Pub Date : 2023-06-04DOI: 10.36001/IJPHM.2020.V11I1.2594
Yigit A. Yucesan, F. Viana
Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation).
{"title":"A Physics-informed Neural Network for Wind Turbine Main Bearing Fatigue","authors":"Yigit A. Yucesan, F. Viana","doi":"10.36001/IJPHM.2020.V11I1.2594","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I1.2594","url":null,"abstract":"Unexpected main bearing failure on a wind turbine causes unwanted maintenance and increased operation costs (mainly due to crane, parts, labor, and production loss). Unfortunately, historical data indicates that failure can happen far earlier than the component design lives. Root cause analysis investigations have pointed to problems inherent from manufacturing as the major contributor, as well as issues related to event loads (e.g., startups, shutdowns, and emergency stops), extreme environmental conditions, and maintenance practices, among others. Altogether, the multiple failure modes and contributors make modeling the remaining useful life of main bearings a very daunting task. In this paper, we present a novel physics-informed neural network modeling approach for main bearing fatigue. The proposed approach is fully hybrid and designed to merge physics-informed and data-driven layers within deep neural networks. The result is a cumulative damage model where the physics-informed layers are used model the relatively well-understood physics (L10 fatigue life) and the data-driven layers account for the hard to model components (i.e., grease degradation).","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43552821","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 : 2023-06-04DOI: 10.36001/ijphm.2019.v10i3.2629
A. Natarajan, Emil Laftchiev
Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have hown that thermal comfort is a key component of human performance in the work place and that personalized thermal comfort models can be learned from user labeled data that is collected from wearable devices and room sensors. These personalized thermal comfort models can then be used to optimize the thermal comfort of room occupants to maximize their performance. Unfortunately, personalized thermal comfort models can only be learned after extensive dataset collection and user labeling. This paper addresses this challenge by proposing a transfer active learning framework for thermal comfort prediction that reduces the burdensome task of collecting large labeled datasets for each new user. The framework leverages domain knowledge from prior users and an active learning strategy for new users that reduces the necessary size of the labeled dataset. When tested on a real dataset collected from five users, this framework achieves a 70% reduction in the required size of the labeled dataset as compared to the fully supervised learning approach. Specifically, the framework achieves a mean error of 0.822±0.05, while the supervised learning approach achieves a mean error of 0.852±0.04.
{"title":"Transfer Active Learning Framework to Predict Thermal Comfort","authors":"A. Natarajan, Emil Laftchiev","doi":"10.36001/ijphm.2019.v10i3.2629","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2629","url":null,"abstract":"Personal thermal comfort is the feeling that individuals have about how hot, cold or comfortable they are. Studies have hown that thermal comfort is a key component of human performance in the work place and that personalized thermal comfort models can be learned from user labeled data that is collected from wearable devices and room sensors. These personalized thermal comfort models can then be used to optimize the thermal comfort of room occupants to maximize their performance. Unfortunately, personalized thermal comfort models can only be learned after extensive dataset collection and user labeling. This paper addresses this challenge by proposing a transfer active learning framework for thermal comfort prediction that reduces the burdensome task of collecting large labeled datasets for each new user. The framework leverages domain knowledge from prior users and an active learning strategy for new users that reduces the necessary size of the labeled dataset. When tested on a real dataset collected from five users, this framework achieves a 70% reduction in the required size of the labeled dataset as compared to the fully supervised learning approach. Specifically, the framework achieves a mean error of 0.822±0.05, while the supervised learning approach achieves a mean error of 0.852±0.04.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46801546","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 : 2023-06-04DOI: 10.36001/ijphm.2019.v10i3.2624
W. Fink
Predictive Health Management (PHM), originally applied in the Aerospace Industry, tries to predict when what part would fail for what reason(s) in order to make preventive maintenance more efficient and cost-effective. Over the past several years, PHM has been infused increasingly into the human healthcare, precision medicine, and human performance sectors. As such, a diverse and trans-disciplinary group of expert authors presents in this Special Issue on PHM for Human Health & Performance its perspectives on PHM in the context of prognostics and health management for human health and performance, both on Earth and in space, in nine excellent contributions that cover a wide range of current research and application topics related to this emerging field. In particular, these contributions highlight various technological and analytical aspects that in combination contribute and make a reality an autonomous healthcare paradigm. These aspects include, but are not limited to: wearable smart sensors, rehabilitation devices and robotics, image classification, signal processing, data mining, data understanding, machine learning, prediction and diagnosis, electronic health records and databases, and overarching PHM-based healthcare frameworks, etc.
{"title":"Special Issue on PHM for Human Health & Performance","authors":"W. Fink","doi":"10.36001/ijphm.2019.v10i3.2624","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2624","url":null,"abstract":"Predictive Health Management (PHM), originally applied in the Aerospace Industry, tries to predict when what part would fail for what reason(s) in order to make preventive maintenance more efficient and cost-effective. Over the past several years, PHM has been infused increasingly into the human healthcare, precision medicine, and human performance sectors. As such, a diverse and trans-disciplinary group of expert authors presents in this Special Issue on PHM for Human Health & Performance its perspectives on PHM in the context of prognostics and health management for human health and performance, both on Earth and in space, in nine excellent contributions that cover a wide range of current research and application topics related to this emerging field. In particular, these contributions highlight various technological and analytical aspects that in combination contribute and make a reality an autonomous healthcare paradigm. These aspects include, but are not limited to: wearable smart sensors, rehabilitation devices and robotics, image classification, signal processing, data mining, data understanding, machine learning, prediction and diagnosis, electronic health records and databases, and overarching PHM-based healthcare frameworks, etc.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48656627","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 : 2023-06-04DOI: 10.36001/ijphm.2019.v10i4.2610
Mélanie Ducoffe, I. Haloui, J. Gupta
Modern vehicles are more and more connected. For instance, in the aerospace industry, newer aircraft are already equipped with data concentrators and enough wireless connectivity to transmit sensor data collected during the whole flight to the ground, usually when the airplane is at the gate. Moreover, platforms that were not designed with such capability can be retrofitted to install devices that enable wireless data collection,as is done on Airbus A320 family. For military and heavy helicopters, HUMS (Health and Usage Monitoring System) also allows the collection of sensor data. Finally, satellites send continuously to the ground sensor data, called telemetries. Most of the time, fortunately, the platforms behave normally, faults and failures are thus rare. In order to go beyond corrective or preventive maintenance, and anticipate future faults and failures, we have to look for any drift, any change, in systems’ behavior, in data that is normal almost all the time. Moreover, collected sensor data is time series data. The problem is then anomaly detection or novelty detection in time series data. Among machine learning techniques that can be used to analyze data, Deep Learning, especially Convolutional Neural Networks, is very popular since it has surpassed human capacities for image classification and object detection. In this field, Generative Adversarial Networks are a technique to generate data similar to a potentially high dimension original dataset. In our case, generate new data could be useful to enrich the learning dataset with generated abnormal data to make it less unbalanced. Yet we are more interested in the potential of such techniques to perform anomaly detection for high dimensional data, comparing newly observed data with data that could have been generated from a distribution built from normal examples.
{"title":"Anomaly Detection on Time Series with Wasserstein GAN applied to PHM","authors":"Mélanie Ducoffe, I. Haloui, J. Gupta","doi":"10.36001/ijphm.2019.v10i4.2610","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i4.2610","url":null,"abstract":"Modern vehicles are more and more connected. For instance, in the aerospace industry, newer aircraft are already equipped with data concentrators and enough wireless connectivity to transmit sensor data collected during the whole flight to the ground, usually when the airplane is at the gate. Moreover, platforms that were not designed with such capability can be retrofitted to install devices that enable wireless data collection,as is done on Airbus A320 family. For military and heavy helicopters, HUMS (Health and Usage Monitoring System) also allows the collection of sensor data. Finally, satellites send continuously to the ground sensor data, called telemetries. Most of the time, fortunately, the platforms behave normally, faults and failures are thus rare. In order to go beyond corrective or preventive maintenance, and anticipate future faults and failures, we have to look for any drift, any change, in systems’ behavior, in data that is normal almost all the time. Moreover, collected sensor data is time series data. The problem is then anomaly detection or novelty detection in time series data. Among machine learning techniques that can be used to analyze data, Deep Learning, especially Convolutional Neural Networks, is very popular since it has surpassed human capacities for image classification and object detection. In this field, Generative Adversarial Networks are a technique to generate data similar to a potentially high dimension original dataset. In our case, generate new data could be useful to enrich the learning dataset with generated abnormal data to make it less unbalanced. Yet we are more interested in the potential of such techniques to perform anomaly detection for high dimensional data, comparing newly observed data with data that could have been generated from a distribution built from normal examples.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42727636","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 : 2023-06-04DOI: 10.36001/IJPHM.2020.V11I1.2609
Diana Barraza, V´ıctor G. Tercero-G´omez, A. Cordero-Franco, M. Beruvides
The monitoring of condition variables for maintenance purposes is a growing trend amongst researchers and practitioners where decisions are based on degradation levels. The two approaches in Condition-Based Maintenance (CBM) are diagnosing the level of degradation (diagnostics) or predicting when a certain level of degradation will be reached (prognostics). Using diagnostics determines when it is necessary to perform maintenance, but it rarely allows for estimation of future degradation. In the second case, prognostics does allow for degradation and failure prediction, however, its major drawback lies in when to perform the analysis, and exactly what information should be used for predictions. This encumbrance is due to previous studies that have shown that degradation variable could undergo a change that misleads these calculations. This paper addresses the issue of identifying explosive changes in condition variables, using Control Charts, to determine when to perform a new model fitting in order to obtain more accurate Remaining Useful Life (RUL) estimations. The diagnostic-prognostic methodology allows for discarding pre-change observations to avoid contamination in condition prediction. In addition the performance of the integration methodology is compared against adaptive autoregressive (AR) models. Results show that using only the observations acquired after the out-of-control signal produces more accurate RUL estimations.
{"title":"Remaining Useful Life Estimation Based on Detection of Explosive Changes: Analysis of Bearing Vibration","authors":"Diana Barraza, V´ıctor G. Tercero-G´omez, A. Cordero-Franco, M. Beruvides","doi":"10.36001/IJPHM.2020.V11I1.2609","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I1.2609","url":null,"abstract":"The monitoring of condition variables for maintenance purposes is a growing trend amongst researchers and practitioners where decisions are based on degradation levels. The two approaches in Condition-Based Maintenance (CBM) are diagnosing the level of degradation (diagnostics) or predicting when a certain level of degradation will be reached (prognostics). Using diagnostics determines when it is necessary to perform maintenance, but it rarely allows for estimation of future degradation. In the second case, prognostics does allow for degradation and failure prediction, however, its major drawback lies in when to perform the analysis, and exactly what information should be used for predictions. This encumbrance is due to previous studies that have shown that degradation variable could undergo a change that misleads these calculations. This paper addresses the issue of identifying explosive changes in condition variables, using Control Charts, to determine when to perform a new model fitting in order to obtain more accurate Remaining Useful Life (RUL) estimations. The diagnostic-prognostic methodology allows for discarding pre-change observations to avoid contamination in condition prediction. In addition the performance of the integration methodology is compared against adaptive autoregressive (AR) models. Results show that using only the observations acquired after the out-of-control signal produces more accurate RUL estimations.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41729737","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 : 2023-06-04DOI: 10.36001/IJPHM.2020.V11I1.2608
S. Voronov, Mattias Krysander, E. Frisk
Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple-readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.
{"title":"Predictive Maintenance of Lead-Acid Batteries with Sparse Vehicle Operational Data","authors":"S. Voronov, Mattias Krysander, E. Frisk","doi":"10.36001/IJPHM.2020.V11I1.2608","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I1.2608","url":null,"abstract":"Predictive maintenance aims to predict failures in components of a system, a heavy-duty vehicle in this work, and do maintenance before any actual fault occurs. Predictive maintenance is increasingly important in the automotive industry due to the development of new services and autonomous vehicles with no driver who can notice first signs of a component problem. The lead-acid battery in a heavy vehicle is mostly used during engine starts, but also for heating and cooling the cockpit, and is an important part of the electrical system that is essential for reliable operation. This paper develops and evaluates two machine-learning based methods for battery prognostics, one based on Long Short-Term Memory (LSTM) neural networks and one on Random Survival Forest (RSF). The objective is to estimate time of battery failure based on sparse and non-equidistant vehicle operational data, obtained from workshop visits or over-the-air readouts. The dataset has three characteristics: 1) no sensor measurements are directly related to battery health, 2) the number of data readouts vary from one vehicle to another, and 3) readouts are collected at different time periods. Missing data is common and is addressed by comparing different imputation techniques. RSF- and LSTM-based models are proposed and evaluated for the case of sparse multiple-readouts. How to measure model performance is discussed and how the amount of vehicle information influences performance.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49033806","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 : 2023-06-04DOI: 10.36001/ijphm.2020.v11i1.2606
Myeongbaek Youn, Yunhan Kim, Dongki Lee, Minki Cho, Byeng D. Youn
A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts, (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately.
{"title":"Fatigue Crack Length Estimation and Prediction using Trans-fitting with Support Vector Regression","authors":"Myeongbaek Youn, Yunhan Kim, Dongki Lee, Minki Cho, Byeng D. Youn","doi":"10.36001/ijphm.2020.v11i1.2606","DOIUrl":"https://doi.org/10.36001/ijphm.2020.v11i1.2606","url":null,"abstract":"A method is described in this paper for crack propagation prediction using only the initial crack length of the target specimen. The proposed method consists of two parts, (1) crack length estimation using support vector regression (SVR) and (2) crack length prediction using a new trans-fitting method. Features based on the filtered wave signals were defined and a model was constructed using the SVR method to estimate the crack length. The hyper-parameters of the SVR model were selected based on a grid search algorithm. Prediction of the crack length was based on the previous crack length, which was estimated based on the wave signals. In this step, a newly proposed trans-fitting method was applied. The proposed trans-fitting method updated the selected candidate function to translocate the trend of crack propagation based on the training dataset. By translocating the trends to the estimated crack length of the target specimen, the crack propagation could be predicted. The proposed method was validated by comparison with given specimens. The results show that the proposed method can estimate and predict the crack length accurately.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134927097","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 : 2023-06-04DOI: 10.36001/ijphm.2019.v10i4.2623
P. Costa, A. Akçay, Yingqian Zhang, U. Kaymak
Machine Prognostics and Health Management (PHM) is often concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions enable equipment health assessment and maintenance planning. In this work, we propose a Long Short-Term Memory (LSTM) network combined with global Attention mechanisms to learn RUL relationships directly from time-series sensor data. We use the NASA Commercial Modular Aero- Propulsion System Simulation (C-MAPPS) datasets to assess the performance of our proposed method. We compare our approach with current state-of-the-art methods on the same datasets and show that our results yield competitive results. Moreover, our method does not require previous degradation knowledge, and attention weights can be used to visualise temporal relationships between inputs and predicted outputs.
{"title":"Attention and Long Short-Term Memory Network for Remaining Useful Lifetime Predictions of Turbofan Engine Degradation","authors":"P. Costa, A. Akçay, Yingqian Zhang, U. Kaymak","doi":"10.36001/ijphm.2019.v10i4.2623","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i4.2623","url":null,"abstract":"Machine Prognostics and Health Management (PHM) is often concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions enable equipment health assessment and maintenance planning. In this work, we propose a Long Short-Term Memory (LSTM) network combined with global Attention mechanisms to learn RUL relationships directly from time-series sensor data. We use the NASA Commercial Modular Aero- Propulsion System Simulation (C-MAPPS) datasets to assess the performance of our proposed method. We compare our approach with current state-of-the-art methods on the same datasets and show that our results yield competitive results. Moreover, our method does not require previous degradation knowledge, and attention weights can be used to visualise temporal relationships between inputs and predicted outputs.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45422713","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 : 2023-06-04DOI: 10.36001/ijphm.2019.v10i3.2706
M. Johnson, M. J. Sobrepera, E. Kina, Rochelle J. Mendonca
To address shortages in rehabilitation clinicians and provide for the growing numbers of elder and disabled patients needing rehabilitation, we have been working towards developing an affordable socially assistive robot for remote therapy and health monitoring. Our system is being designed to initially work via remote control, while addressing some of the challenges of traditional telepresence. To understand how to design a system to meet the needs of elders, we created a mobile therapy robot prototype from two commercial robots and demonstrated this system to clinicians in two types of rehabilitation care settings, a daycare setting and a inpatient rehabilitation setting. We propose to introduce the prototype as a social and therapy agent into clinician-patient interactions with the aim of improving the quality of information transfer between the clinician and the patient. This paper describes an investigative effort to understand how clinicians who work with elders accept this prototype. Clinicians from each setting differed in their needs for the robot. Those in daycare settings preferred a more social robot to encourage and motivate elders to exercise as well as monitor their health. Clinicians in the inpatient rehabilitation setting desired a robot with more therapeutic and treatment capabilities. Both groups wanted a robot with some autonomy that was portable, maintainable, affordable, and durable. We discuss these results in detail along with the ethical implications of increasing the robots autonomy and suggest additional requirements for achieving a smarter robot that can meet the clinicians social, health monitoring and prognostication desires.
{"title":"Design of an Affordable Socially Assistive Robot for Remote Health and Function Monitoring and Prognostication","authors":"M. Johnson, M. J. Sobrepera, E. Kina, Rochelle J. Mendonca","doi":"10.36001/ijphm.2019.v10i3.2706","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2706","url":null,"abstract":"To address shortages in rehabilitation clinicians and provide for the growing numbers of elder and disabled patients needing rehabilitation, we have been working towards developing an affordable socially assistive robot for remote therapy and health monitoring. Our system is being designed to initially work via remote control, while addressing some of the challenges of traditional telepresence. To understand how to design a system to meet the needs of elders, we created a mobile therapy robot prototype from two commercial robots and demonstrated this system to clinicians in two types of rehabilitation care settings, a daycare setting and a inpatient rehabilitation setting. We propose to introduce the prototype as a social and therapy agent into clinician-patient interactions with the aim of improving the quality of information transfer between the clinician and the patient. This paper describes an investigative effort to understand how clinicians who work with elders accept this prototype. Clinicians from each setting differed in their needs for the robot. Those in daycare settings preferred a more social robot to encourage and motivate elders to exercise as well as monitor their health. Clinicians in the inpatient rehabilitation setting desired a robot with more therapeutic and treatment capabilities. Both groups wanted a robot with some autonomy that was portable, maintainable, affordable, and durable. We discuss these results in detail along with the ethical implications of increasing the robots autonomy and suggest additional requirements for achieving a smarter robot that can meet the clinicians social, health monitoring and prognostication desires.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44609594","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 : 2023-06-04DOI: 10.36001/IJPHM.2020.V11I1.2591
Luca Grosso, Andrea De Martin, G. Jacazio, M. Sorli
Robotic roller hemming is currently one of the most used solution for joining metal sheets in automotive industry, especially for those production lines which need to favor flexibility with respect to raw productivity and is mostly employed to assemble car doors. Hemming is a fairly delicate process since it does not only suffice a technical requirement – to join two panels together – but also an aesthetical one, since the joint panels are an integral part of the vehicle design and as such an important selling point. An unpredicted rupture or an advanced degradation condition of the system would lead to a significant loss in the quality of the final product or to a sudden stoppage of the production line. The development of a PHM system for hemming devices would hence provide a significant advantage, especially if designed to work for both new and legacy equipment. In this paper, we provide the results of a preliminary analysis of a new PHM framework for robotic roller hemming studied to work without having access to PLC data, the employed data-driven methodology is detailed and applied to the case of increasing wear in the head finger roll. Results from different prognostics routines are hence presented and compared.
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