Pub Date : 2014-06-22DOI: 10.1109/ICPHM.2014.7036404
W. O. L. Vianna, Ivo Paixão de Medeiros, Bernardo Santos Aflalo, Leonardo Ramos Rodrigues, Joao Pedro Pinheiro Malere
This paper describes the application of the PHM concept to assess the State of Health (SoH) of a Proton Exchange Membrane Fuel Cell (PEMFC) as part of the IEEE PHM 2014 Data Challenge. Two regression approaches are used as health monitoring algorithms to estimate the impedance of the PEMFC. One was a linear regression and the other was a higher order polynomial regression combined with other function found on the literature. The linear regression presented the best results compared to the other method.
{"title":"Proton Exchange Membrane Fuel Cells (PEMFC) impedance estimation using regression analysis","authors":"W. O. L. Vianna, Ivo Paixão de Medeiros, Bernardo Santos Aflalo, Leonardo Ramos Rodrigues, Joao Pedro Pinheiro Malere","doi":"10.1109/ICPHM.2014.7036404","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036404","url":null,"abstract":"This paper describes the application of the PHM concept to assess the State of Health (SoH) of a Proton Exchange Membrane Fuel Cell (PEMFC) as part of the IEEE PHM 2014 Data Challenge. Two regression approaches are used as health monitoring algorithms to estimate the impedance of the PEMFC. One was a linear regression and the other was a higher order polynomial regression combined with other function found on the literature. The linear regression presented the best results compared to the other method.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"4 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":"134094878","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.2014.7036388
M. Watson, S. B. Johnson, L. Trevino
Human exploration of the solar system requires fully autonomous systems when travelling more than 5 light minutes from Earth. This autonomy is necessary to manage a large, complex spacecraft with limited crew members and skills available. The communication latency requires the vehicle to deal with events with only limited crew interaction in most cases. The engineering of these systems requires an extensive knowledge of the spacecraft systems, information theory, and autonomous algorithm characteristics. The characteristics of the spacecraft systems must be matched with the autonomous algorithm characteristics to reliably monitor and control the system. This presents a large system engineering problem. Recent work on product-focused, elegant system engineering will be applied to this application, looking at the full autonomy stack, the matching of autonomous systems to spacecraft systems, and the integration of different types of algorithms. Each of these areas will be outlined and a general approach defined for system engineering to provide the optimal solution to the given application context.
{"title":"System engineering of autonomous space vehicles","authors":"M. Watson, S. B. Johnson, L. Trevino","doi":"10.1109/ICPHM.2014.7036388","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036388","url":null,"abstract":"Human exploration of the solar system requires fully autonomous systems when travelling more than 5 light minutes from Earth. This autonomy is necessary to manage a large, complex spacecraft with limited crew members and skills available. The communication latency requires the vehicle to deal with events with only limited crew interaction in most cases. The engineering of these systems requires an extensive knowledge of the spacecraft systems, information theory, and autonomous algorithm characteristics. The characteristics of the spacecraft systems must be matched with the autonomous algorithm characteristics to reliably monitor and control the system. This presents a large system engineering problem. Recent work on product-focused, elegant system engineering will be applied to this application, looking at the full autonomy stack, the matching of autonomous systems to spacecraft systems, and the integration of different types of algorithms. Each of these areas will be outlined and a general approach defined for system engineering to provide the optimal solution to the given application context.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"76 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":"121860356","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.2014.7036368
Jing Lin, Ming Zhao
In this article, the challenges in condition monitoring and fault diagnosis of rotating machinery under speed-varying working condition is discussed. A general review is then given about the research progress of signal processing techniques dealing with non-stationary signal under that working condition. How to fully exploit the rich diagnostic information from the speed-varying processes is addressed. Some prospects are given finally.
{"title":"A review and strategy for the diagnosis of speed-varying machinery","authors":"Jing Lin, Ming Zhao","doi":"10.1109/ICPHM.2014.7036368","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036368","url":null,"abstract":"In this article, the challenges in condition monitoring and fault diagnosis of rotating machinery under speed-varying working condition is discussed. A general review is then given about the research progress of signal processing techniques dealing with non-stationary signal under that working condition. How to fully exploit the rich diagnostic information from the speed-varying processes is addressed. Some prospects are given finally.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"14 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":"122757546","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.2014.7036369
Yong Li, Xiufeng Wang, Jing Lin
Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced, which combines benefits of the wavelet transform and the bicoherence analysis. By simultaneously using the amplitude of the continuous wavelet transform and biphase information, this method can eliminate the spurious bicoherence coming from long coherence time waves and non phase coupling waves efficiently. Based on this method, two quadratic nonlinearity features are proposed for fault diagnosis of rolling element bearing. At the same time, the proposed features are applied to the real-world vibration data collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Experiment results demonstrate that the performance of the proposed features is much better than that of some original features.
{"title":"Fault diagnosis of rolling element bearing using nonlinear wavelet bicoherence features","authors":"Yong Li, Xiufeng Wang, Jing Lin","doi":"10.1109/ICPHM.2014.7036369","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036369","url":null,"abstract":"Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced, which combines benefits of the wavelet transform and the bicoherence analysis. By simultaneously using the amplitude of the continuous wavelet transform and biphase information, this method can eliminate the spurious bicoherence coming from long coherence time waves and non phase coupling waves efficiently. Based on this method, two quadratic nonlinearity features are proposed for fault diagnosis of rolling element bearing. At the same time, the proposed features are applied to the real-world vibration data collected from locomotive roller bearings with faults on inner race, outer race and rollers, respectively. Experiment results demonstrate that the performance of the proposed features is much better than that of some original features.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"35 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":"126484233","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.2014.7036407
Taejin Kim, Hyunjae Kim, J. Ha, Keunsu Kim, Jun-Seop Youn, J. Jung, B. Youn
The 2014 IEEE PHM data challenge problem deals with the state-of-health (SOH) of proton exchange membrane fuel cell (PEMFC) given two degradation data sets: (i) a reference data set (FC1) operated under constant current is fully given until 991 h and (ii) a test data set (FC2) operated under rippled current is partially given until 550h. The proposed research aims at predicting the SOH (or EIS spectra) of PEM fuel cell after 550h for FC2. First, a full scale equivalent circuit model (ECM) with 10 parameters is developed to describe the electrochemical physics of PEMFC more realistically. The model reduction is suggested because of limited data. Since some parameters remain nearly unchanged due to irrelevance to degradation, it is reasonable to use the degenerated 4-parameter ECM while fixing the other parameters at their means. Despite the model reduction, the degradation pattern is clearly observed through the degenerated 4-parameter ECM. Then the coefficients of the four parameters are estimated by building linear regression models between the parameters and voltage. Since the voltage change after 550h is not provided for FC2, the voltage degradation model is developed by modeling both reversible and irreversible degradation processes. This research also proposes a hybrid prognostic approach to the SOH (or EIS spectra) prediction. The voltage degradation model and the degenerated 4-parameter ECM are first developed based on the observation of the physical phenomenon. They are then trained for the purpose of the SOH prediction with the training EIS data sets (FC1 and FC2). It is demonstrated that this hybrid SOH prediction offers highly accurate prediction of the SOH (or EIS spectra) at t = 666, 830, and 1016h. Moreover, possible error sources are also discussed to further improve the prediction accuracy in future.
{"title":"A degenerated equivalent circuit model and hybrid prediction for state-of-health (SOH) of PEM fuel cell","authors":"Taejin Kim, Hyunjae Kim, J. Ha, Keunsu Kim, Jun-Seop Youn, J. Jung, B. Youn","doi":"10.1109/ICPHM.2014.7036407","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036407","url":null,"abstract":"The 2014 IEEE PHM data challenge problem deals with the state-of-health (SOH) of proton exchange membrane fuel cell (PEMFC) given two degradation data sets: (i) a reference data set (FC1) operated under constant current is fully given until 991 h and (ii) a test data set (FC2) operated under rippled current is partially given until 550h. The proposed research aims at predicting the SOH (or EIS spectra) of PEM fuel cell after 550h for FC2. First, a full scale equivalent circuit model (ECM) with 10 parameters is developed to describe the electrochemical physics of PEMFC more realistically. The model reduction is suggested because of limited data. Since some parameters remain nearly unchanged due to irrelevance to degradation, it is reasonable to use the degenerated 4-parameter ECM while fixing the other parameters at their means. Despite the model reduction, the degradation pattern is clearly observed through the degenerated 4-parameter ECM. Then the coefficients of the four parameters are estimated by building linear regression models between the parameters and voltage. Since the voltage change after 550h is not provided for FC2, the voltage degradation model is developed by modeling both reversible and irreversible degradation processes. This research also proposes a hybrid prognostic approach to the SOH (or EIS spectra) prediction. The voltage degradation model and the degenerated 4-parameter ECM are first developed based on the observation of the physical phenomenon. They are then trained for the purpose of the SOH prediction with the training EIS data sets (FC1 and FC2). It is demonstrated that this hybrid SOH prediction offers highly accurate prediction of the SOH (or EIS spectra) at t = 666, 830, and 1016h. Moreover, possible error sources are also discussed to further improve the prediction accuracy in future.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"151 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":"131693386","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.2014.7036385
N. J. Jameson, M. Azarian, M. Pecht
Solenoid operated valves are vital components in many process control systems. They are components that are often critical to safety. Solenoid valve degradation is difficult to detect in situ, leading to failures, which are often sudden and unexpected. This paper reviews some of the common causes of solenoid valve degradation and discusses strategies that leverage these mechanisms to detect and diagnose faults before they lead to failure.
{"title":"Fault diagnostic opportunities for solenoid operated valves using physics-of-failure analysis","authors":"N. J. Jameson, M. Azarian, M. Pecht","doi":"10.1109/ICPHM.2014.7036385","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036385","url":null,"abstract":"Solenoid operated valves are vital components in many process control systems. They are components that are often critical to safety. Solenoid valve degradation is difficult to detect in situ, leading to failures, which are often sudden and unexpected. This paper reviews some of the common causes of solenoid valve degradation and discusses strategies that leverage these mechanisms to detect and diagnose faults before they lead to failure.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"153 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":"123405931","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.2014.7036403
David R. Simmons, S. Marotta
Application of health monitoring technology and the capability to predict failures can potentially improve the efficiency and effectiveness of US Army missile sustainment. Sustainment functions throughout the weapon's lifecycle and across the spectrum of different Army missiles such as field testing, lab testing, inspections, retrograde, maintenance, reliability improvements, and supply functions offer many opportunities to better the sustainment status quo in both cost and performance. New technology solutions must however accrue benefits associated with metrics such as total life cycle cost, as well as other readiness-type metrics such as reporting accuracy, timeliness, uncertainty, and other factors. Application of health monitoring and failure prediction must provide an acceptably high return in benefits as compared to the investments needed upfront for development and deployment. Given those constraints, only the most innovative and well engineered applications of existing, emerging, and new technology will suffice in context even though overall sustainment improvements are highly desired. The information in this paper is intended to notionally describe the US Army's missile logistics and supply such that feasible and effective technology solutions can be targeted at specific processes, and thereby provide maximal quantifiable effect. The paper will also briefly address applied research currently being conducted that targets Return-on-Investment (ROI) risks and thereby better enable technology transition.
{"title":"Return-on-Investment potential for US Army missile health monitoring","authors":"David R. Simmons, S. Marotta","doi":"10.1109/ICPHM.2014.7036403","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036403","url":null,"abstract":"Application of health monitoring technology and the capability to predict failures can potentially improve the efficiency and effectiveness of US Army missile sustainment. Sustainment functions throughout the weapon's lifecycle and across the spectrum of different Army missiles such as field testing, lab testing, inspections, retrograde, maintenance, reliability improvements, and supply functions offer many opportunities to better the sustainment status quo in both cost and performance. New technology solutions must however accrue benefits associated with metrics such as total life cycle cost, as well as other readiness-type metrics such as reporting accuracy, timeliness, uncertainty, and other factors. Application of health monitoring and failure prediction must provide an acceptably high return in benefits as compared to the investments needed upfront for development and deployment. Given those constraints, only the most innovative and well engineered applications of existing, emerging, and new technology will suffice in context even though overall sustainment improvements are highly desired. The information in this paper is intended to notionally describe the US Army's missile logistics and supply such that feasible and effective technology solutions can be targeted at specific processes, and thereby provide maximal quantifiable effect. The paper will also briefly address applied research currently being conducted that targets Return-on-Investment (ROI) risks and thereby better enable technology transition.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"26 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":"131464781","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.2014.7036402
P. Lall, Kazi Mirza
Electronics in automotive underhood applications may be subjected to temperatures in the neighborhood of 150°C to 175°C. Several electronic functions such as lane departure warning systems, collision avoidance systems are critical to vehicle operation. Prior studies have shown that low silver leadfree SnAgCu alloys exhibit pronounced deterioration in mechanical properties even after short exposure to high temperatures. Current life prediction models for second level interconnects do not provide a method for quick-turn assessment of the effect of mean temperature on cyclic life. In this paper, a method has been developed for assessment of the effect of mean cyclic temperature on the thermal fatigue reliability based on physics based leading damage indicators including phase-growth rate and the intermetallic thickness. Since the quantification of the thermal profile in the field applications may be often very difficult, the proposed method does not require the acquisition of the thermal profile history. Three environments of -50°C to +50°C, 0°C to 100°C, 50°C to 150°C with identical thermal excursion and different mean temperatures have been studied. Test assemblies with three different packages including CABGA 144, PBGA 324, and PBGA 676 have been used for the study. Damage-proxy based damage-equivalency relationships have been derived for the three thermal cycles. Weibull distributions have been developed for the three test assemblies to evaluate the effect of the mean cyclic temperature on the thermal fatigue life. Data indicates that the thermal fatigue lie drops with the increase in mean temperature of the thermal cycle even if the thermal excursion magnitude is kept constant. Damage equivalency model predictions of the effect of mean temperature of the thermal cycle have been validated versus weibull life distributions. The damage proxy based damage equivalency methodology shows good correlation with experimental data.
{"title":"Prognostication of the effect of mean temperature of thermal cycle on SAC305 leadfree reliability using damage pre-cursors","authors":"P. Lall, Kazi Mirza","doi":"10.1109/ICPHM.2014.7036402","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036402","url":null,"abstract":"Electronics in automotive underhood applications may be subjected to temperatures in the neighborhood of 150°C to 175°C. Several electronic functions such as lane departure warning systems, collision avoidance systems are critical to vehicle operation. Prior studies have shown that low silver leadfree SnAgCu alloys exhibit pronounced deterioration in mechanical properties even after short exposure to high temperatures. Current life prediction models for second level interconnects do not provide a method for quick-turn assessment of the effect of mean temperature on cyclic life. In this paper, a method has been developed for assessment of the effect of mean cyclic temperature on the thermal fatigue reliability based on physics based leading damage indicators including phase-growth rate and the intermetallic thickness. Since the quantification of the thermal profile in the field applications may be often very difficult, the proposed method does not require the acquisition of the thermal profile history. Three environments of -50°C to +50°C, 0°C to 100°C, 50°C to 150°C with identical thermal excursion and different mean temperatures have been studied. Test assemblies with three different packages including CABGA 144, PBGA 324, and PBGA 676 have been used for the study. Damage-proxy based damage-equivalency relationships have been derived for the three thermal cycles. Weibull distributions have been developed for the three test assemblies to evaluate the effect of the mean cyclic temperature on the thermal fatigue life. Data indicates that the thermal fatigue lie drops with the increase in mean temperature of the thermal cycle even if the thermal excursion magnitude is kept constant. Damage equivalency model predictions of the effect of mean temperature of the thermal cycle have been validated versus weibull life distributions. The damage proxy based damage equivalency methodology shows good correlation with experimental data.","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":"130389498","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.2014.7036381
Yanjun Yan, James Z. Zhang
A healthy wind turbine is essential for efficient wind energy generation, and fault monitoring is required to ensure that. To detect faults accurately, while reducing false alarms, we need to identify the temporary power generation loss that is not due to fault. One major phenomenon that is not a fault but will cause power reduction is the wake effect. Wake effect is the blocking of the air flow towards one turbine by another turbine or other structures, when the wind is blowing in certain direction. Identifying the existence of wake can effectively reduce the false alarms of faults. Meanwhile, although wake is not a fault, it increases the turbine's structural loading and may eventually lead to a fault. Therefore, it is beneficial to understand how susceptible a turbine is to wake effect, which is affected by the layout of the wind farm and reflected in the data. Currently the wind farms are typically equipped with the capacity to collect SCADA (supervisory control and data acquisition) data in this standard data format; however, the large amount of SCADA data may not be fully utilized to its potential, and we propose to use the SCADA data to learn the wake pattern for each turbine. Our approach is driven by the data, without any pre-determined modeling, and hence it is automatic, adaptive and widely applicable. Specifically, we propose to represent the wind speed difference data against wind direction, similar to an image, and then we use an edge detector to discern the pattern in the data to capture the wake effect. Due to the nature of the images to be processed, where outlier pixels can be viewed as “salt & pepper” noise, we propose to use a linear prediction based entropy thresholding method for edge detection, to illustrate our concept for wake effect detection. To further improve wake detection accuracy, each extracted edge map was converted into a linear data series so that an envelope can be built based on the pixels forming the “valleys” and the “peaks”. The envelope enables simple and accurate measurements of the width and depth of the “valleys”. Finally, we generate a single wake pattern for each turbine of interest, by fusing the wake patterns caused by all the neighboring turbines, no matter how many neighboring turbines there are. The more the wind directions in the wake pattern, the more susceptible the turbine of interest is to wake effect. The accurate wake pattern generated by our approach is helpful to separate wakes from true faults, and to understand the vulnerability of the turbines.
{"title":"Using edge-detector to model wake effects on wind turbines","authors":"Yanjun Yan, James Z. Zhang","doi":"10.1109/ICPHM.2014.7036381","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036381","url":null,"abstract":"A healthy wind turbine is essential for efficient wind energy generation, and fault monitoring is required to ensure that. To detect faults accurately, while reducing false alarms, we need to identify the temporary power generation loss that is not due to fault. One major phenomenon that is not a fault but will cause power reduction is the wake effect. Wake effect is the blocking of the air flow towards one turbine by another turbine or other structures, when the wind is blowing in certain direction. Identifying the existence of wake can effectively reduce the false alarms of faults. Meanwhile, although wake is not a fault, it increases the turbine's structural loading and may eventually lead to a fault. Therefore, it is beneficial to understand how susceptible a turbine is to wake effect, which is affected by the layout of the wind farm and reflected in the data. Currently the wind farms are typically equipped with the capacity to collect SCADA (supervisory control and data acquisition) data in this standard data format; however, the large amount of SCADA data may not be fully utilized to its potential, and we propose to use the SCADA data to learn the wake pattern for each turbine. Our approach is driven by the data, without any pre-determined modeling, and hence it is automatic, adaptive and widely applicable. Specifically, we propose to represent the wind speed difference data against wind direction, similar to an image, and then we use an edge detector to discern the pattern in the data to capture the wake effect. Due to the nature of the images to be processed, where outlier pixels can be viewed as “salt & pepper” noise, we propose to use a linear prediction based entropy thresholding method for edge detection, to illustrate our concept for wake effect detection. To further improve wake detection accuracy, each extracted edge map was converted into a linear data series so that an envelope can be built based on the pixels forming the “valleys” and the “peaks”. The envelope enables simple and accurate measurements of the width and depth of the “valleys”. Finally, we generate a single wake pattern for each turbine of interest, by fusing the wake patterns caused by all the neighboring turbines, no matter how many neighboring turbines there are. The more the wind directions in the wake pattern, the more susceptible the turbine of interest is to wake effect. The accurate wake pattern generated by our approach is helpful to separate wakes from true faults, and to understand the vulnerability of the turbines.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"27 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":"128346579","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.2014.7036371
Chao Jin, Wenyu Zhao, Zongchang Liu, J. Lee, Xiao He
As diesel engines play a critical role in various applications, the ability for a health monitoring system to perform early fault diagnosis is of vital importance for the reliable functioning of a diesel engine throughout its service. In regard to the criticality of diesel engine fault diagnosis, an abundance of research efforts have been conducted by adopting injection-based, vibration-based, or instantaneous speed-based methods. The study presented in this paper discusses an integrated approach of diagnosing combustion faults and valve leakage, by combining vibration signal with cylinder pressure and revolution speed signals. Signal processing methods, including time frequency domain analysis such as wavelet decomposition are applied to extract features from data collected under different health conditions. The dimension of the feature set is further reduced based on discriminant analysis. Classification techniques are subsequently evaluated as fault diagnosis tools. The proposed approach is validated on a small-scale diesel engine test bed, and is to be applied in a real world health monitoring system.
{"title":"A vibration-based approach for diesel engine fault diagnosis","authors":"Chao Jin, Wenyu Zhao, Zongchang Liu, J. Lee, Xiao He","doi":"10.1109/ICPHM.2014.7036371","DOIUrl":"https://doi.org/10.1109/ICPHM.2014.7036371","url":null,"abstract":"As diesel engines play a critical role in various applications, the ability for a health monitoring system to perform early fault diagnosis is of vital importance for the reliable functioning of a diesel engine throughout its service. In regard to the criticality of diesel engine fault diagnosis, an abundance of research efforts have been conducted by adopting injection-based, vibration-based, or instantaneous speed-based methods. The study presented in this paper discusses an integrated approach of diagnosing combustion faults and valve leakage, by combining vibration signal with cylinder pressure and revolution speed signals. Signal processing methods, including time frequency domain analysis such as wavelet decomposition are applied to extract features from data collected under different health conditions. The dimension of the feature set is further reduced based on discriminant analysis. Classification techniques are subsequently evaluated as fault diagnosis tools. The proposed approach is validated on a small-scale diesel engine test bed, and is to be applied in a real world health monitoring system.","PeriodicalId":376942,"journal":{"name":"2014 International Conference on Prognostics and Health Management","volume":"24 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":"127240084","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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