International Journal of Prognostics and Health Management最新文献

{"title":"Review of Technologies for Automatic Health Monitoring of Structures and Buildings","authors":"Artem Basko, O. Ponomarova, Y. Prokopchuk","doi":"10.36001/ijphm.2021.v12i2.3015","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i2.3015","url":null,"abstract":"Research in the field of structural monitoring of structures, buildings and structures is not abating. A key link in a modern wireless monitoring system is a sensor built using wireless technologies. Undoubtedly, wireless sensors are gradually replacing wired systems that are difficult to maintain, connect and costly. However, we should not forget about wired systems, wireless sensor networks are a new stage in the development of structural monitoring.\u0000The level of development of monitoring systems and wireless sensors for monitoring tasks has not yet been fully investigated for their universal application in various applications. There are also software restrictions associated with the creation and configuration of sensor networks.\u0000The importance of using automatic monitoring systems lies in their application in smart homes as monitoring system for the condition of a building and as a human security system.\u0000According to this study, it aims to provide a comprehensive overview of structural health monitoring over the years. In particular, this article reviewed and analyzed the main components of wireless communication, such as: hardware of smart wireless sensors, wireless protocol, network architecture, operating systems. This review also presents the scope of both test benches and real deployments of such systems.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48471253","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}

{"title":"Use of Nonlinear Optics for Assessment of Cable Polymer Aging","authors":"Kaylee N. Rellaford, Dallin L Smith, Alex Farnsworth, Shane M. Drake, H. Lee, J. Patterson","doi":"10.36001/ijphm.2021.v12i2.2966","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i2.2966","url":null,"abstract":"Polymer jackets play an important protective role in distribution cabling by providing structure and resistance to moisture, heat, and exposure to harmful chemicals. Current methods of structural assessment, such as elongation at break (E-at-B), are inherently destructive. While other non-destructive methods such as indenter evaluation are available, they are not suitable for in-service use. We propose that second harmonic generation (SHG) could provide a non-destructive means of characterizing the aging of chlorosulfonated polyethylene (CSPE) cable jackets. SHG was used to study cables previously aged and characterized by the Electric Power Research Institute (EPRI). Comparative data between the SHG results and indenter modulus tests suggest that SHG can be used to qualitatively differentiate between minimally and significantly aged CSPE cable jackets. The results of this proof-of-concept study suggest additional work that could be done to better understand the mechanisms of the aging of CSPE cable jackets and how SHG could be used to monitor the aging process.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49408162","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}

{"title":"Special Issue on PHM for Human Health and Performance II","authors":"T. Lockhart, Rahul Soangra, Ijphmeditor","doi":"10.36001/ijphm.2021.v12i4.3089","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i4.3089","url":null,"abstract":"This special issue was conceived during the 11th Annual Conference of Prognostic and Health Management Society’s Panel session on the September 25th at Scottsdale, AZ, USA. We would like to thank the panel members and their colleagues in their participation in this special issue focusing on engineered technologies for older adults. This work was partially funded by the NSF ERC seed grant from an interdisciplinary group of researchers from Iowa State University, Arizona State University, Georgia Tech, Florida State University, Chapman University and the University of California Irvine who are engaged in developing a large-scale grant proposal that will be focused on integrated technologies to promote resilient aging and reducing healthcare costs.The manuscripts exemplify our research focus and illustrates contributions in the fields of wearable smart sensors, sensor-data-fusion, machine learning and data mining, prediction and diagnosis, and electronic health records and databases - all in the context of prognostics and health management for human health and performance.We would like to thank the PHM Society for providing an opportunity to publish in their premier journal, and importantly, we are grateful for help of the Editor-in-Chief – Marcos Orchard, Ph.D. for his countless hours to edit and make it best possible of this special issue. Finally, we would like to express sincere appreciation to all the reviewers who have contributed their time and thoughtful feedback to making this special issue publication a success.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41457439","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}

{"title":"Effects of Rucksack Military Accessory on Gait Dynamic Stability","authors":"S. Moon, Christopher W. Frames, Rahul Soangra, T. Lockhart","doi":"10.36001/ijphm.2021.v12i4.2778","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i4.2778","url":null,"abstract":"Various factors are responsible for injuries that occur in the U.S. Army soldiers. In particular, rucksack load carriage equipment influences the stability of the lower extremities and possibly affects gait balance. The objective of this investigation was to assess the gait and local dynamic stability of the lower extremity of five subjects as they performed a simulated rucksack march on a treadmill. The Motek Gait Real-time Interactive Laboratory (GRAIL) was utilized to replicate the environment of the rucksack march. The first walking trial was without a rucksack and the second set was executed with the All-Purpose Lightweight Individual Carrying Equipment (ALICE), an older version of the rucksack, and the third set was executed with the newer rucksack version, Modular Lightweight Load Carrying Equipment (MOLLE). In this experiment, the Inertial Measurement Unit (IMU) system, Dynaport was used to measure the ambulatory data of the subject. This experiment required subjects to walk continuously for 200 seconds with a 20kg rucksack, which simulates the real rucksack march training. To determine the dynamic stability of different load carriage and normal walking condition, Local Dynamic Stability (LDS) was calculated to quantify its stability. The results presented that comparing Maximum Lyapunov Exponent (LyE) of normal walking was significantly lower compared to ALICE (P=0.000007) and MOLLE (P=0.00003), however, between ALICE and MOLLE rucksack walking showed no significant difference (P=0.441). The five subjects showed significantly improved dynamic stability when walking without a rucksack in comparison with wearing the equipment. In conclusion, we discovered wearing a rucksack result in a significant (P < 0.0001) reduction in dynamic stability.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42481734","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}

{"title":"Long-term Resting Metabolic Rate Analysis in Pregnancy and Weight Loss Interventions","authors":"Shayok, Teresa Wu, E. Forzani, Corrie M. Whisner, David Jackemeyer","doi":"10.36001/ijphm.2021.v12i4.3077","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i4.3077","url":null,"abstract":"In this paper, we first studied the change in resting metabolic rate (RMR) of 4 women during their pregnancy period. We retrospectively analyzed published data, which lacked rigorous statistical analysis. We introduced new data that helps to define RMR baseline variabilities and further compare the RMR fluctuations in steady physiological conditions (no pregnancy, no weight/diet/exercise regime change) to assess “true” RMR changes that can guide healthy weight management in pregnancy and other conditions. For each subject, the change in the RMR values were computed as the difference between the values during the metabolic rate inspection period and the baseline values. This difference was compared against the difference values of a reference subject, using a two-sided paired t-test at the significance level of 5%. Our results indicated that some subjects exhibit a statistically significant increase, some exhibit a decrease while others show no significant statistical variation in RMR values during pregnancy. These are important findings that demystify the old idea that the RMR of a pregnant woman “always” increases since she is generating a new life; rather, individualized physiological processes can produce metabolic changes that cannot be generalized and need individual RMR measurements throughout pregnancy. The insights gained from this study were then applied to retrospectively analyze the RMR of 20 subjects during a 6-month pilot weight loss intervention with 89% efficiency in weight loss. Our analysis revealed that there was no significant decrease in metabolic activities at the end of the program. Although this contradicts the belief that weight loss is associated with a decrease in metabolic activities, our results can be explained by the fact that subjects adhered to a healthy nutritional diet and regular exercise during the pro- gram; thus, the effect of weight loss on decreasing the RMR was counter-balanced by the effect of healthier diet and exercise on increasing the RMR, which helped in maintaining a steady and healthy metabolic rate. Both studies, pregnancy and weight loss interventions indicated that changes in the metabolic rate of pregnant women and individuals undergoing weight loss interventions are unpredictable, therefore there is an urgent need to implement personalized practices of weight management by periodically measuring RMR and adjusting food caloric intakes based on the individual’s metabolic rate.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45905632","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}

{"title":"Predicting Fall Risk Through Automatic Wearable Monitoring","authors":"Markey C. Olson, T. Lockhart","doi":"10.36001/ijphm.2021.v12i4.2958","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i4.2958","url":null,"abstract":"Falls represent a major burden on elderly individuals and society as a whole. Technologies that are able to detect individuals at risk of fall before occurrence could help reduce this burden by targeting those individuals for rehabilitation to reduce risk of falls. Wearable technologies especially, which can continuously monitor aspects of gait, balance, vital signs, and other aspects of health known to be related to falls, may be useful and are in need of study. A systematic review was conducted in accordance with the Preferred Reporting Items for Systematics Reviews and Meta-Analysis (PRISMA) 2009 guidelines to identify articles related to the use of wearable sensors to predict fall risk. Fifty four studies were analyzed. The majority of studies (98.0%) utilized inertial measurement units (IMUs) located at the lower back (58.0%), sternum (28.0%), and shins (28.0%). Most assessments were conducted in a structured setting (67.3%) instead of with free-living data. Fall risk was calculated based on retrospective falls history (48.9%), prospective falls reporting (36.2%), or clinical scales (19.1%). Measures of the duration spent walking and standing during free-living monitoring, linear measures such as gait speed and step length, and nonlinear measures such as entropy correlate with fall risk, and machine learning methods can distinguish between falls. However, because many studies generating machine learning models did not list the exact factors being considered, it is difficult to compare these models directly. Few studies to date have utilized results to give feedback about fall risk to the patient or to supply treatment or lifestyle suggestions to prevent fall, though these are considered important by end users. Wearable technology demonstrates considerable promise in detecting subtle changes in biomarkers of gait and balance related to an increase in fall risk. However, more large-scale studies measuring increasing fall risk before first fall are needed, and exact biomarkers and machine learning methods used need to be shared to compare results and pursue the most promising fall risk measurements. There is a great need for devices measuring fall risk also to supply patients with information about their fall risk and strategies and treatments for prevention.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47336746","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}

{"title":"Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection","authors":"S. Sendlbeck, Alexander Fimpel, B. Siewerin, M. Otto, K. Stahl","doi":"10.36001/ijphm.2021.v12i2.3026","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i2.3026","url":null,"abstract":"Gear flank changes caused by wear do not only affect the dynamic behavior of gear systems, but they can also compromise the load-carrying capacity of gear teeth up to critical failure. To help avoid unintended consequences like downtime or safety risks, a condition monitoring system needs to be able to estimate the current wear during operation based on available sensor measurements. While many condition monitoring approaches in research rely on vibrational analysis with manual feature engineering, gearboxes running at slow speed do not reveal much excitation information for this purpose. We therefore introduce an approach for slow-speed gear wear monitoring that is based on the dynamic gear transmission error and that contains an automated feature selection process. For this purpose, we extract a large set of features from the preprocessed transmission error samples. Applying combined filter and embedded feature selection methods enables us to automatically identify and remove features with low relevance. The selection process consists of filtering features with no statistical dependence on the target wear value, removing redundant features with a correlation analysis and a recursive feature elimination process with cross-validation based on a random forest regressor. The remaining relevant set of features is the basis for model training and subsequent wear estimation. For this, the present research employed two independent ensemble models, random forest regression and gradient boosted regression trees. To train and test the proposed approach, we conducted slow-speed gear experiments with developing gear wear on a single-stage spur gear test rig setup. The results of both models show good gear wear estimation performance compared to the actual wear mass loss, even for small quantities. Hence, the proposed transmission error-based approach with automated feature selection is able to quantify the degree of slow-speed wear and offers a possible way for condition monitoring and fault diagnosis.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":"1 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70086136","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}

{"title":"Unsupervised Minimum Redundancy Maximum Relevance Feature Selection for Predictive Maintenance","authors":"V. Hamaide, F. Glineur","doi":"10.36001/ijphm.2021.v12i2.2955","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i2.2955","url":null,"abstract":"Identifying and selecting optimal prognostic health indicators in the context of predictive maintenance is essential to obtain a good model and make accurate predictions. Several metrics have been proposed in the past decade to quantify the relevance of those prognostic parameters. Other works have used the well-known minimum redundancy maximum relevance (mRMR) algorithm to select features that are both relevant and non-redundant. However, the relevance criterion is based on labelled machine malfunctions which are not always available in real life scenarios. In this paper, we develop a prognostic mRMR feature selection, an adaptation of the conventional mRMR algorithm, to a situation where class labels are a priori unknown, which we call unsupervised feature selection. In addition, this paper proposes new metrics for computing the relevance and compares different methods to estimate redundancy between features. We show that using unsupervised feature selection as well as adapting relevance metrics with the dynamic time warping algorithm help increase the effectiveness of the selection of health indicators for a rotating machine case study.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47329195","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}

{"title":"Assessment of Health Monitoring Trustworthiness of Avionics Systems","authors":"V. Ulansky, I. Machalin, I. Terentyeva","doi":"10.36001/ijphm.2021.v12i2.2907","DOIUrl":"https://doi.org/10.36001/ijphm.2021.v12i2.2907","url":null,"abstract":"The article provides a methodology for assessing the trustworthiness of health monitoring the dismounted avionics systems with automated test equipment (ATE). The indicators include the probabilities of false-positive, false-negative, true-positive, and true-negative. For the first time, we introduced into consideration the instability of the source of stimulus signal (SSS), the random and systematic component of the measuring channel error, and the reliability characteristics of the systems themselves. We consider a specific case of an exponential distribution of permanent failures and intermittent faults and derive formulas for calculating the trustworthiness indicators. Numerical calculations illustrate how the probabilities of correct and incorrect decisions depend on accuracy parameters. We show that the probabilities of false-positive and false-negative increase much faster than the probabilities of true-positive and true-negative decrease when the standard deviation of stimulus signal increases. For a Very High-Frequency Omni-Directional Range (VOR) receiver, we demonstrate that even with a zero random error generated by the source of the stimulus signal, the probabilities of false-positive and false-negative are different from zero.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":"26 1","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70085771","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}

{"title":"Requirements and Data Integrity Considerations for Diagnostics Testbeds","authors":"Ioannis Bardakis, Ioan-Octavian Niculita, P. Wallace","doi":"10.36001/IJPHM.2020.V11I2.2927","DOIUrl":"https://doi.org/10.36001/IJPHM.2020.V11I2.2927","url":null,"abstract":"The process of generating high quality data for the test and evaluation of diagnostic and prognostic algorithms is still of high importance to the Prognostics and Health Management (PHM) research community. To support these efforts a testbed has been designed, manufactured and commissioned. It has specifically been designed in order to replicate several component degradation faults with high accuracy and high repeatability. This paper documents the design, requirements and the data integrity elements of this benchmark hydraulic system. This document consolidates the process of designing diagnostics testbeds as at present there is a lack of literature on how diagnostics testbeds should be built and is intended to serve as a starting point and quick reference guide for engineers and researchers intending to design and develop a testbed to test and validate PHM applications. The first part of this paper highlights design requirements for all the design aspects for such testbeds with great consideration for industry standards and best practices covering the achievement of electromagnetic compatibility (EMC) and noise mitigation, as well as operators’ safety and equipment protection. The second part of the paper put great emphasis on data integrity elements of the data generated by this testbed (describing the system under healthy and faulty conditions) before it is actually used for system characterization or by diagnostics and prognostics algorithms.","PeriodicalId":42100,"journal":{"name":"International Journal of Prognostics and Health Management","volume":" ","pages":""},"PeriodicalIF":2.1,"publicationDate":"2021-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43557174","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}