Autonomous Health Management Research Articles

서비스업종에서의 안전리더십이 안전문화, 안전행동에 미치는 영향연구

According to recent statistics, the number of service workers in the industry exceeded 11,714,000 in 2017 to 13,825,000 in 2021, with about 4,217,000 in manufacturing and 1,651,000 in construction, the largest number of service workers in the industry. In addition, it is confirmed that the incidence of industrial accidents by size is the highest among those with less than 50 employees. The roadmap for reducing serious accidents recently announced by the Ministry of Employment and Labor aims to reduce industrial accidents through autonomous safety and health management of companies, such as the spread of safety awareness and safety culture within the organization. Based on previous studies that the role of workers in the organization is important for the organization's safety culture, and safety leadership affects organizational safety culture and safety behavior, this study attempted to confirm how safety leadership in the service sector affects safety culture and safety behavior. Safety leadership is an independent variable, and 206 workers in the service sector at workplaces with more than 50 employees and workplaces with less than 50 employees were examined to determine how it affects safety behavior, such as well as procedures, consciousness, and attitudes. A confirmatory factor analysis was conducted to secure compatibility and reliability of the tool, and the Cronbach's alpha value was derived. In addition, frequency analysis and descriptive statistics analysis were performed to understand the characteristics of the study subject and major variables, and Pearson correlation analysis was used to determine the correlation between major variables. The mediating effect was analyzed using the Process macro, and the significance of the mediating effect was confirmed through the Bootstrapping-test. The significance of all statistical results was based on 0.05. As a result of the study, safety leadership was found to have a forward effect on safety culture and safety behavior, but more than 50 people and less than 50 people showed different patterns. In other words, safety leadership directly affected safety culture and safety behavior in workplaces with more than 50 employees, but safety leadership directly affected safety culture in workplaces with less than 50 employees.Through the results of this study, the research and policy suggestions necessary in the future are as follows. First, a detailed study on the sub-factors of each variable is needed. Second, it is necessary to identify additional variables that affect the safety culture of the service industry by size and prepare measures to spread and improve the safety culture in institutional and step-by-step ways.

Interpretable Neural Network with Limited Weights for Constructing Simple and Explainable HI using SHM Data

Recently, companies all over the world have been focusing on the improvement of autonomous health management systems in order to enhance performance and reduce downtime costs. To achieve this, the remaining useful life predictions have been given remarkable attention. These predictions depend on the proper designing process and the quality of health indicators (HI) generated from structural health monitoring sensors based on prior established multiple prognostic evaluation criteria. Constructing such HIs from noisy sensory data demands powerful models that enable the automatic selection and fusion of features taken from those relevant measurements. Deep learning models are promising to autonomously extract features in scenarios with a huge volume of data without requiring considerable domain expertise. Nonetheless, the features established by artificial neural networks are complicated to comprehend and cannot be regarded as physical system characteristics. In this regard, the goal of this paper is to extend a new model; an interpretable artificial neural network that enables the automatic selection and fusion of features to construct the most appropriate HIs with remarkably fewer parameters. This model consists of additive and multiplicative layers that provide a feature fusion that better reflects the system’s physical properties. Additionally, the weights are discretized in two ways: a) using a ternary form with values {-1, 0, 1}, and b) relaxing the aforementioned ternary form by rounding the weights at the first decimal point in the range of [-1, 1]. Both discretization techniques have the ability to softly control the number of parameters that should be ignored. This trick guarantees interpretability for the neural network by extracting simple yet powerful equations representing the constructed HIs. Finally, the model’s performance is evaluated and compared with other approaches using a practical case study. The results show that the proposed approach's designed HIs are both interpretable and of high quality according to the criteria of the HI's evaluation

Research ON Satellites Fault Diagnosis Method Based On Artificial Intelligence

In order to enable satellites to use fault diagnosis, fault prediction, failure prediction and other data models to determine the health status of satellites autonomously, According to the statistics of satellite orbit faults, this paper gives an artificial intelligence-based satellite fault diagnosis method and compares the advantages and disadvantages of three different satellite fault diagnosis methods to ensure that when a satellite fault occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow The study ensures that when a satellite failure occurs, it can implement autonomous reconfiguration, redundancy switching, autonomous mode switching or downgrade use when its own conditions allow, ensuring the safety of satellite operation in orbit. Through the analysis of the application of typical fault diagnosis methods for a satellite control subsystem, it can be seen that artificial intelligence-based satellite fault diagnosis methods have gradually replaced traditional methods and become the core key technology for the future application of autonomous satellite health management system in orbit, which is also one of the core functions that future software-defined satellites should have.

State-of-Health Prediction for Reaction Wheel of On-Orbit Satellite Based on Fourier Broad Learning System

The state-of-health (SoH) of the reaction wheel (RW) is a key indicator of the satellite’s ability to function properly and complete its mission successfully. Quick and accurate prediction of the RW’s SoH is an important guarantee of autonomous satellite mission execution and health management. In view of the pseudoperiodicity and strong noise characteristics of satellite on-orbit telemetry data, this paper aims to design a prediction framework meeting the satellite state on-orbit prediction requirements, which mainly includes data reconstruction, similar data screening, self-revised prediction and the fuzzy expression of results. First, a new method for RW health characterization is proposed for actual telemetry data from satellites, and a completely new set of historical samples is constructed. Second, a fusion deviation measure is proposed by selecting samples from the historical sample set that are similar to the sample to be predicted. Third, a Fourier Broad Learning System (FBLS) is proposed to improve the depiction accuracy of a standard Broad Learning System (BLS) in learning satellite telemetry data with less computational and time resources. Finally, a fuzzy expression is proposed to obtain the prediction results. The proposed method is validated with real satellite on-orbit telemetry data. The case study results show that the SoH of the RW can be predicted quickly and accurately, which demonstrates the promising performance of our method.

Artificial Intelligence System for College Students’ Physical Fitness and Health Management Based on Physical Measurement Big Data

Most of the current health management products are used in medical institutions and generally do not pay enough attention to the student population. Based on this, this paper designs a student‐oriented and functional autonomous health management system. This paper proposes a personal health management system based on a multidimensional data model based on the main social characteristics of the population with chronic diseases and the actual needs of personal health management for chronic diseases. The value of various health data for health management is deeply analyzed and mined, and a multidimensional model data warehouse is constructed according to relevant national health data standards to create a standard data platform for intelligent health warning and disease risk assessment. This paper researches and designs a closed‐loop personal health management method based on the Plan‐Do‐Check‐Action (PDCA) cycle management model, with detailed functional design in four aspects: health data collection and recording, health assessment, health planning, and tracking and execution. This paper researches health data collection, processing, and storage technologies and adopts HDFS data storage technology, html, css, Java Script, java, and other software development technologies, combined with j Query, UEditor, Date Range Picker, and other plug‐ins, as well as SMS email generation interface, wireless Bluetooth transmission interface, etc. This system web and mobile application platforms are designed and developed. Relational database is used as the system database, and a snowflake‐type multidimensional data model is designed. Finally, the functions and performance of this system were tested, and the development and trial run of the basic version have been completed.

RUL prediction for automatic machines: a mixed edge-cloud solution based on model-of-signals and particle filtering techniques

This work aims to provide useful insights into the course of action and the challenges faced by machine manufacturers when dealing with the actual application of Prognostics and Health Management procedures in industrial environments. Taking into account the computing capabilities and connectivity of the hardware available for smart manufacturing, we propose a particular solution that allows meeting one of the essential requirements of intelligent production processes, i.e., autonomous health management. Indeed, efficient and fast algorithms, that does not require a high computational cost and can be appropriately performed on machine controllers, i.e., on edge, are combined with others, which can handle large amounts of data and calculations, executed on remote powerful supervisory platforms, i.e., on the cloud. In detail, new condition monitoring algorithms based on Model-of-Signals techniques are developed and implemented on local controllers to process the raw sensor readings and extract meaningful and compact features, according to System Identification rules and guidelines. These results are then transmitted to remote supervisors, where Particle Filters are exploited to model components degradation and predict their Remaining Useful Life. Practitioners can use this information to optimise production planning and maintenance policies. The proposed architecture allows keeping the communication traffic between edge and cloud in the nowadays affordable “Big data” range, preventing the unmanageable “Huge data” scenario that would follow from the transmission of raw sensor data. Furthermore, the robustness and effectiveness of the proposed method are tested considering a meaningful benchmark, the PRONOSTIA dataset, allowing reproducibility and comparison with other approaches.

A novel method for solar panel temperature determination based on a wavelet neural network and Hammerstein-Wiener model

Accurate prediction of solar panel temperature can help keep on-orbit satellites in good condition. Traditional physical models have the ability to describe and predict temperature; however, the effect is not entirely satisfactory. To produce a better forecast, the panel current of the solar panel, which is strongly correlated with temperature signals, is chosen as the input, and a novel system identification model between the two signals is established. The model we propose is based on a Hammerstein-Wiener model and integrates wavelet neural networks that adopt self-constructed wavelet bases. In addition, a complete process of training and parameter optimization is designed to be less time consuming than previous methods. The result from a test set of real telemetry data demonstrates the efficiency and accuracy of our method. Moreover, the proposed prediction model, which is based on historical data, can be used in on board self-learning and our subsequent autonomous health management.

An automated health indicator construction methodology for prognostics based on multi-criteria optimization

In recent years, the development of autonomous health management systems received increasing attention from worldwide companies to improve their performances and avoid downtime losses. This can be done, in the first step, by constructing powerful health indicators (HI) from intelligent sensors for system monitoring and for making maintenance decisions. In this context, this paper aims to develop a new methodology that allows automatically choosing the pertinent measurements among various sources and also handling raw data from high-frequency sensors to extract the useful low-level features. Then, it combines these features to create the most appropriate HI following the previously defined multiple evaluation criteria. Thanks to the flexibility of the genetic programming, the proposed methodology does not require any expertise knowledge about system degradation trends but allows easily integrating this information if available. Its performance is then verified on two real application case studies. In addition, an insightful overview on HI evaluation criteria is also discussed in this paper.

Autonomous health management design for lithium-ion battery in middle and high orbit satellites

Autonomous health management design for lithium-ion battery in middle and high orbit satellites

The Research of Autonomous In-orbit Health Management System for China’s Manned Spacecrafts

The development trend of manned spacecraft is long-term in-orbit flight and long-term astronaut’s residency. Under the condition of limited TT&C resource, the autonomous health management of spacecraft is a critical and mandatory system to ensure the safety of the astronauts and platform. Based on the discussion of the autonomous health management objectives and management modes of manned spacecraft and combining the distribution and hierarchical information structure of China’s manned spacecraft, this paper elaborates an autonomous health management system. This autonomous health management system adopts a by-level safety threshold management method by establishing a mapping database between different important risk safety thresholds and system safety modes. When the key parameters reach different pre-set thresholds, the system autonomously downgrades and reconstructs the aircraft functionalities in the event of failure to realize optimal auto-processing and solves the issue between randomness of auto-processing and task continuity. The research has been applied to the China’s Space Station.

Effects of a service dog on social participation of a C5 complete spinal cord injury patient: A case report

Patients with complete high cervical spinal cord injury have significantly restricted social participation even with the use of various prosthetic devices. We describe a patient with a C5 complete injury who showed remarkable improvements in social participation after obtaining a service dog. During March X-8, a 48-year-old man experienced a C5 complete spinal cord injury and underwent anterior cervical fusion. In January X-7, he underwent cystostomy. He discharged from the hospital in September with Zancolli classification: C5A and American Spinal Injury Association Classification: A. Since living with his service dog, he has been giving lectures about his experiences and opinions regarding physical disabilities and barrier-free-concepts at medical institutions and schools. In October X-2, he successfully flew from Ishikawa Prefecture to Tokyo with his service dog. He received the Ishikawa Prefectural Barrier-free Governor Prize. In July X, a semi-structured interview was conducted using the International Classification of Functioning, Disability, and Health (ICF) Rehabilitation Set. The interview and analysis were performed after obtaining written consent. The ethics committee of our institution permitted this study. There are no conflicts of interest. Using a service dog positively changed the man's health condition as defined by ICF. The following outcomes were observed: ability to take initiative and focus on actions, increased motivation, clearer planning for specific care needs, physical improvement, increased resilience, increased physical movements, emergence of new roles, better ability to resolve emotional conflict, improved autonomous health management, locomotion with a service dog, communication facilitation, acquisition of stress coping measures, living with consideration of other people, acquiring a job, employment, experiencing positive emotions, reduction of psychological barrier, increased social support and empathy due to visualization of disability, assistance from public service providers ( Fig. 1 ). Service dogs can encourage social participation, even for those with a complete high cervical spinal cord injury.

Integrating a mobile health applications for self-management to enhance Telecare system

A dramatic global increase in the pervasiveness of chronic illness has coincided with a reduction in the availability of healthcare resources, coupled with increased costs for care, forcing a fundamental reevaluation of care processes. These trends have driven interest in the use of advanced healthcare information systems and telematics applications to improve care availability while reducing overall costs, but such measures require taking an integrated approach to a range of interrelated social, economic, political and cultural impacts and challenges. Telecare technologies allow hospitals to continuously monitor biomedical indicators, while providing patients with online services such as clinical appointment scheduling, medical consulting, remote alerts, etc. Telecare has the potential to transform the healthcare industry by reducing costs, increasing quality, and enhancing patient satisfaction. The development of a real-time monitoring healthcare service model through the integration of information and communications technologies (ICT) has emerged as a research priority. This study explores the design, value creation, development and evaluation of Telecare systems and mobile health applications for autonomous health management to ensure appropriate home-based health monitoring and treatment while improving care performance. A well-defined methodology is needed to develop artifacts due to increasing disease complexity. This study develops an Android-based self-management application based on design science research methodology. The App assists users in collecting and monitoring indicators to prompt appropriate care services. This study uses individual home self-care as the basic Telecare unit to design a service model integrating six kinds of healthcare services. Usability testing is conducted to reflect five constructs: system usefulness, ease of learning, information quality, interface quality, and overall satisfaction. Experimental results support previous research findings regarding the Chronic Care Model and enhance the effectiveness of mobile-based services. Our work provides a useful reference to researchers and practitioners interested in understanding how hospitals can better facilitate more effective mobile-based technology adoption in today’s e-health environment.

Autonomous health management for PMSM rail vehicles through demagnetization monitoring and prognosis control

Autonomous vehicles are playing an increasingly importance in support of a wide variety of critical events. This paper presents a novel autonomous health management scheme on rail vehicles driven by permanent magnet synchronous motors (PMSMs). Firstly, the PMSMs are modeled based on first principle to deduce the initial profile of pneumatic braking (p-braking) force, then which is utilized for real-time demagnetization monitoring and degradation prognosis through similarity-based theory and generate prognosis-enhanced p-braking force strategy for final optimal control. A case study is conducted to demonstrate the feasibility and benefit of using the real-time prognostics and health management (PHM) information in vehicle ‘drive-brake’ control automatically. The results show that accurate demagnetization monitoring, degradation prognosis, and real-time capability for control optimization can be obtained, which can effectively relieve brake shoe wear.

Autonomous Prognostics and Health Management (APHM)

The objective of this paper is to show how PHM con-cepts can be included in the design of an autonomous Un-manned Air Vehicle (UAV) and in doing so, provide effec-tive diagnostic/prognostic capabilities during system opera-tion. The authors propose a PHM Cycle that is divided into two parts, covering the design of the Autonomous PHM system and the operation of the PHM system in real-time application. The paper presents steps in design of Autono-mous Prognostics and Health Management (APHM) devel-oped using the above approach, to provide contingency management integrated with autonomous decision-making for power management on a UAV. APHM was developed using commercial software tools such as the JACK® auton-omous software platform to provide real-time intelligent decision making and MADe® - Maintenance Aware Design environment to identify risks due to equipment failures and to select appropriate sensor coverage. The PHM Cycle methodology is demonstrated in application to autonomous, real-time, engine health and power management on an Un-manned Air Vehicle (UAV).

Transforming the operations paradigm of space exploration

The international space community has become comfortable over the years with a model of operations based on verbal delegation of operations control from a mission control center based on Earth. This model requires near-constant telemetry regarding the status of the vehicle, as well as tasks on-board. The future long-duration exploration missions being considered will require significant changes in this operational paradigm, adjusting to situational realities, capitalizing on the evolution that has occurred in vehicle autonomous health management, and maximizing the time crew members can devote to exploration.

Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance

This article describes a novel scheme for autonomous component health management (ACHM) with failed actuator detection and identification and failed sensor detection, identification, and avoidance. This new scheme has features that are very superior to those with triple redundant sensing and voting, yet requires fewer sensors; it can be applied to any system with redundant sensing. Relevant background to the ACHM scheme is provided in this article. Simulation results for its application to a single-axis spacecraft attitude control system with a third-order plant and dual-redundant measurements of the system states are presented. The ACHM scheme fulfills key functions needed by an integrated vehicle health monitoring (IVHM) system. It is autonomous; is adaptive; works in real time; provides optimal state estimation; identifies failed components; avoids failed components; reconfigures for multiple failures, reconfigures for intermittent failures; works for hard-over, soft, and zero-output failures; and works for both open- and closed-loop systems. The ACHM scheme combines a prefilter that generates preliminary estimates of the system states, detects and identifies failed sensors and actuators, and avoids failed sensors in generating preliminary estimates of the system states with a fixed-gain Kalman filter that provides model-based state estimates, which are utilized in the failure detection logic, and generates optimal estimates for the system states. The simulation results show that ACHM can successfully detect, identify, and avoid sensor failures that are single or multiple; persistent and intermittent; and hard-over, soft, and zero-output types. It is now ready to be tested on a computer model of an actual system.