Pub Date : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3300
Sylvain Poupry, Cédrick Béler, K. Medjaher
This thesis aims to set up a scientific approach to monitor and take preventive actions on the air quality for the actors of a territory not covered by conventional measuring stations. Thus, a Cyber-Physical System (CPS) approach combined with Prognostics Health Management (PHM) methodologies is chosen to move toward a self-monitoring and self-reconfiguration system. To collect data in an inexpensive manner, measurement stations with low-cost sensors (LCS) are developed. LCS have drawbacks and the first part of this thesis is the use of redundancy and a proposed algorithm to increase their hardware and data reliability. A first station is deployed as proof of concept and the region is already receiving real-time data. The next phase is to build forecasting models to help authorities make decisions.
{"title":"Contribution to the Design and Implementation of a Reflexive Cyber-Physical System: Application to Air Quality Prediction in the Vallees des Gaves","authors":"Sylvain Poupry, Cédrick Béler, K. Medjaher","doi":"10.36001/phme.2022.v7i1.3300","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3300","url":null,"abstract":"This thesis aims to set up a scientific approach to monitor and take preventive actions on the air quality for the actors of a territory not covered by conventional measuring stations. Thus, a Cyber-Physical System (CPS) approach combined with Prognostics Health Management (PHM) methodologies is chosen to move toward a self-monitoring and self-reconfiguration system. To collect data in an inexpensive manner, measurement stations with low-cost sensors (LCS) are developed. LCS have drawbacks and the first part of this thesis is the use of redundancy and a proposed algorithm to increase their hardware and data reliability. A first station is deployed as proof of concept and the region is already receiving real-time data. The next phase is to build forecasting models to help authorities make decisions.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123706606","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 : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3350
H. Park, N. Kim, Jooho Choi
In the safety critical systems such as industrial plants or aircraft, failure occurs inevitably during the operation, and it is important to prevent this while maintaining high availability. Therefore, a lot of efforts are being directed toward developing advanced prognostics algorithms and sensing techniques as an enabler for predictive maintenance. The key for reliable and accurate prediction not only relies on the prognostics algorithms but also based on the collection of sensor data. However, there is not much in-dept studies toward evaluating the varying sensing techniques based on the prediction performance and inspection scheduling. It would be more reasonable for practitioner to select different cost of sensors based on the sensors’ contribution on reducing the cost on unnecessary inspection or measurement while maintaining its prognosis performance. Thus, the authors try to thoroughly evaluate the cost-effectiveness of the different sensor with respect to sensor resistance to noise. The simulation is conducted to analyze the prediction performance with varying measurement interval and different level of noise during degradation. Then real run-to-fail (RTF) dataset acquired from two different sensors are analyzed to design optimal measurement system for predictive maintenance.
{"title":"Comparative Study of Health Monitoring Sensors based on Prognostic Performance","authors":"H. Park, N. Kim, Jooho Choi","doi":"10.36001/phme.2022.v7i1.3350","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3350","url":null,"abstract":"In the safety critical systems such as industrial plants or aircraft, failure occurs inevitably during the operation, and it is important to prevent this while maintaining high availability. Therefore, a lot of efforts are being directed toward developing advanced prognostics algorithms and sensing techniques as an enabler for predictive maintenance. The key for reliable and accurate prediction not only relies on the prognostics algorithms but also based on the collection of sensor data. However, there is not much in-dept studies toward evaluating the varying sensing techniques based on the prediction performance and inspection scheduling. It would be more reasonable for practitioner to select different cost of sensors based on the sensors’ contribution on reducing the cost on unnecessary inspection or measurement while maintaining its prognosis performance. Thus, the authors try to thoroughly evaluate the cost-effectiveness of the different sensor with respect to sensor resistance to noise. The simulation is conducted to analyze the prediction performance with varying measurement interval and different level of noise during degradation. Then real run-to-fail (RTF) dataset acquired from two different sensors are analyzed to design optimal measurement system for predictive maintenance.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134495406","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 : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3298
Weikun Deng, K. Nguyen, K. Medjaher
Sparse & noisy monitoring data leads to numerous challenges in prognostic and health management (PHM). Big data volume but poor quality with scarce healthy states information limits the performance of training machine learning (ML) and physics-based failure modeling. To address these challenges, this thesis aims to develop a new hybrid PHM framework with the ability to autonomously discover and exploit incomplete implicit physics knowledge in sparse & noisy monitoring data, providing a solution for deep physics knowledge-ML fusion by physics-informed machine learning algorithms. In addition, the developed hybrid framework also applies the self-supervised learning paradigm to significantly improve the learning performance under uncertain, sparse, and noisy data with lower requirements for specialist area knowledge. The performance of the developed algorithms will be investigated on the sparse and noise data generated by simulation data sets, public benchmark data sets, and the PHM platform to demonstrate its applicability.
{"title":"Physics Informed Self Supervised Learning For Fault Diagnostics and Prognostics in the Context of Sparse and Noisy Data","authors":"Weikun Deng, K. Nguyen, K. Medjaher","doi":"10.36001/phme.2022.v7i1.3298","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3298","url":null,"abstract":"Sparse & noisy monitoring data leads to numerous challenges in prognostic and health management (PHM). Big data volume but poor quality with scarce healthy states information limits the performance of training machine learning (ML) and physics-based failure modeling. To address these challenges, this thesis aims to develop a new hybrid PHM framework with the ability to autonomously discover and exploit incomplete implicit physics knowledge in sparse & noisy monitoring data, providing a solution for deep physics knowledge-ML fusion by physics-informed machine learning algorithms. In addition, the developed hybrid framework also applies the self-supervised learning paradigm to significantly improve the learning performance under uncertain, sparse, and noisy data with lower requirements for specialist area knowledge. The performance of the developed algorithms will be investigated on the sparse and noise data generated by simulation data sets, public benchmark data sets, and the PHM platform to demonstrate its applicability.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130117962","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 : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3316
Juseong Lee, Ingeborg de Pater, S. Boekweit, M. Mitici
Several studies have proposed Remaining-Useful-Life (RUL) prognostics for aircraft components in the last years. However, few studies focus on integrating these RUL prognostics into maintenance planning frameworks. This paper proposes an optimization model for opportunistic maintenance scheduling of aircraft components that integrates RUL prognostics and that groups the maintenance of these components to reduce costs. We illustrate our approach for the maintenance of a fleet of aircraft, each equipped with multiple landing gear brakes. RUL prognostics for the landing gear brakes are obtained using a Bayesian regression model. Based on these RUL prognostics, we group the replacement of brakes using an integer linear program. As a result, we obtain a cost-optimal RUL-driven opportunistic-maintenance schedule for the brakes of a fleet of aircraft. Compared with traditional maintenance strategies, our approach leads to a reduction of up to 20% of the total maintenance costs.
{"title":"Remaining-Useful-Life prognostics for opportunistic grouping of maintenance of landing gear brakes for a fleet of aircraft","authors":"Juseong Lee, Ingeborg de Pater, S. Boekweit, M. Mitici","doi":"10.36001/phme.2022.v7i1.3316","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3316","url":null,"abstract":"Several studies have proposed Remaining-Useful-Life (RUL) prognostics for aircraft components in the last years. However, few studies focus on integrating these RUL prognostics into maintenance planning frameworks. This paper proposes an optimization model for opportunistic maintenance scheduling of aircraft components that integrates RUL prognostics and that groups the maintenance of these components to reduce costs. We illustrate our approach for the maintenance of a fleet of aircraft, each equipped with multiple landing gear brakes. RUL prognostics for the landing gear brakes are obtained using a Bayesian regression model. Based on these RUL prognostics, we group the replacement of brakes using an integer linear program. As a result, we obtain a cost-optimal RUL-driven opportunistic-maintenance schedule for the brakes of a fleet of aircraft. Compared with traditional maintenance strategies, our approach leads to a reduction of up to 20% of the total maintenance costs.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130465322","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 : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3313
K. Hencken, Elsi-Mari Borrelli, D. Ceccarelli, A. Krivda
Partial Discharges are short breakdowns inside electrical equipment. As they indicate weaknesses of the insulation strength, they are seen as important precursors to a failure of the system. Therefore measurement and analysis of the patterns of instances in time and strength of the discharge are an important tool to analyze the insulation status of electric equipment, that has been addressed already using different methods in the past. In this work we explore how a physics-based stochastic process can be combined with Approximate Bayesian Computation (ABC) as a new way to analyze them. ABC is a method to infer probability distributions of model parameters in cases, where the likelihood is not tractable, but simulations can be done easily. As such it is of interest for complex phenomena or measurement systems, as often found in prognostics applications. Especially the ABC-SMC method was found to be useful here. Real Partial Discharge measurement data was used not only for parameter estimation, but also to do model comparison in order to compare different physical models proposed in the literature.
{"title":"Approximate Bayesian Computation as a New Tool for Partial Discharge Analysis of Partial Discharge Data","authors":"K. Hencken, Elsi-Mari Borrelli, D. Ceccarelli, A. Krivda","doi":"10.36001/phme.2022.v7i1.3313","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3313","url":null,"abstract":"Partial Discharges are short breakdowns inside electrical equipment. As they indicate weaknesses of the insulation strength, they are seen as important precursors to a failure of the system. Therefore measurement and analysis of the patterns of instances in time and strength of the discharge are an important tool to analyze the insulation status of electric equipment, that has been addressed already using different methods in the past. In this work we explore how a physics-based stochastic process can be combined with Approximate Bayesian Computation (ABC) as a new way to analyze them. ABC is a method to infer probability distributions of model parameters in cases, where the likelihood is not tractable, but simulations can be done easily. As such it is of interest for complex phenomena or measurement systems, as often found in prognostics applications. Especially the ABC-SMC method was found to be useful here. Real Partial Discharge measurement data was used not only for parameter estimation, but also to do model comparison in order to compare different physical models proposed in the literature.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130542464","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 : 2022-06-29DOI: 10.36001/phme.2022.v7i1.3326
Jørgen F. Pedersen, R. Schlanbusch, V. Shanbhag
Fluid leakage due to piston rod seal failure in hydraulic cylinders results in unscheduled maintenance, machine downtime and loss of productivity. Therefore, it is vital to understand the piston rod seal failure at initial stages. In literature, very few attempts have been made to implement forecasting techniques for piston rod seal failure in hydraulic cylinders using acoustic emission (AE) features. Therefore, in this study, we aim to forecast piston rod seal failure using AE features in the auto regressive integrated moving average (ARIMA) model. AE features like root mean square (RMS) and mean absolute percentage error (MAPE) were collected from run-to-failure (RTF) tests that were conducted on a hydraulic test rig. The hydraulic test rig replicates the piston rod movement and fluid leakage conditions similar to what is normally observed in hydraulic cylinders. To assess reliability of our study, two RTF tests were conducted at 15 mm/s and 25 mm/s rod speed each. The process of seal wear from unworn to worn state in the hydraulic test rig was accelerated by creating longitudinal scratches on the piston rod. An ARIMA model was developed based on the RMS features that were calculated from four RTF tests. The ARIMA model can forecast the RMS values ahead in time as long as the original series does not experience any large shifts in variance or deviates heavily from the normal increasing trend. The ARIMA model provided good accuracy in forecasting the seal failure in at least two of four RTF tests that were conducted. The ARIMA model that was fitted with 15 pre-samples was used to forecast 10 out of sequence samples, and it showed a maximum moving absolute percentage error (MAPE value) of 28.99 % and a minimum of 4.950 %. The forecasting technique based on ARIMA model and AE features proposed in this study lays a strong basis to be used in industries to schedule the seal change in hydraulic cylinders.
{"title":"Forecasting Piston Rod Seal Failure Based on Acoustic Emission Features in ARIMA Model","authors":"Jørgen F. Pedersen, R. Schlanbusch, V. Shanbhag","doi":"10.36001/phme.2022.v7i1.3326","DOIUrl":"https://doi.org/10.36001/phme.2022.v7i1.3326","url":null,"abstract":"Fluid leakage due to piston rod seal failure in hydraulic cylinders results in unscheduled maintenance, machine downtime and loss of productivity. Therefore, it is vital to understand the piston rod seal failure at initial stages. In literature, very few attempts have been made to implement forecasting techniques for piston rod seal failure in hydraulic cylinders using acoustic emission (AE) features. Therefore, in this study, we aim to forecast piston rod seal failure using AE features in the auto regressive integrated moving average (ARIMA) model. AE features like root mean square (RMS) and mean absolute percentage error (MAPE) were collected from run-to-failure (RTF) tests that were conducted on a hydraulic test rig. The hydraulic test rig replicates the piston rod movement and fluid leakage conditions similar to what is normally observed in hydraulic cylinders. To assess reliability of our study, two RTF tests were conducted at 15 mm/s and 25 mm/s rod speed each. The process of seal wear from unworn to worn state in the hydraulic test rig was accelerated by creating longitudinal scratches on the piston rod. An ARIMA model was developed based on the RMS features that were calculated from four RTF tests. The ARIMA model can forecast the RMS values ahead in time as long as the original series does not experience any large shifts in variance or deviates heavily from the normal increasing trend. The ARIMA model provided good accuracy in forecasting the seal failure in at least two of four RTF tests that were conducted. The ARIMA model that was fitted with 15 pre-samples was used to forecast 10 out of sequence samples, and it showed a maximum moving absolute percentage error (MAPE value) of 28.99 % and a minimum of 4.950 %. The forecasting technique based on ARIMA model and AE features proposed in this study lays a strong basis to be used in industries to schedule the seal change in hydraulic cylinders.","PeriodicalId":422825,"journal":{"name":"PHM Society European Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127739025","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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