been also considered.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128735642","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}
Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036364
L. Klint, Henrik Lisby, Haical Abas Binthahir
Visual brand identities often and rapidly deteriorate (degradation to failure) if not strenuously, time-consumingly and continuously managed/maintained. There are several reasons for this and in this paper we identify the various visual brand identity processes and components, and the culprits/pitfalls (failure causes) that typically lead to a visual brand identity getting off track. In this paper, we also propose a PHM-based visual brand identity management system (VBIMS) to avoid or reduce such deterioration.
{"title":"Using a PHM-based visual brand identity management system to manage deterioration of visual brand identities and prolong their life span","authors":"L. Klint, Henrik Lisby, Haical Abas Binthahir","doi":"10.1109/ICPHM.2014.7036364","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036364","url":null,"abstract":"Visual brand identities often and rapidly deteriorate (degradation to failure) if not strenuously, time-consumingly and continuously managed/maintained. There are several reasons for this and in this paper we identify the various visual brand identity processes and components, and the culprits/pitfalls (failure causes) that typically lead to a visual brand identity getting off track. In this paper, we also propose a PHM-based visual brand identity management system (VBIMS) to avoid or reduce such deterioration.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132262866","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}
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