Pub Date : 2023-06-04DOI: 10.36001/ijphm.2019.v10i3.2626
Yan Zhang
Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.
{"title":"Patient-Specific Readmission Prediction and Intervention for Health Care","authors":"Yan Zhang","doi":"10.36001/ijphm.2019.v10i3.2626","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2626","url":null,"abstract":"Hospital readmission is often associated with unfavorable patient outcomes and a large cost of resources. Therefore, preventing avoidable re-hospitalizations is imperative. To target this problem, one important metric that researchers and practitioners strive to reduce is the 30-day hospital readmission rate. In this paper, we introduce a general decision support system that utilizes machine learning (ML) based patientspecific prediction to guide the suggestion of patient intervention program assignment, with the objective of minimizing the readmission cost for hospitals. This work has three major contributions. First, the proposed solution is highly scalable by using PySpark. Second, we outline solution architecture components including (1) data injection (both real-time sensor reading and data at rest), processing, and analysis, and (2) ML model building, evaluation, deployment and scoring. Third, we discuss how the ML prediction results can be taken into account in a decision support system by presenting a rich visualization.","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":"41880019","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.v10i3.2707
J. Runyon, Min Jia, M. Goldstein, Perry Skeath, L. Abrell, J. Chorover, E. Sternberg
The simultaneous measurement of cortisol with its downstream metabolites in human eccrine sweat is a sensitive approach to capture minute-to-minute stress responses. This study investigates exercise stress induced time dependent dynamic changes in cortisol, cortisone and downstream inactive cortisol metabolites in human eccrine sweat using a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Cortisol and metabolite production (change in concentration over time) was measured in sweat at different time points during an administered exercise stress session with four healthy volunteers. Biomarker production plots were found to be highly individualized and sensitive to stress interventions such as exercise, and corresponded with stress response measures such as increases in heart rate. The LC-MS/MS method yielded baseline resolution between cortisol and cortisol metabolites with lower levels of detection and quantitation for each compound below 1 partper-billion (ppb). Cortisol and cortisol metabolites were found at concentrations ranging from 1 – 25 ppb in human eccrine sweat. They were also found to be stable in sweat with respect to temperature (37 C for up to 5 hours), pH (3-9) and freeze/thaw cycles (up to 4) This indicates that changes in these biomarker concentrations and their rate of production are due to stress-related physiological enzyme activation, rather than passive degradation in sweat. The physiological status of enzyme activation is thus captured and preserved in human eccrine sweat samples. This is advantageous for the development of wearable devices and methodologies which can assess human health, stress, wellbeing and performance.
{"title":"Dynamic Behavior of Cortisol and Cortisol Metabolites in Human Eccrine Sweat","authors":"J. Runyon, Min Jia, M. Goldstein, Perry Skeath, L. Abrell, J. Chorover, E. Sternberg","doi":"10.36001/ijphm.2019.v10i3.2707","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2707","url":null,"abstract":"The simultaneous measurement of cortisol with its downstream metabolites in human eccrine sweat is a sensitive approach to capture minute-to-minute stress responses. This study investigates exercise stress induced time dependent dynamic changes in cortisol, cortisone and downstream inactive cortisol metabolites in human eccrine sweat using a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Cortisol and metabolite production (change in concentration over time) was measured in sweat at different time points during an administered exercise stress session with four healthy volunteers. Biomarker production plots were found to be highly individualized and sensitive to stress interventions such as exercise, and corresponded with stress response measures such as increases in heart rate. The LC-MS/MS method yielded baseline resolution between cortisol and cortisol metabolites with lower levels of detection and quantitation for each compound below 1 partper-billion (ppb). Cortisol and cortisol metabolites were found at concentrations ranging from 1 – 25 ppb in human eccrine sweat. They were also found to be stable in sweat with respect to temperature (37 C for up to 5 hours), pH (3-9) and freeze/thaw cycles (up to 4) This indicates that changes in these biomarker concentrations and their rate of production are due to stress-related physiological enzyme activation, rather than passive degradation in sweat. The physiological status of enzyme activation is thus captured and preserved in human eccrine sweat samples. This is advantageous for the development of wearable devices and methodologies which can assess human health, stress, wellbeing and 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":"46492264","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.v11i1.2605
Hyeon Bae Kong, Soo-Ho Jo, Joon Ha Jung, Jong M. Ha, Yong Chang Shin, Heonjun Yoon, Kyung Ho Sun, Yun-Ho Seo, Byung Chul Jeon
Lamb-wave-based nondestructive testing and evaluation (NDT/E) methods have drawn much attention due to their potential to inspect plate-like structures in a variety of industrial applications. To estimate and/or predict fatigue crack growth, many research efforts have been made to develop data-driven or physics-based methods. Data-driven methods show high predictive capability without the need for physical domain knowledge; however, fewer data can lead to overfitting in the results. On the other hand, physics-based methods can provide reliable results without the need for measured data; however, small amounts of physical information can worsen their predictive capability. In real applications, both the measurable data and the physical information of systems may be considerably limited; it is thus challenging to estimate and/or predict the crack length using either the data-driven or physics-based method alone. To make use of the advantages and minimize the disadvantages of each method, the work outlined in this paper aims to develop a hybrid approach that combines the data-driven and the physics-based methods for estimation and prediction of fatigue crack growth with and without Lamb wave signals. First, with Lamb wave signals, a data-driven method based on signal processing and the random forest model can be used estimate crack lengths. Second, in the absence of Lamb wave signals, a physics-based method based on an ensemble prognostics approach and Walker’s equation can be used to predict crack lengths with the help of the previously estimated crack lengths. To demonstrate the validity of the proposed approach, a case study is presented using datasets provided in the 2019 PHM Conference Data Challenge by the PHM Society. The case study confirms that the proposed method shows high accuracy; the RMSEs for specimens T7 and T8 are calculated as 0.2021 and 0.551, respectively. A penalty score is calculated as 7.63, this result led to a 2nd place finish in the Data Challenge. To the best of the authors’ knowledge, this is the first attempt to propose a hybrid approach for estimation and prediction of fatigue crack growth.
{"title":"A Hybrid Approach of Data-driven and Physics-based Methods for Estimation and Prediction of Fatigue Crack Growth","authors":"Hyeon Bae Kong, Soo-Ho Jo, Joon Ha Jung, Jong M. Ha, Yong Chang Shin, Heonjun Yoon, Kyung Ho Sun, Yun-Ho Seo, Byung Chul Jeon","doi":"10.36001/ijphm.2020.v11i1.2605","DOIUrl":"https://doi.org/10.36001/ijphm.2020.v11i1.2605","url":null,"abstract":"Lamb-wave-based nondestructive testing and evaluation (NDT/E) methods have drawn much attention due to their potential to inspect plate-like structures in a variety of industrial applications. To estimate and/or predict fatigue crack growth, many research efforts have been made to develop data-driven or physics-based methods. Data-driven methods show high predictive capability without the need for physical domain knowledge; however, fewer data can lead to overfitting in the results. On the other hand, physics-based methods can provide reliable results without the need for measured data; however, small amounts of physical information can worsen their predictive capability. In real applications, both the measurable data and the physical information of systems may be considerably limited; it is thus challenging to estimate and/or predict the crack length using either the data-driven or physics-based method alone. To make use of the advantages and minimize the disadvantages of each method, the work outlined in this paper aims to develop a hybrid approach that combines the data-driven and the physics-based methods for estimation and prediction of fatigue crack growth with and without Lamb wave signals. First, with Lamb wave signals, a data-driven method based on signal processing and the random forest model can be used estimate crack lengths. Second, in the absence of Lamb wave signals, a physics-based method based on an ensemble prognostics approach and Walker’s equation can be used to predict crack lengths with the help of the previously estimated crack lengths. To demonstrate the validity of the proposed approach, a case study is presented using datasets provided in the 2019 PHM Conference Data Challenge by the PHM Society. The case study confirms that the proposed method shows high accuracy; the RMSEs for specimens T7 and T8 are calculated as 0.2021 and 0.551, respectively. A penalty score is calculated as 7.63, this result led to a 2nd place finish in the Data Challenge. To the best of the authors’ knowledge, this is the first attempt to propose a hybrid approach for estimation and prediction of fatigue crack growth.","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":"134927096","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.v10i3.2625
V. B, Parvathy C R, H. A. M., K. P K
Tumor hypoxia results in most of the anticancer drugs becoming ineffective. However, due to lack of proper signaling in the hypoxic micro environment, the condition cannot be detected in advance, leading into unnecessary delay in the diagnosis and treatment. The main objective of the work is to identify the hypoxia prone SNPs to help the patients to predict their possibility of hypoxia formation and to Design and develop a machine helping in diagnosing the hypoxia from pathological images using deep learning with 'convolution neural network. The genetic signatures corresponding to 'tumor hypoxia development' have been identified by pharmacogenomic method, comprising of genomics, epigenomics, metagenomics and environmental genomics. All the common hypoxia related mutations have been included in the study. The formation of the hypoxia condition has to be carefully identified and monitored during the process of treatment to ensure that the right drug is being administered. In the present manuscript, a novel method of elucidating the condition using deep convolution network from simple pathological image has been suggested. The efficiency of the suggested machine is found to be 92.8% making it as a potential device for prediction of hypoxia mutation and thereby helping us to monitor the hypoxic conditions effectively. Thus, the hypoxia prone SNPs corresponding to common mutations have been identified. The patients having the hypoxia prone SNPs are advised to guard against hypoxia formation with the help of diagnostic tests using the machine. The machine helps to warn the patients against the respective mutations from simple pathological image of the tumor cells.
{"title":"Tumor Hypoxia Diagnosis using Deep CNN Learning strategy a theranostic pharmacogenomic approach","authors":"V. B, Parvathy C R, H. A. M., K. P K","doi":"10.36001/ijphm.2019.v10i3.2625","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2625","url":null,"abstract":"Tumor hypoxia results in most of the anticancer drugs becoming ineffective. However, due to lack of proper signaling in the hypoxic micro environment, the condition cannot be detected in advance, leading into unnecessary delay in the diagnosis and treatment. The main objective of the work is to identify the hypoxia prone SNPs to help the patients to predict their possibility of hypoxia formation and to Design and develop a machine helping in diagnosing the hypoxia from pathological images using deep learning with 'convolution neural network. The genetic signatures corresponding to 'tumor hypoxia development' have been identified by pharmacogenomic method, comprising of genomics, epigenomics, metagenomics and environmental genomics. All the common hypoxia related mutations have been included in the study. The formation of the hypoxia condition has to be carefully identified and monitored during the process of treatment to ensure that the right drug is being administered. In the present manuscript, a novel method of elucidating the condition using deep convolution network from simple pathological image has been suggested. The efficiency of the suggested machine is found to be 92.8% making it as a potential device for prediction of hypoxia mutation and thereby helping us to monitor the hypoxic conditions effectively. Thus, the hypoxia prone SNPs corresponding to common mutations have been identified. The patients having the hypoxia prone SNPs are advised to guard against hypoxia formation with the help of diagnostic tests using the machine. The machine helps to warn the patients against the respective mutations from simple pathological image of the tumor cells.","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":"43329749","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.V11I1.2595
E. Jakobsson, R. Pettersson, E. Frisk, Mattias Krysander
The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.
{"title":"Fatigue Damage Monitoring for Mining Vehicles using Data Driven Models","authors":"E. Jakobsson, R. Pettersson, E. Frisk, Mattias Krysander","doi":"10.36001/IJPHM.2020.V11I1.2595","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I1.2595","url":null,"abstract":"The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.","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":"41647406","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.V10I3.2628
Karan Jain, Arijit Guha, A. Patra
Atherosclerosis refers to the plaque deposition in the arteries that can eventually lead to any of the three cardiovascular diseases, namely, heart attack, stroke, or peripheral vascular disease, depending upon the site of the blockage in the human arterial network. This work attempts to prognose this pathological condition via lumped cardiovascular modeling while utilizing the radial artery blood pressure measurements. To achieve this, the cardiovascular system has been modeled as a third order non-linear system with explicit emphasis on the systemic circulation. The parameters of the model are estimated using non-linear least squares estimation technique by minimizing the error between the measured and the estimated arterial pressure waveforms. The arterial pressure is found to be sensitive to three of the model parameters, namely, arterial compliance, systemic vascular resistance, and the peak cardiac muscle elastance. Based on the analysis, a growth model of systolic blood pressure is developed as a function of the arterial blockage. A particle filter based mathematical framework is then utilized to predict the time it would take to reach the stage of critical arterial blockage that may cause heart attacks.
{"title":"Particle Filter Based Framework for the Prognosis of Atherosclerosis via Lumped Cardiovascular Modeling","authors":"Karan Jain, Arijit Guha, A. Patra","doi":"10.36001/IJPHM.2019.V10I3.2628","DOIUrl":"https://doi.org/10.36001/IJPHM.2019.V10I3.2628","url":null,"abstract":"Atherosclerosis refers to the plaque deposition in the arteries that can eventually lead to any of the three cardiovascular diseases, namely, heart attack, stroke, or peripheral vascular disease, depending upon the site of the blockage in the human arterial network. This work attempts to prognose this pathological condition via lumped cardiovascular modeling while utilizing the radial artery blood pressure measurements. To achieve this, the cardiovascular system has been modeled as a third order non-linear system with explicit emphasis on the systemic circulation. The parameters of the model are estimated using non-linear least squares estimation technique by minimizing the error between the measured and the estimated arterial pressure waveforms. The arterial pressure is found to be sensitive to three of the model parameters, namely, arterial compliance, systemic vascular resistance, and the peak cardiac muscle elastance. Based on the analysis, a growth model of systolic blood pressure is developed as a function of the arterial blockage. A particle filter based mathematical framework is then utilized to predict the time it would take to reach the stage of critical arterial blockage that may cause heart attacks.","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":"47455960","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.V11I1.2593
Takaaki Tagawa, Y. Tadokoro, T. Yairi
In this paper, we propose a novel framework to help human operators- who are domain experts but not necessarily familiar with statistics- analyze a complex system and find unknown changes and causes. Despite the prevalence, researchers have rarely tackled this problem. Our framework focuses on the representation and explanation of changes occurring between two datasets, specifically the normal data and data with the observed changes. We employ two-dimensional scatter plots which can provide comprehensive representation without requiring statistical knowledge. This helps a human operator to intuitively understand the change and the cause. An analysis to find two-attribute pairs whose scatter plots well explain the change does not require high computational complexity owing to the novel characteristic function-based approach. Although a hyper-parameter needs to be determined, our analysis introduces a novel appropriate prior distribution to determine the proper hyper-parameter automatically. The experimental results show that our method presents the change and the cause with the same accuracy as that of the state-of-the-art kernel hypothesis testing approaches, while reducing the computational costs by almost 99% at the maximum for all popular benchmark datasets. The experiment using real vehicle driving data demonstrates the practicality of our framework.
{"title":"Scalable Change Analysis and Representation Using Characteristic Function","authors":"Takaaki Tagawa, Y. Tadokoro, T. Yairi","doi":"10.36001/IJPHM.2020.V11I1.2593","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I1.2593","url":null,"abstract":"In this paper, we propose a novel framework to help human operators- who are domain experts but not necessarily familiar with statistics- analyze a complex system and find unknown changes and causes. Despite the prevalence, researchers have rarely tackled this problem. Our framework focuses on the representation and explanation of changes occurring between two datasets, specifically the normal data and data with the observed changes. We employ two-dimensional scatter plots which can provide comprehensive representation without requiring statistical knowledge. This helps a human operator to intuitively understand the change and the cause. An analysis to find two-attribute pairs whose scatter plots well explain the change does not require high computational complexity owing to the novel characteristic function-based approach. Although a hyper-parameter needs to be determined, our analysis introduces a novel appropriate prior distribution to determine the proper hyper-parameter automatically. The experimental results show that our method presents the change and the cause with the same accuracy as that of the state-of-the-art kernel hypothesis testing approaches, while reducing the computational costs by almost 99% at the maximum for all popular benchmark datasets. The experiment using real vehicle driving data demonstrates the practicality of our framework.","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":"42127401","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.v10i3.2627
A. Popov, W. Fink, A. Hess
This paper discusses a Prognostics and Health Management [PHM]-based approach to implementing Human Health & Performance [HH&P] technologies. Targeted specifically are NASA Autonomous Medical Decision and Integrated Biomedical Informatics of Human Health, Life Support, and Habitation Systems in Technology Area 06 [TA 06] of NASA integrated technology roadmap [April 2012]. The proposed PHM-based implementation is to bridge PHM, an engineering discipline, to the HH&P technology domain to mitigate space travel risks by focusing on efforts to reduce countermeasure mass and volume, and drive down risks to an acceptable level. NASA Autonomous Medical Decision technology is based on wireless handheld devices and is a result of a necessary paradigm shift from telemedicine to HH&P autonomy. The Integrated Biomedical Informatics technology is based on Crew Electronic Health Records [CEHR], equipped with a predictive diagnostics capability developed for use by crew members rather than by healthcare professionals. This paper further explores the proposed PHM-based solutions for crew health maintenance in terms of predictive diagnostics to provide early and actionable real-time warnings to each crew member about health-related risks and impending health problems that otherwise might go undetected. The paper also discusses the paradigm’s hypothesis and its innovation methodology, as implemented with computed biomarkers. The suggested paradigm is to be validated on the International Space Station [ISS] to ensure that crew autonomy in terms of the inherent predictive capability and two-fault-tolerance of the methodology become the dominant design drivers in sustaining crew health and performance.
{"title":"Paradigm Shift from Telemedicine to Autonomous Human Health and Performance for Long-Duration Space Missions","authors":"A. Popov, W. Fink, A. Hess","doi":"10.36001/ijphm.2019.v10i3.2627","DOIUrl":"https://doi.org/10.36001/ijphm.2019.v10i3.2627","url":null,"abstract":"This paper discusses a Prognostics and Health Management [PHM]-based approach to implementing Human Health & Performance [HH&P] technologies. Targeted specifically are NASA Autonomous Medical Decision and Integrated Biomedical Informatics of Human Health, Life Support, and Habitation Systems in Technology Area 06 [TA 06] of NASA integrated technology roadmap [April 2012]. The proposed PHM-based implementation is to bridge PHM, an engineering discipline, to the HH&P technology domain to mitigate space travel risks by focusing on efforts to reduce countermeasure mass and volume, and drive down risks to an acceptable level. NASA Autonomous Medical Decision technology is based on wireless handheld devices and is a result of a necessary paradigm shift from telemedicine to HH&P autonomy. The Integrated Biomedical Informatics technology is based on Crew Electronic Health Records [CEHR], equipped with a predictive diagnostics capability developed for use by crew members rather than by healthcare professionals. This paper further explores the proposed PHM-based solutions for crew health maintenance in terms of predictive diagnostics to provide early and actionable real-time warnings to each crew member about health-related risks and impending health problems that otherwise might go undetected. The paper also discusses the paradigm’s hypothesis and its innovation methodology, as implemented with computed biomarkers. The suggested paradigm is to be validated on the International Space Station [ISS] to ensure that crew autonomy in terms of the inherent predictive capability and two-fault-tolerance of the methodology become the dominant design drivers in sustaining crew health and 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":"44991182","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 : 2023-05-15DOI: 10.36001/ijphm.2023.v14i1.3431
A. Trilla, N. Mijatovic, Xavier Vilasis-Cardona
This work develops a versatile approach to discover anomalies in operational data for nominal (i.e., non-parametric) subsystem event signals using unsupervised Deep Learning techniques. Firstly, it builds a neural convolutional frameworkto extract both intrasubsystem and intersubsystem patterns. This is done by applying banks of voxel filters on the charteddata. Secondly, it generalizes the learned embedded regularity of a Variational Autoencoder manifold by merging latentspace-overlapping deviations with non-overlapping synthetic irregularities. Contingencies like novel data, modeldrift, etc., are therefore seamlessly managed by the proposed data-augmented approach. Finally, it creates a smooth diagnosis probabilistic function on the ensuing low-dimensional distributed representation. The resulting enhanced solution warrants analytically strong tools for a critical industrial environment. It also facilitates its hierarchical integrability, and provides visually interpretable insights of the degraded condition hazard to increase the confidence in its predictions. This strategy has been validated with eight pairwise-interrelated subsystems from high-speed trains. Its outcome also leads to further reliable explainability from a causal perspective.
{"title":"Unsupervised Probabilistic Anomaly Detection Over Nominal Subsystem Events Through a Hierarchical Variational Autoencoder","authors":"A. Trilla, N. Mijatovic, Xavier Vilasis-Cardona","doi":"10.36001/ijphm.2023.v14i1.3431","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i1.3431","url":null,"abstract":"This work develops a versatile approach to discover anomalies in operational data for nominal (i.e., non-parametric) subsystem event signals using unsupervised Deep Learning techniques. Firstly, it builds a neural convolutional frameworkto extract both intrasubsystem and intersubsystem patterns. This is done by applying banks of voxel filters on the charteddata. Secondly, it generalizes the learned embedded regularity of a Variational Autoencoder manifold by merging latentspace-overlapping deviations with non-overlapping synthetic irregularities. Contingencies like novel data, modeldrift, etc., are therefore seamlessly managed by the proposed data-augmented approach. Finally, it creates a smooth diagnosis probabilistic function on the ensuing low-dimensional distributed representation. The resulting enhanced solution warrants analytically strong tools for a critical industrial environment. It also facilitates its hierarchical integrability, and provides visually interpretable insights of the degraded condition hazard to increase the confidence in its predictions. This strategy has been validated with eight pairwise-interrelated subsystems from high-speed trains. Its outcome also leads to further reliable explainability from a causal perspective.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44804367","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 : 2023-03-25DOI: 10.36001/ijphm.2023.v14i1.3393
Oliver Gnepper, Hannes Hitzer, Olaf Enge-Rosenblatt
Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.
{"title":"Predictive Diagnosis in Axial Piston Pumps","authors":"Oliver Gnepper, Hannes Hitzer, Olaf Enge-Rosenblatt","doi":"10.36001/ijphm.2023.v14i1.3393","DOIUrl":"https://doi.org/10.36001/ijphm.2023.v14i1.3393","url":null,"abstract":"Increasing reliability, availability and safety requirements as well as an increasing amount of data acquisition systems have enabled condition-based maintenance in mobile and industrial machinery. In this paper, we present a methodology to develop a robust diagnostic approach. This includes the consideration of variable operating conditions in the data acquisition process as well as a versatile, non domain-specific feature extraction technique. By doing so, we train anomaly detection models for different fault types and different fault intensities in variable displacement axial piston pumps. Our specific interest points to the investigation of high-frequency condition indicators with a sampling rate of 1 MHz. Furthermore, we compare those to industry standard sensors, sampled with up to 20 kHz.By considering variable operating conditions, we are able to quantify the influence of the operating point. The results show, that high-frequency features are a suitable condition-indicator across several operating points and can be used to detect faults more easily. Although set up on a test-bench, the experimental design allows to draw conclusions about realistic field operational conditions.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2023-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48605687","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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