Integrated System Health Management Research Articles

Development and Application of a Smart Mattress and Integrated Health Management System for Real-time Monitoring Utilizing Theory of Inventive Problem Solving and Signal Processing Techniques

Development and Application of a Smart Mattress and Integrated Health Management System for Real-time Monitoring Utilizing Theory of Inventive Problem Solving and Signal Processing Techniques

Tech driven wellness: centralized integrated health management system in UAE

Tech driven wellness: centralized integrated health management system in UAE

Health Management of Aircraft Fuel Systems: A Practical Prognostic Perspective

Aircraft health management, specifically pertaining to fuel systems, is a comprehensive process that is undertaken to ensure the continuous airworthiness of the aircraft over its entire lifespan. The integration of health management into aircraft systems requires a methodical strategy to quantitatively evaluate the possible advantages provided by each specific application. Gaining a thorough comprehension of the advantages linked to integrated health management systems provides useful insights for enhancing the design of these systems and formulating efficient prognostic techniques. The role of prognosis in the field of prognostics and health management (PHM) is essential for improving the dependability of systems. Prognosis involves accurately predicting the remaining useful life (RUL) within the domain of integrated health management. This study improves the precision of prediction results, optimises the derived prerequisites for health management in the specialised field of RUL prediction for aircraft fuel systems, provides a thorough understanding, and advances the grasp of the previously described requirements in a more effective way. Additionally, it presents the most suitable solutions in order to address these demands by utilising prognostic techniques. Furthermore, this study presents an analysis of the proposed hybrid prognostic approach in the setting of controlled laboratory conditions. The applications encompassed in this work consist of three main areas. Firstly, the evaluation of inspection interval modifications is conducted by taking into account load and environmental monitoring data. Secondly, design considerations for damage prognostics are incorporated into the analysis. Lastly, a design modification is presented as an example of how condition-based maintenance (CBM) can be facilitated. A methodology that enables the efficient and well-founded construction of a health management system, specifically designed for aircraft fuel systems, is presented. This methodology, together with its associated outcomes, presents a compelling advantage in terms of lowering life cycle expenses while simultaneously enhancing availability, dependability, and performance.

Incidence of Scrub Typhus according to Changes in Geographic and Demographic Characteristic in the Chungcheong Region of Korea.

To ascertain the incidence trends of scrub typhus in the Chungcheong region, we analyzed the epidemiological survey information of 14,379 cases of scrub typhus reported to the integrated disease health management system of the Korea Centers for Disease Control and Prevention between 2012 and 2022, along with demographic data from the Korean Statistics Information Service. Geographical analyses were performed to confirm the correlation between high-risk areas and the proportion of elderly people. The average age, proportion of elderly people, and changes in the agricultural population were statistically associated with incidence. The incidence of scrub typhus, and the agricultural population, in the Chungcheong region has decreased compared with that in 2012-2013. However, recent trends indicate a resurgence linked to increased outdoor activity, with higher risks observed in older age groups. Additionally, advancing age correlates with a heightened probability of reinfection and additional infections with other febrile diseases. The incidence of scrub typhus in the Chungcheong region (2012-2022) is changing according to age and route of infection, highlighting the need for revised prevention and promotion policies.

Launch Vehicle Integrated Health Management System Model

Launch Vehicle Integrated Health Management System Model

Analysis of Clinical Characteristics of Tuberculosis Patients with Dementia in Gyeongsangbuk-do, Republic of Korea.

(1) Background: Among Korean research papers there have been studies on the correlation between tuberculosis-hypertension and diabetes and the correlation between dementia-hypertension and diabetes, but there were no analysis data specifically on tuberculosis and dementia. (2) Methods: A total of 2992 tuberculosis patients in the Gyeongbuk region were analyzed through a final analysis of integrated disease and health management system data collected from 2021 to 2022. In this selection, patients with tuberculosis under 50 years of age and 368 people diagnosed with tuberculosis were excluded. (3) Results: From 2021 to 2022, among the 2992 tuberculosis patients in Gyeongsangbuk-do aged 50 or older, 2722 (91.0%) belonged to the general tuberculosis patient group, while 270 (9.0%) belonged to the dementia-tuberculosis patient group. The average age in the dementia-tuberculosis group was 81.4 years, significantly higher than the general group's average of 75.7 years. Within the dementia-tuberculosis patient group, 235 patients (87.0%) had underlying medical conditions in addition to dementia and tuberculosis. The tuberculosis treatment cure rate was 56.3% (1477 patients) in the general group and 38.9% (105 patients) in the dementia-tuberculosis patient group. (4) Conclusions: The cure rate was notably higher in the general group. Similarly, the mortality rate (deaths due to tuberculosis) was significantly higher in the dementia-tuberculosis patient group (7.0%, 19 patients) compared to the normal group (3.0%, 81 patients). The mortality rate in the dementia group was more than twice that of the general group.

Prevalence of mental disorders among middle-aged population of primary healthcare centers in Northeastern Iran

BackgroundPrimary healthcare centers (PHCs) serve as the cornerstone of accessible medical services in society, playing a crucial role in screening, detecting, and treating various health issues. This study aimed to investigate the prevalence of psychiatric disorders in middle-aged individuals who refer to PHCs and the potential of PHCs in diagnosing mental disorders.MethodsThis cross-sectional study was implemented at PHCs under the supervision of Mashhad University of Medical Sciences (MUMS) in northeast Iran in 2018. The enrolled subjects were middle-aged adults who had electronic medical records in SINA, an integrated health management system, and the electronic medical records of MUMS. The prevalence of psychiatric disorders by type and their relationship with demographic information was evaluated by a Chi-square test using SPSS 22.ResultsThis study involved 218,341 middle-aged participants. Prevalence of psychiatric disorders was 8.59%, and depression (53.72%) and anxiety (42.02%) were the most common psychiatric disorders in both males and females. The prevalence of mental disorders was significantly higher in females than in males (88.18% vs. 18.81%; P < 0.0001). Indeed, a significant higher prevalence of depression, anxiety, somatoform, childhood psychiatric disorder, and bipolar disorders was observed in females compared to males (P < 0.05). In addition, individuals between the age of 45–60 years, and those from rural areas showed more prevalence of mental disorders than others, but these differences were not significant.ConclusionsConsidering the previous studies in Iran, the prevalence of mental disorders among patients presenting to PHCs was noticeably lower than expected rates. It seems probable that this huge difference is due to poor screening and detection of mental illness in PHCs of MUMS. It is recommended that health policymakers pursue specific measures to make PHCs more helpful for people with mental health problems in the community.

PENGELOLAAN DAN KESEHATAN LINGKUNGAN DI SEKOLAH BERASRAMA SMAN CAHAYA MADANI BANTEN BOARDING SCHOOL, KABUPATEN PANDEGLANG, BANTEN

Environmental health is a very important element in relation to the potential for the spread of disease, especially in the era of the Covid-19 pandemic, this is happen when environmental conditions are not feasible. Through good environmental management accompanied by regular and continuous education for the residents of this limited environment, boarding school can become an example of the application of good environmental health management. Through this outreach activity, the Community Service Program of the Environmental Engineering and Landscape Architecture Study Program aims to educate the academic community of SMAN CMBBS in order to increase awareness in meeting proper basic facilities and implementing an integrated environmental health pattern by implementing clean and healthy living behaviors. The method used is a counseling of educational materials and posters through online seminars using video conference. For now, as many as 51.9% of seminar respondents do not have the readiness to restart offline learning process because there is no proper preparation from the school regarding an integrated environmental health management system in preventing the formation of a pandemic cluster. The implications of this PkM activity are very useful in increasing insight and awareness of the CMBBS academic community in facing the offline learning process.

Increasing demand for point-of-care testing and the potential to incorporate the Internet of medical things in an integrated health management system.

As the number of people with COVID-19 increases daily around the world, point-of-care testing (POCT) is gaining attention as a tool that can provide immediate test results and greatly help to deter infection and determine what to do next. POCT has several drawbacks such as a low sensitivity and specificity, but according to studies POCT has increased sensitivity on par with that of polymerase chain reaction testing. The advantage of POCT is that the results can be obtained quickly, regardless of the location. To further enhance its benefits, POCT is being developed and researched in conjunction with the Internet of medical things (IoMT), which allows POCT results to be collected, recorded, and managed over a network. IoMT will be beneficial not only for the use of POCT simply as a testing tool but also for its integration into diagnostic and health management systems. IoMT will enable people to regularly receive their test results in their daily lives and to provide personalized diagnosis and treatment of individual conditions, which will be beneficial in terms of disease prevention and maintenance of health.

ISHM-Oriented Time Decision-Making for Condition-Based Maintenance of Multistate Systems

In this paper, an integrated system health management oriented condition monitoring and maintenance decision-making model is proposed, which uses intelligent mobile agents to achieve real-time monitoring and to realize life cycle maintenance decision-making for multistate systems (MSSs). The model is applied to an aircraft engine system numerical example, the results of which illustrate that the proposed system and model have the ability to provide efficient and accurate engine system maintenance decision-making information and provide detailed health condition information. The feasibility of the proposed system and model is demonstrated and MSS frequent maintenance decisions are recorded and revealed.

Integrated Health Management System Approach

This paper aims to develop an integrated shipboard condition prognostics system that integrates sensing, feature extraction, and particle filtering-based diagnostic and prognostic algorithms with applications to bearing systems. The proposed effort aims to provide effective assessment of the condition of shipboard rotating machinery systems and lower the operation and maintenance (O&amp;M) cost. The proposed work is tested on data of various fault modes, models with multiple interactive faults, and experimental testbed as a whole system. The proposed condition prognostics system is scalable, generic, easy-to-implement, and mathematically rigorous, which can be applied to a variety of Navy applications.

Diagnosis and prognosis of slow speed bearing behavior under grease starvation condition

The monitoring and diagnosis of rolling element bearings with acoustic emission and vibration measurements has evolved as one of the much used techniques for condition monitoring and diagnosis of rotating machinery. Furthermore, recent developments indicate the drive toward integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this article is an experimental study of slow speed bearings in a starved lubricated contact. It investigates the influence of grease starvation conditions on detection and monitoring natural defect initiation and propagation using acoustic emission approach. The experiments are also aimed at a comparison of results acquired by acoustic emission and vibration diagnosis on full-scale axial bearing. In addition to this, the article concentrates on the estimation of the remaining useful life for bearings while in operation. To implement this, a multilayer artificial neural network model has been proposed to correlate the selected acoustic emission features with corresponding bearing wear throughout laboratory experiments. Experiments confirm that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

Estimation of Remaining Useful Life of Slow Speed Bearings Using Acoustic Emission Signals

Various direct and indirect sensing methods for machine condition monitoring have been reported in the literature. Among these methods, acoustic emission technique is one of the effective means of monitoring rolling element bearings during industrial processes. Today, many machines use computerized classification in a wide range of applications. Further, recent developments indicate the drive towards integration of diagnosis and prognosis algorithms in future integrated machine health management systems. With this in mind, this paper concentrates on the estimation of the remaining useful life for bearings whilst in operation. To implement this, a linear regression classifier and multilayer artificial neural network model have been proposed to correlate the selected AE features with corresponding bearing wear throughout laboratory experiments. Results showed that the proposed models exhibit good prediction performance. This paper also presents the use of a new representative fault indicator, signal intensity estimator, employed for AE signals originating from natural degradation of slow speed rolling element bearings. It is concluded that the obtained results were promising and selecting this appropriate signal processing technique can significantly affect the defect identification.

A Multi-Agent Solution to Distributed Fault Diagnosis of Preheater Cement Cyclone

Systems health monitoring is essential to guaranteeing the safe, efficient, and reliable operation of engineering systems. Integrated systems health management methodologies include fault diagnosis mechanism. Diagnosis involves detecting when a fault has occurred, isolating the true fault, and identifying the true damage to the system. This important issue is even harder when the systems to be diagnosed are dynamic and spatially distributed systems with their successively increasing complexity. For such systems, a single diagnostic entity having a model of the whole system approach is inappropriate. Whereas a distributed approach of multiple diagnostic agents can offer a solution. An overall systematic solution for these issues could be obtained by an artificial intelligent mechanism called the multi-agent system (MAS). This paper presents a MAS model for fault diagnosis based on logical theory of diagnosis. In this approach, each local diagnostic agent has knowledge above its subsystem and an abstract view of the neighboring subsystems and it is able to determine the local minimal diagnoses that are consistent with global diagnoses. The multi-agent models are simulated in Java Agent Development Framework and are applied to the preheated cement cyclone in the workshop of SCIMAT clinker.

Prognostics: a literature review

Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

D-matrix Based Fault Modeling for Cryogenic Loading Systems

&#x0D; &#x0D; &#x0D; The study is motivated by NASA plans to develop technology for an autonomous cryogenic loading operation including online fault diagnostics as a part of Integrated Health Management system. For years, the diagnostic modeling effort is performed in many paradigms. None of these paradigms independently can provide a complete set of efficiency metrics: better diagnostics, lower run-time, etc. D-matrix, a causal 0-1 relationship between faults and tests, is proposed as a single representation between different model-based diagnostic methods for comparison and communication. This framework is suitable to create a common platform for communication via D-matrix for systems engineering process. The knowledge transfer between modeling techniques is done via D-matrix. In addition, D-matrix provides a common paradigm to compare the embedded knowledge and performance of heterogeneous diagnostic systems. D-matrix is generated from physics models to be used with faster run-time performance D-matrix based diagnostic algorithms. Additionally, we will also investigate if the derived D- matrix and thereby the physics model is sufficient and accurate for efficient diagnostics via iDME tool.&#x0D; &#x0D; &#x0D;

Developing Prognostic Models Using Duality Principles for DC-to-DC Converters

Within the field of Integrated System Health Management, there is still a lack of technological approaches suitable for the creation of adequate prognostic model for large applications whereby a number of similar or even identical subsystems and components are used. Existing similarity among a number of different systems, which are comprised of similar components but with different topologies, can be employed to assign the prognostics of one system to other systems using an inference engine. In the process of developing prognostics, this approach will thereby save resources and time. This paper presents a radically novel approach for building prognostic models based on system similarity in cases where duality principle in electrical systems is utilized. In this regard, unified damage model is created based on standard Tee/Pi models, prognostics model based on transfer functions, and remaining useful life (RUL) estimator based on how energy relaxation time of system is changed due to degradation. An advantage is that the prognostic model can be generalized such that a new system could be developed on the basis and principles of the prognostic model of other systems. Simple electronic circuits, dc-to-dc converters, are to be used as an experiment to exemplify the potential success of the proposed technique validated with prognostics models from particle filter.

Integrated System Health Management-based Fuzzy On-board Condition Prediction for Manned Spacecraft Avionics

The purpose of this study is to develop a prediction methodology for a condition assessment of on-board integrated health management systems in manned spacecraft avionics. The framework of this study is based on two main premises. The first is the need to examine the problems in the on-board prediction tools in space avionics integrated system health management, an area that has been rarely focused on. The second is the need to consider the failure correlation coefficients to enable accurate health predictions. To deal with the uncertainty in the prediction process, a fuzzy theory–gray model–support vector machine approach, which uses fuzzy theory combined with a gray model and a support vector machine, is used to make the prediction. An example is given to demonstrate the accuracy and reliability of the model. Copyright © 2014 John Wiley & Sons, Ltd.

Improved reliability-based decision support methodology applicable in system-level failure diagnosis and prognosis

Reliability modeling and troubleshooting reasoning involving complex component interactions in complex systems are an active research topic and a critical challenge to be overcome in decision support. In this paper, we propose an innovative concept of decision support methodology for system failure diagnosis and prognosis in complex systems. Advanced causal structure, incorporating domain and engineering knowledge, and a new Bayesian network (BN) representation of system structure and component interaction are proposed. Based on the BN representation, a Bayesian framework is developed to analyze and fuse the multisource information from different hierarchical levels of a system. This capability supports higher-fidelity modeling and assessing of the reliability of the components, the subsystems, and the system as a whole. The feasibility of our advanced causal structure approach has been proven with implementation using test data acquired from electromechanical actuator systems. A case study is successfully conducted to demonstrate the effectiveness of the proposed methodology. The proposed decision support process in integrated system health management will enable enhancements in flight safety and condition-based maintenance by increasing availability and mission effectiveness while reducing maintenance costs.

Data mining–based intelligent fault diagnostics for integrated system health management to avionics

Space avionics are the essential capabilities of a spacecraft that guarantee space flight safety and mission success. One of the most important elements developed to deal with the health of the space avionics is the integrated system health management. Fault diagnostics, a safety-critical process in the integrated system health management, has become more complex as the number of avionics systems within the spacecraft has grown, so failure data are now multidimensional, often incomplete, and have cumulatively acquired uncertainties. Therefore, an accurate fault diagnostics model is needed to handle these types of data and ensure information is adequately adapted and efficiently updated. To date, there has been little research focused on efficient and effective space avionics fault diagnostics. This article presents a novel integrated system health management–oriented intelligent diagnostics methodology based on data mining. A numerical example is provided to illustrate the methodology, which demonstrates the significant benefits of data mining for the efficient processing of massive, incomplete data, and the ability of using a robust diagnostic Bayesian network to identify faults with uncertainty in a dynamic environment. The combined approach shows how some limitations can be overcome with an improved diagnostic performance. For application, sensory information must initially be discretized to Boolean values. Data mining is then used to mine for useful association rules and to learn the dynamic Bayesian network structure. After parameter training, the diagnostics is conducted. This methodology can be applied to systems of varying sizes and is flexible enough to accommodate other efficient diagnostic methods.