This is a research-in-progress of designing an intelligent system for mangrove crab larval growth stages development characterization and detection. This research applies image processing, machine learning, and prototyping in the design of the system. An initial experiment is conducted to verify the accuracy of classification and recognition. The model achieved an average of 85% accuracy in classification of larval images samples. This study contributes to the development of the corpus of mangrove crab larval images in a context of a developing country. This paper also recommends further enhancement of the system.
{"title":"Classifying Mangrove Crub Images for Growth Stages Detection and Monitoring","authors":"Jasmin Almarinez, Alexander A. Hernandez","doi":"10.1109/SDPC.2019.00134","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00134","url":null,"abstract":"This is a research-in-progress of designing an intelligent system for mangrove crab larval growth stages development characterization and detection. This research applies image processing, machine learning, and prototyping in the design of the system. An initial experiment is conducted to verify the accuracy of classification and recognition. The model achieved an average of 85% accuracy in classification of larval images samples. This study contributes to the development of the corpus of mangrove crab larval images in a context of a developing country. This paper also recommends further enhancement of the system.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134208324","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}
A state assessment method based on fusion of grey correlation analysis and D-S evidence theory is proposed by taking the fire control computer and sensor subsystem of armored equipment as the research object. Firstly, the state evaluation model based on information fusion is established, and the basic knowledge of information fusion technology, D-S evidence theory and grey correlation degree are briefly introduced. Because there are many indicators in the research object and the signals are complicated to process. Therefore, the gray correlation analysis method is used to reduce the redundant index and optimize the index system. Then, the credibility distribution of each index in the reduction set is obtained, and the D-S evidence theory is used to fuse the results of the reduction to obtain the state assessment result. Finally, the gray correlation analysis method and D-S evidence theory constitute a complete information fusion system. The test results of the actual fire control system status data prove that the results are consistent with the prior knowledge, which proves the credibility of the method.
{"title":"State Assessment Method of Fire Control System Based on Fusion of Grey Relational and D-S Evidence Theory","authors":"Li Yingshun, Min Zheng, Weiyan Tong, X. Yi","doi":"10.1109/SDPC.2019.00034","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00034","url":null,"abstract":"A state assessment method based on fusion of grey correlation analysis and D-S evidence theory is proposed by taking the fire control computer and sensor subsystem of armored equipment as the research object. Firstly, the state evaluation model based on information fusion is established, and the basic knowledge of information fusion technology, D-S evidence theory and grey correlation degree are briefly introduced. Because there are many indicators in the research object and the signals are complicated to process. Therefore, the gray correlation analysis method is used to reduce the redundant index and optimize the index system. Then, the credibility distribution of each index in the reduction set is obtained, and the D-S evidence theory is used to fuse the results of the reduction to obtain the state assessment result. Finally, the gray correlation analysis method and D-S evidence theory constitute a complete information fusion system. The test results of the actual fire control system status data prove that the results are consistent with the prior knowledge, which proves the credibility of the method.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134256884","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}
Jun Shen, Hongyu Zhou, Jiahui Feng, Yang Chai, Qingyuan Wang
As Chinese railway network continues to expand from the eastern area to the western area, the accidents of trains forced parking caused by traction network failure occur in the course of operation from time to time, which not only seriously affects the economic and social development of China, but also poses a serious threat to the safety of passengers ' lives and property. When train power is lost, it will passively stop for waiting for rescuing or use the self-stored energy to carry out for self-rescue to the nearest station. For this reason, a grey linear regression model based on BP Neural network is proposed to determine easy parking points of train running interval with UAS simulation platform, and compared with UAS simulation results, it is proved that the BP neural network grey system can complete the determination of easy parking points of train running interval well.
{"title":"Determination of Easy Parking Points of Train Driving Interval Based on UAS and BP Neural Network Linear Grey system","authors":"Jun Shen, Hongyu Zhou, Jiahui Feng, Yang Chai, Qingyuan Wang","doi":"10.1109/SDPC.2019.00031","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00031","url":null,"abstract":"As Chinese railway network continues to expand from the eastern area to the western area, the accidents of trains forced parking caused by traction network failure occur in the course of operation from time to time, which not only seriously affects the economic and social development of China, but also poses a serious threat to the safety of passengers ' lives and property. When train power is lost, it will passively stop for waiting for rescuing or use the self-stored energy to carry out for self-rescue to the nearest station. For this reason, a grey linear regression model based on BP Neural network is proposed to determine easy parking points of train running interval with UAS simulation platform, and compared with UAS simulation results, it is proved that the BP neural network grey system can complete the determination of easy parking points of train running interval well.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131966461","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}
With the development of artificial neutral network and control science, black box control has become one of the most popular topics for the researchers because of its good performance of the self-adaptivity, robustness and antidisturbance in recent years. Since there are lots of drawbacks for the BP neutral networks such as low converging speed and uncertainly of network structure and weight factors. This paper develops a new modified F-R algorithm to improve converging speed of back propagation neutral network and tries to eliminate the bad effect to the whole control system caused by uncertainty. The topology structure and weight factor of the neutral network are optimized by using GA (Genetic Algorithm) offline. This paper introduces servo control system, network optimization algorithm, gradient descent algorithm, and modified Fletcher- Reeves algorithm. The black box algorithm is programed in MATLAB and simulated in the Simulink for control system. During the simulating experiment, the load disturbance is added to test the capability to withstand the disturbance. The results show that the modified Fletcher-Reeves algorithm has the better performance in the response time, overshooting and antidisturbance ability compared with other two methods. In the end, the experiment is carried out based on the successful simulation. The control program is finished in LabVIEW and applied to the servo-control systems of EMA (Electron-mechanic Actuator). The results indicate the response of the system has the better stability and rapidity, which can meet the requirements of engineering application greatly.
{"title":"The Research of New Black Box Control Method Based on Conjugate Gradient Algorithm","authors":"Weiwei Ma, Yong Zhou, Jiakuan Gao","doi":"10.1109/SDPC.2019.00065","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00065","url":null,"abstract":"With the development of artificial neutral network and control science, black box control has become one of the most popular topics for the researchers because of its good performance of the self-adaptivity, robustness and antidisturbance in recent years. Since there are lots of drawbacks for the BP neutral networks such as low converging speed and uncertainly of network structure and weight factors. This paper develops a new modified F-R algorithm to improve converging speed of back propagation neutral network and tries to eliminate the bad effect to the whole control system caused by uncertainty. The topology structure and weight factor of the neutral network are optimized by using GA (Genetic Algorithm) offline. This paper introduces servo control system, network optimization algorithm, gradient descent algorithm, and modified Fletcher- Reeves algorithm. The black box algorithm is programed in MATLAB and simulated in the Simulink for control system. During the simulating experiment, the load disturbance is added to test the capability to withstand the disturbance. The results show that the modified Fletcher-Reeves algorithm has the better performance in the response time, overshooting and antidisturbance ability compared with other two methods. In the end, the experiment is carried out based on the successful simulation. The control program is finished in LabVIEW and applied to the servo-control systems of EMA (Electron-mechanic Actuator). The results indicate the response of the system has the better stability and rapidity, which can meet the requirements of engineering application greatly.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131990301","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}
Considering the shortcomings of a single Group Method of Data Handling (GMDH) network that is easy to fall into local optimum, this paper proposes an integrated GMDH framework for Remaining Useful Life (RUL) prediction. The framework generates three GMDH networks through different division of training data, and integrates the results of the three GMDH networks with a three-layer back propagation (BP) neural network. The NASA C-MAPSS dataset is used to evaluate the effectiveness of the proposed methodˈ by comparison with the prediction results of a single GMDH network and Long Short-Term Memory (LSTM) network. The results show that the proposed method can effectively improve the generalization ability of the GMDH network and is superior to the LSTM in terms of root mean squared error (RMSE).
{"title":"Integrated Group Method of Data Handing Framework for Remaining Useful Life Prediction","authors":"Xin Ge, Shunjie Zhang, Q. Cheng, Xuejun Zhao, Yong Qin","doi":"10.1109/SDPC.2019.00160","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00160","url":null,"abstract":"Considering the shortcomings of a single Group Method of Data Handling (GMDH) network that is easy to fall into local optimum, this paper proposes an integrated GMDH framework for Remaining Useful Life (RUL) prediction. The framework generates three GMDH networks through different division of training data, and integrates the results of the three GMDH networks with a three-layer back propagation (BP) neural network. The NASA C-MAPSS dataset is used to evaluate the effectiveness of the proposed methodˈ by comparison with the prediction results of a single GMDH network and Long Short-Term Memory (LSTM) network. The results show that the proposed method can effectively improve the generalization ability of the GMDH network and is superior to the LSTM in terms of root mean squared error (RMSE).","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"214 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132359622","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}
Virtual maintenance is the application of virtual reality technology in equipment maintenance. The purpose of this paper is to propose a method for joint analysis of virtual and actual maintenance time by means of virtual maintenance technology. We first establish a joint parameter model considering the virtual and real maintenance time under the common factor and give a method to predict the actual maintenance time based on the model. Secondly, discuss the model parameter estimation methods and some properties of the estimates for complete and missing data and then study the feasibility of the parameter estimation method based on EM algorithm with missing data and the stability of the estimate in different missing rates through numerical simulation. Finally, we use the proposed method to analyze the example of equipment maintenance in virtual and actual environment.
{"title":"A Joint Model of Virtual and Actual Maintenance Time with Covariates","authors":"Xiaoyue Xie, Jian Shi, X. Yi, Shulin Liu","doi":"10.1109/SDPC.2019.00054","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00054","url":null,"abstract":"Virtual maintenance is the application of virtual reality technology in equipment maintenance. The purpose of this paper is to propose a method for joint analysis of virtual and actual maintenance time by means of virtual maintenance technology. We first establish a joint parameter model considering the virtual and real maintenance time under the common factor and give a method to predict the actual maintenance time based on the model. Secondly, discuss the model parameter estimation methods and some properties of the estimates for complete and missing data and then study the feasibility of the parameter estimation method based on EM algorithm with missing data and the stability of the estimate in different missing rates through numerical simulation. Finally, we use the proposed method to analyze the example of equipment maintenance in virtual and actual environment.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165909","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}
Due to the excellent performance and cost-effective, unmanned aerial vehicle (UAV) has been widely used in civil and military fields. But the accident rate of UAV is much higher than that of manned aircraft. Therefore, the sensor data monitoring of UAV has become a research hotspot, which can further support UAV Prognostics and Health Management (PHM). However, the on-board computing resources and power are limited, and most state-of-the-art sensor data monitoring methods can only be operated on ground. A huge challenge is presented to UAV real-time condition monitoring. In this paper, an on-board system is developed for real-time fixed-wing UAV sensor monitoring. Firstly, an LSTM network is designed to fulfill accurate estimation of UAV sensor data. Secondly, the sensor data estimation model with high computational complexity is accelerated by utilizing High Level Synthesis (HLS). Finally, the calculation optimized model is deployed in an on-board embedded hardware platform. The simulated fixed-wing UAV flight data are used to verify the performance of the proposed system. The experimental results show that the proposed system is effective for fixed-wing UAV real-time sensor data estimation.
{"title":"On-board Sensor Data Monitoring System For Unmanned Aerial Vehicle PHM","authors":"Mingxi Jiang, Benkuan Wang, Datong Liu, Yu Peng","doi":"10.1109/SDPC.2019.00102","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00102","url":null,"abstract":"Due to the excellent performance and cost-effective, unmanned aerial vehicle (UAV) has been widely used in civil and military fields. But the accident rate of UAV is much higher than that of manned aircraft. Therefore, the sensor data monitoring of UAV has become a research hotspot, which can further support UAV Prognostics and Health Management (PHM). However, the on-board computing resources and power are limited, and most state-of-the-art sensor data monitoring methods can only be operated on ground. A huge challenge is presented to UAV real-time condition monitoring. In this paper, an on-board system is developed for real-time fixed-wing UAV sensor monitoring. Firstly, an LSTM network is designed to fulfill accurate estimation of UAV sensor data. Secondly, the sensor data estimation model with high computational complexity is accelerated by utilizing High Level Synthesis (HLS). Finally, the calculation optimized model is deployed in an on-board embedded hardware platform. The simulated fixed-wing UAV flight data are used to verify the performance of the proposed system. The experimental results show that the proposed system is effective for fixed-wing UAV real-time sensor data estimation.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128090402","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}
Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What’s more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.
{"title":"A Fault Diagnosis Method for Valve Train of Diesel Engine Considering Incomplete Feature Set","authors":"Zhinong Jiang, Y. Lai, Zijia Wang, Jinjie Zhang","doi":"10.1109/SDPC.2019.00161","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00161","url":null,"abstract":"Abnormal valve clearance is a common fault of diesel engine, and early warning of abnormal valve clearance plays an important role in the condition based maintenance of diesel engine. Although information fusion technology can improve the accuracy of fault diagnosis, it cannot guarantee that the fused features can perfectly represent the required key information. For the incomplete feature set, a method combining multi-domain feature and improved support vector machine is proposed. Firstly, the extraction of multi-domain feature is carried out to deeply explore the state information of valve train contained in the original vibration signal. The statistical characteristics and waveform characteristics are extracted from time domain vibration signals, and the frequency domain feature similar to time-domain feature is extracted after the Fourier transform of the vibration signal. What’s more, according to the working principle of diesel engine, the energy characteristics in angular frequency domain are extracted. Then, an improved support vector machine method based on multi-domain feature is proposed to further reduce the diagnostic errors caused by incomplete feature set. Finally, the proposed method is compared with other traditional methods about the fault diagnosis of valve train of diesel engine. The results show that the proposed method is applicable to the fault diagnosis of valve train of diesel engine with good accuracy, and the generalization ability of diagnostic model is greatly improved.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134325247","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}
Considering the photovoltaic volatility, intermittency and random, the accurate prediction of photovoltaic power output is very important for grid dispatching and energy management. In order to improve the accuracy of photovoltaic system short-term power prediction, this paper analyzes the relationship between the power output and the environment factors. The principal component analysis (PCA) based particle group-ridge wave neural network model and support vector machine regression (SVR) for short-term prediction model are developed. In this paper, the PCA is used to reduce the number of input environment factors and extract the main components. The ridge wave neural network parameters are selected by particle swarm optimization (PSO). The SVR model is used to optimize the network structure for a better model performance. The correlation and reliability of the prediction results are discussed. The results show that, excluding the influence of weather interference factors, SVR has higher precision and accuracy in prediction model, smaller mean variance, and better prediction effect in the prediction mode.
{"title":"Prediction of Photovoltaic Power Generation Based on PSO-RNN and SVR Model","authors":"Z. Luo, F. Fang","doi":"10.1109/SDPC.2019.00174","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00174","url":null,"abstract":"Considering the photovoltaic volatility, intermittency and random, the accurate prediction of photovoltaic power output is very important for grid dispatching and energy management. In order to improve the accuracy of photovoltaic system short-term power prediction, this paper analyzes the relationship between the power output and the environment factors. The principal component analysis (PCA) based particle group-ridge wave neural network model and support vector machine regression (SVR) for short-term prediction model are developed. In this paper, the PCA is used to reduce the number of input environment factors and extract the main components. The ridge wave neural network parameters are selected by particle swarm optimization (PSO). The SVR model is used to optimize the network structure for a better model performance. The correlation and reliability of the prediction results are discussed. The results show that, excluding the influence of weather interference factors, SVR has higher precision and accuracy in prediction model, smaller mean variance, and better prediction effect in the prediction mode.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116084624","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}
Leng Han, Xinyu Wang, Song Feng, Dewei Yang, Hong Xiao
An effectively way to detect potential failures of mechanical equipment is to detect its lubricating oil. For ships and steam turbines, large-diameter wear debris detection sensors are recommended to meet the actual demand of high throughput. However, when wear debris cuts the magnetic line through the flow path, due to the coupling of gravity, lubricating oil and magnetic field, the wear debris move in a curve, which will have an important effect on the induced voltage of the sensor. Therefore, it is necessary to research the influence of the debris trajectory on the output signal of inductive sensor. A series of experimental was conducted in this paper to research the change of the corresponding induced voltage when the wear debris flows the magnetic field at different angles and the experimental results show that the radial angle affects not only the amplitude of the output signal but also the waveform. Conversely, the axial angle takes barley effect on the output signal.
{"title":"Influence of the debris trajectory on output inductive signal in large-diameter particle detector","authors":"Leng Han, Xinyu Wang, Song Feng, Dewei Yang, Hong Xiao","doi":"10.1109/SDPC.2019.00117","DOIUrl":"https://doi.org/10.1109/SDPC.2019.00117","url":null,"abstract":"An effectively way to detect potential failures of mechanical equipment is to detect its lubricating oil. For ships and steam turbines, large-diameter wear debris detection sensors are recommended to meet the actual demand of high throughput. However, when wear debris cuts the magnetic line through the flow path, due to the coupling of gravity, lubricating oil and magnetic field, the wear debris move in a curve, which will have an important effect on the induced voltage of the sensor. Therefore, it is necessary to research the influence of the debris trajectory on the output signal of inductive sensor. A series of experimental was conducted in this paper to research the change of the corresponding induced voltage when the wear debris flows the magnetic field at different angles and the experimental results show that the radial angle affects not only the amplitude of the output signal but also the waveform. Conversely, the axial angle takes barley effect on the output signal.","PeriodicalId":403595,"journal":{"name":"2019 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116806944","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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