Health Management Technology Research Articles

Technologized Talk

In this study, the authors examine patient use of and feelings about wearable technologies for health attainment and management. Based on an online survey of 81 patients using wearable technologies to track and manage health, as well as interviews with three patients utilizing wearables for health management, the authors examine how wearable technologies are being used by patients to attain health, manage health, and/or prevent health issues, and what value users find in these wearable technologies. The authors also examine how such use is impacting communication between medical professionals and patients. Specifically, the authors explore how the inclusion of wearable technologies has changed the “rhetorical relationship” between patients and medical professionals. The study concludes with a discussion of the future of wearable devices in patient-medical practitioner relationships and clinical settings.

Research on Occupational Safety, Health Management and Risk Control Technology in Coal Mines.

This paper studies the occupational safety and health management methods as well as risk control technology associated with the coal mining industry, including daily management of occupational safety and health, identification and assessment of risks, early warning and dynamic monitoring of risks, etc.; also, a B/S mode software (Geting Coal Mine, Jining, Shandong, China), i.e., Coal Mine Occupational Safety and Health Management and Risk Control System, is developed to attain the aforementioned objectives, namely promoting the coal mine occupational safety and health management based on early warning and dynamic monitoring of risks. Furthermore, the practical effectiveness and the associated pattern for applying this software package to coal mining is analyzed. The study indicates that the presently developed coal mine occupational safety and health management and risk control technology and the associated software can support the occupational safety and health management efforts in coal mines in a standardized and effective manner. It can also control the accident risks scientifically and effectively; its effective implementation can further improve the coal mine occupational safety and health management mechanism, and further enhance the risk management approaches. Besides, its implementation indicates that the occupational safety and health management and risk control technology has been established based on a benign cycle involving dynamic feedback and scientific development, which can provide a reliable assurance to the safe operation of coal mines.

Modelowanie utrzymania ruchu i gotowości systemu kolejowego za pomocą kolorowych sieci Petriego

Prognostics and health management (PHM) technologies permit actionable information to enable proper decision-making for improving systems’ performance. With the increasing requirements placed on the rail systems’ availability, better maintenance decisions should be evaluated before practical application. The aim of this work is to build maintenance models and estimate the performance of considered maintenance decisions regarding the rail system’s reliability and availability by means of Colored Petri nets. As a high-level formalization method, Colored Petri nets provide different color sets, which are suitable to represent different maintenance attributions. The maintenance models are evaluated at both the structure and parameterization levels. At the structure level, the structure correctness of the maintenance models is evaluated by using the state space analysis. At the parameterization level, specific maintenance decisions are illustrated. With various maintenance parameters, comparisons of system reliability and availability are made with the results obtained with the Colored Petri nets model.

Failure Data Acquisition System Based on WSN for Vehicle PHM Technology

Prognostic and health management (PHM) technology needs plenty of historical failure data to build accurate model for failure analysis, diagnostic and prognostic. However the existing data collection equipment is not flexible enough for special vehicle because of the wired connection or lack of real-time information feedback in real vehicle experiment. A new failure data acquisition system based on wireless sensor network is proposed to acquire the sufficient historical failure data for the development of vehicle PHM technology. The proposed system architecture consists of several wireless failure data acquisition (WFDA) nodes, a gateway node, monitoring software and probability density ratio (PDR) algorithm working on a base station. Compared with other related acquisition systems, the WFDA node is small enough and suitable for working in a narrow space inside the vehicle. A double-buffer resampling strategy is specifically developed in this node to solve the contradiction of high sampling rate and low wireless bandwidth. The PDR algorithm embedded in monitoring software is used to detect abnormal data and show researchers the analysis results which can be relied to change the test item in time. Experiments results in the laboratory preliminary verified the effectiveness of system.

Application of deterministic resampling particle filter to fatigue prognosis

The method based on a particle filter for a fatigue crack growth prognosis has proved to be a powerful and effective tool for developing prognostics and health management (PHM) technology. However, the widely used basic particle filter have the unavoidable particle impoverishment problem, which will make particles unable to approximate the true posterior probability density function of the system state and lead to a prognosis result with a large error. This paper proposes a fatigue crack growth prognosis method based on a deterministic resampling particle filter. The active structural health monitoring based on the Lamb wave is used for on-line crack length monitoring with piezoelectric transducers. With the on-line crack measurement, the crack state and crack growth model parameters are estimated for a fatigue crack growth prognosis. In addition, the deterministic resampling procedure is employed to overcome the particle impoverishment problem. The result shows the proposed crack growth prognosis method based on deterministic resampling particle filter can provide more satisfactory results than the basic particle filter.

Inter-rater and test-retest reliability of the Danish version of the everyday technology use questionnaire

Background: Everyday technologies are naturally integrated in people’s daily life. For older adults and adults living with a chronic disease like chronic obstructive pulmonary disease (COPD), the ability to use technologies for health management has become increasingly important. The aim of this study was to investigate inter-rater and test-retest reliability of the Danish version of the Everyday Technology Use Questionnaire (ETUQ) in a sample of older adults with (n = 23) and without (n = 24) COPD.Material and method: The ETUQ was initially translated in accordance with the dual panel approach and then administered to a sample of 47 participants. Svensson’s method for paired ordinal data was utilized to calculate and analyze reliability.Results: Overall, inter-rater and test-retest reliability of the Danish version of the ETUQ demonstrated excellent percentage agreement (PA) (>75%), although for test-retest reliability, nine items demonstrated fair (53%) to good (73%) agreement.Conclusion: This study supports the use of the Danish version of the ETUQ in a sample of older adults with or without COPD.Application to practice: The Danish version of the ETUQ is an evidence-based evaluation that can reliably contribute to clinical occupational therapy and research in Denmark focusing on everyday technology use.

항공기용 가스터빈 엔진의 건전성 관리기술 발전 동향

항공기용 가스터빈 엔진의 건전성 관리기술 발전 동향

What drives construction workers' acceptance of wearable technologies in the workplace?: Indoor localization and wearable health devices for occupational safety and health

Abstract The use of wearable sensing devices (e.g., GPS and physiological sensors) can open a new door toward occupational safety and health management in physically demanding and hazardous construction (e.g., tracking worker's locations in dangerous working area and monitoring of worker's physiological status). These potential benefits cannot be achieved, however, if construction workers do not recognize the value of wearable devices as well as the details of their usage. This study thus investigates determinants for workers' adoption of wearable technology in the occupational work context. Specifically, the scope of this study is to test hypotheses regarding workers' intention to adopt two representative wearable devices for occupational safety and health, a smart vest with an embedded indoor GPS for location tracking, and a wristband-type wearable activity tracker (i.e., wristband) with physiological sensors. The research results indicate that perceived usefulness (PU), social influence (SI), and perceived privacy risk (PR) are associated with workers' intention to adopt (IA) both smart vest and wristband. Also, workers' experiences using wearable devices positively moderates the association between PU and IA of smart vest and negatively moderates the association between SI and IA of smart vest. In the work context, foremen are more likely to be influenced by PU than workers with regard to using a wristband. By considering the different functions, benefits, and challenges of each device, and by taking into account individual and job characteristics, the results of this study provide crucial insight into the process of motivating workers to adopt each device in their work, which can promote the continued and appropriate use of wearable technology in occupational safety and health management.

On-line prognosis of fatigue crack propagation based on Gaussian weight-mixture proposal particle filter

Accurate on-line prognosis of fatigue crack propagation is of great meaning for prognostics and health management (PHM) technologies to ensure structural integrity, which is a challenging task because of uncertainties which arise from sources such as intrinsic material properties, loading, and environmental factors. The particle filter algorithm has been proved to be a powerful tool to deal with prognostic problems those are affected by uncertainties. However, most studies adopted the basic particle filter algorithm, which uses the transition probability density function as the importance density and may suffer from serious particle degeneracy problem. This paper proposes an on-line fatigue crack propagation prognosis method based on a novel Gaussian weight-mixture proposal particle filter and the active guided wave based on-line crack monitoring. Based on the on-line crack measurement, the mixture of the measurement probability density function and the transition probability density function is proposed to be the importance density. In addition, an on-line dynamic update procedure is proposed to adjust the parameter of the state equation. The proposed method is verified on the fatigue test of attachment lugs which are a kind of important joint components in aircraft structures.

Research Status of Prognostics and Health Management in Nuclear Field

Recently, the concerns of Nuclear Power Plants (NPPs) safety and reliability have increasing because of Fukushima disaster and NPPs increasing operating years. Operational experience has shown that greater situational awareness of the state of safety-critical nuclear plant System, Structure, and Components (SSCs) is necessary, particularly as they age due to exposure to harsh service conditions. While replacement of a subset of components is possible, and may even be economically attractive, it may be economically prohibitive to replace several of the lager components, including the reactor pressure vessel and primary piping. Thus, characterization, management, and mitigation of aging-related degradation in these critical passive components become important to maintain safety margins. In order to deal with these problem above mentioned, thus, it is necessary to develop Prognostics and health Management (PHM) technology. The key technology in PHM is to detect degradation and anomalies and to determine Remaining Useful Life (RUL) and Probability of Failure (POF) of SSCs. The prognostics results can be used to manage the evolving health and condition of nuclear plant SSCs. The prognostics information is used in a Probabilistic Safety Assessment (PSA) model to assess the risk significance of the degradation and the corresponding reduced safety margin. In this paper, the review of the state of the art in PHM for nuclear industry was summarized. In addition, the prognostics application in case of Nuclear filed and technical gap for NPPs weredescribed.

Does PHM Make an Engineered System Resilient under Sensor Faults?

The use of Prognostics and Health Management (PHM) technology enables engineered systems resilient under adverse events. Adverse events include unexpected system failures, anomaly operation, manufacturing defects, etc. As engineered systems become more complex, resilience thus becomes an emerging engineering feature, which offers an attractive ability to resist and recover from adverse events. However, most resilience studies were conducted under the assumption of no false alarms. This study thus concerns how engineering resilience can be formulated while taking into account false alarm due to sensor faults. Sensor faults can be interpreted in various manners: sensor breakdown, calibration change, sensor noise, etc. This study proposes a new formulation of engineering resilience measure considering sensor faults. Sensor faults can be implemented by changing a sensor gain. The effectiveness of the proposed resilience measure is demonstrated by implementing the PHM into the electro-hydrostatic actuator (EHA) and assessing the resilience under sensor faults. This study is also conducted to see how the proposed resilience measure behaves in the course of the sensor degradation.

Remaining useful life estimation of engineered systems using vanilla LSTM neural networks

Abstract Long Short-Term Memory (LSTM) networks are a significant branch of Recurrent Neural Networks (RNN), capable of learning long-term dependencies. In recent years, vanilla LSTM (a variation of original LSTM above) has become the state-of-the-art model for a variety of machine learning problems, especially Natural Language Processing (NLP). However, in industry, this powerful Deep Neural Network (DNN) has not aroused wide concern. In research focusing on Prognostics and Health Management (PHM) technology for complex engineered systems, Remaining Useful Life (RUL) estimation is one of the most challenging problems, which can lead to appropriate maintenance actions to be scheduled proactively to avoid catastrophic failures and minimize economic losses of the systems. Following that, this paper aims to propose utilizing vanilla LSTM neural networks to get good RUL prediction accuracy which makes the most of long short-term memory ability, in the cases of complicated operations, working conditions, model degradations and strong noises. In addition, to promote cognition ability about model degradation processes, a dynamic differential technology was proposed to extract inter-frame information. The whole proposition is illustrated and discussed by performing tests on a case of the health monitoring of aircraft turbofan engines which have four different issues. Performances of vanilla LSTM are benchmarked with standard RNN and Gated Recurrent Unit (GRU) LSTM. Results show the significance of performance improvement achieved by vanilla LSTM.

Maintenance scheduling based on remaining useful life predictions for wind farms managed using power purchase agreements

Abstract Prognostics and Health Management (PHM) technologies have been introduced into wind turbines to forecast the Remaining Useful Life (RUL). PHM with RUL predictions enables predictive maintenance for wind turbines prior to failure, thus avoiding corrective maintenance that may be expensive and cause long downtimes. For a wind farm managed using a power purchase agreement (PPA), a simulation-based European real options analysis model is used to schedule predictive maintenance by maximizing the predictive maintenance option value. For multiple wind turbines indicating RULs concurrently, the predictive maintenance value for each turbine depends on the operational state of all the other turbines, the amount of energy delivered, and the energy delivery target, prices and penalization mechanism for under-delivery defined in the PPA. A case study is presented in which the optimum predictive maintenance opportunity is determined for a wind farm managed using a PPA. To the authors' knowledge, this is the first wind farm maintenance model including a PPA, and the case study demonstrates that the optimum predictive maintenance opportunity for a PPA-managed farm is different from the same farm managed using an “as-delivered” contract, and also differs from the optimum predictive maintenance opportunities for the individual turbines with RULs managed in isolation.

A technical framework of PHM and active maintenance for modern high-speed railway traction power supply systems

ABSTRACTThe safety and reliability of traction power supply systems (TPSSs) have aroused concern for the long-term development of high-speed railway (HSR) in China. To improve the current TPSS maintenance situation, it is essential to manage and make full use of the abundant data from on-line sensors and off-line tests for fault detection and prediction. This paper proposes a novel framework using prognostics and health management and active maintenance (PHM-AM) technology for TPSS maintenance. The hardware components and software modules needed to implement the proposed PHM-AM framework are described in details. Subsequently, several methodologies are employed to address the issues of data management, utilization, and maintenance in TPSSs. Finally, the implementation of the PHM-AM system is illustrated through three case studies and a preliminary software interface. These examples help to bridge the gap between theoretical modelling and the practical application of fault detection, health diagnosis, and maintenance decision-making methods in TPSSs.

A Data Driven Model for Predicting Tool Health Condition in High Speed Milling of Titanium Plates Using Real-Time SCADA

Carbide tools are easy to be damaged and quick-wear in the process of high speed machining (HSM) of titanium plates, as it is difficult to predict its working condition accurately. The machining quality requirement of thin wall parts is so high, and the health condition of the cutter is closely related to the quality of processing. Predicting the change of tool health condition is very important to a controllable processing quality. In this paper, a data model of machine and tool, communication framework and access strategy based on the OPC UA was developed to predict tool healthy condition. A prognostics and health management (PHM) technology was applied to deal with related machine data. A BP neural network model was built to reflect the relationship between machine condition and the parameters of the tools. The tool healthy condition parameters, dimensions errors, surface roughness and surface texture of the workpiece were inspected to revise the prediction model, which developed the accuracy of the model and make it to be more valuable. The model realized the monitoring and predicting of important information of tool healthy condition and made the intelligent judgment to machining quality. The technical level of intelligent monitoring of tool management and processing cycle was improved while the efficiency and quality of machining titanium were increased.

Based on the MSET and SPRT Liquid Nitrogen Pump Prognostics and Health Management

To improve the reliability and availability, prognostics and health management technology was applied to the liquid nitrogen pump. This paper presents a multivariate state estimation techniques and sequential probability ratio test model to predict equipment health. In the approach, correlation model among monitoring parameters in normal work condition is constructed firstly. Then, according to the similarities between the current observed feature vector and each history feature vector contained in process memory matrix, estimation of the current feature vector is calculated by using MSET, and residual signal between the current feature vector and its estimation is obtained in turn. Finally, mean test and variance test for the residual signal is executed by using SPRT, and work condition of the system is pronounced. Use the real-time data to test the model, the result showed that using this method can obtain good effect.

Integration of oral health into primary care: a scoping review protocol

IntroductionIntegrated care has been introduced as a means of improving health outcomes and access to care, and reducing the cost of healthcare. Despite its importance, the integration of oral health...

Dynamic performance of a pantograph–catenary system with consideration of the contact surface

The influence of contact surface upon the dynamic performance of a pantograph–catenary system is a question that has been highlighted by the increasing use of high speed railways. Using the mode-superposition method, we establish a pantograph–catenary-coupled dynamics model with consideration of the contact surface; we analyse the characteristics of the contact surface based on a large amount of measured data; and we determine the characteristic parameters according to their influence upon the system dynamic performance. The results show that the contact wire irregularity contains a periodic component formed by gravity and a random component formed by wear, local assembly errors, hardpoints and the like. Periodic and low-frequency random irregularities mainly lead to the increase in contact force amplitude at peak points with wavelengths submultiple of the tension length, span or dropper spacing. On the contrary, high-frequency random irregularity has a great influence upon the amplitude of all frequencies and is the main factor leading to the deterioration of the system dynamic performance. The contact strip wear surface achieves quadratic function fitting, and we propose two characteristic quantities of this surface, namely, shape parameter A, and wear depth B. The wear surface of contact strip enhances the periodic characteristics of pantograph–catenary system of each span, leading to the amplitude increase of the contact force in frequency with span submultiple wavelength. The bigger the absolute value of the shape parameter, the poorer the dynamic performance. According to the criteria of the contact force eigenvalues, we determine the threshold value of the contact strip shape parameter A, and the contact wire irregularity amplitude of each frequency. The threshold given in this paper can be used in the condition assessment of parts, and make judgements on whether a servicing is needed, which is part of the technologies for informationisation and intelligent high-speed train health management.

A review of diagnostic and prognostic capabilities and best practices for manufacturing.

Prognostics and health management (PHM) technologies reduce time and costs for maintenance of products or processes through efficient and cost-effective diagnostic and prognostic activities. PHM systems use real-time and historical state information of subsystems and components to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. However, PHM is still an emerging field, and much of the published work has been either too exploratory or too limited in scope. Future smart manufacturing systems will require PHM capabilities that overcome current challenges, while meeting future needs based on best practices, for implementation of diagnostics and prognostics. This paper reviews the challenges, needs, methods, and best practices for PHM within manufacturing systems. This includes PHM system development of numerous areas highlighted by diagnostics, prognostics, dependability analysis, data management, and business. Based on current capabilities, PHM systems are shown to benefit from open-system architectures, cost-benefit analyses, method verification and validation, and standards.

A health management technology for multisite cracks in an in-service aircraft fuselage based on multi-time-frame laser ultrasonic energy mapping and serially connected PZTs

The problems of multisite fatigue crack growth and multiple interacting cracks in aircraft fuselage structures may incur catastrophic failure. This article proposes a structural health management (SHM) strategy for multisite cracks in the skin and the vicinity of fastener holes in lap joints and demonstrates that the strategy is effective in an in-service metallic aircraft fuselage. To this end, the present article uses a conductive fabric tape-equipped serially connected piezoelectric sensor array combined with an ultrasonic propagation imaging (UPI) system. The serially connected piezoelectric sensor array is defined as a single sensor that has multiple sensor nodes with one electrical output. It was connected with conductive fabric tape instead of lead wire due to its comparatively low attenuation, light weight, and compact and easy installation on the internal structures of the complex fuselage. This technology does not require multi-cabling and multi-channeling because of its ultrasonic propagation imaging capability from multiple sources of the UPI system. The fuselage side skin of a Cessna-150 with various crack locations and orientations was tested using the proposed SHM technology. The ultrasonic wave propagation imaging video showed multisource wave propagation, and all of the crack-induced scattering wavefields appeared at different times because of the large inspection area. Therefore, we have developed a multi-time-frame ultrasonic energy mapping algorithm, which was processed in the wavenumber domain to visualize the cracks. Consequently, the lightweight, easy, and compact installation of the serially connected PZTs and their combination with the UPI system were shown, demonstrating that the SHM strategy for multisite cracks in an in-service aircraft fuselage is effective.