Search results

This study aims to improve the availability of regenerative braking for urban metro vehicles by introducing a sensorless operational temperature estimation method for the braking resistor (BR) onboard the vehicle, which overcomes the vulnerability of having conventional temperature sensor.

In this study, the energy model based sensorless estimation method is developed. By analyzing the structure and the convection dissipation process of the BR onboard the vehicle, the energy-based operational temperature model of the BR and its cooling domain is established. By adopting Newton's law of cooling and the law of conservation of energy, the energy and temperature dynamic of the BR can be stated. To minimize the use of all kinds of sensors (including both thermal and electrical), a novel regenerative braking power calculation method is proposed, which involves only the voltage of DC traction network and the duty cycle of the chopping circuit; both of them are available for the traction control unit (TCU) of the vehicle. By utilizing a real-time iterative calculation and updating the parameter of the energy model, the operational temperature of the BR can be obtained and monitored in a sensorless manner.

In this study, a sensorless estimation/monitoring method of the operational temperature of BR is proposed. The results show that it is possible to utilize the existing electrical sensors that is mandatory for the traction unit’s operation to estimate the operational temperature of BR, instead of adding dedicated thermal sensors. The results also validate the effectiveness of the proposal is acceptable for the engineering practical.

The proposal of this study provides novel concepts for the sensorless operational temperature monitoring of BR onboard rolling stocks. The proposed method only involves quasi-global electrical variable and the internal control signal within the TCU.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

This paper aims to propose methods for on-line monitoring and process quality assurance of Selective Laser Melting (SLM) technology as a competitive advantage to enhance its implementation into modern manufacturing industry.

Monitoring of thermal emission from the laser impact zone was carried out by an originally developed pyrometer and a charge-coupled device (CCD) camera which were integrated with the optical system of the PHENIX PM-100 machine. Experiments are performed with variation of the basic process parameters such as powder layer thickness (0-120 μm), hatch distance (60-1,000 μm) and fabrication strategy (the so-called “one-zone” and “two-zone”).

The pyrometer signal from the laser impact zone and the 2D temperature mapping from HAZ are rather sensible to variation of high-temperature phenomena during powder consolidation imposed by variation of the operational parameters.

Pyrometer measurements are in arbitrary units. This limitation is due to the difficulty to integrate diagnostic tools into the optical system of a commercial SLM machine.

Enhancement of SLM process stability and efficiency through comprehensive optical diagnostics and on-line control.

High-temperature phenomena in SLM were monitored coaxially with the laser beam for variation of several operational parameters.

Human skin temperature data can provide reference advice for diagnosing many diseases, but current wearable skin temperature monitoring systems have few points and may miss temperature information in critical areas. This paper aims to develop a wearable system that can be used for local temperature monitoring and restore better the actual state of local human temperature by exploring ways to extend more temperature measurement points. This will provide better assistance in developing medical targeting and intelligent clothing.

The temperature measurement system contains modules for temperature monitoring, digital-to-analog conversion and regulated power supply, enabling fast reading of 12 channels of monitoring data. The microprocessor unit is the STM32F407. The instructions and control modes are written in C. The waist area was chosen as the monitoring area because it is susceptible to temperature, and many diseases are associated with skin temperature. Twelve points, including temperature-related acupuncture points and heat-sensitive points, were selected for testing and the data results agreed well with the infrared imaging results.

The waist is selected as the monitoring object, and an easy-to-wear waist temperature monitoring belt is designed to verify the application value of the system. The development of the system provides reference suggestions for the exploration of multi-point temperature monitoring systems and the integration capabilities of temperature measurement modules in wearable multifunctional systems.

In addition to waist temperature monitoring, the wearable temperature measurement system developed in this study can also be applied to other body parts. In addition, the system can be efficiently and effectively combined with various garments, making it a useful tool for researching human skin temperature.

The wearable temperature monitoring system designed in this paper extends the number of temperature test points to 12. The number of test points covers as many localized body areas as possible to indeed reproduce the temperature distribution of the human body. In addition, the selection of test points combines medically relevant body points with physiologically relevant heat-sensitive areas, which makes the temperature measurement data more valuable.

This study proposes targeted modernization of the Department of Defense (DoD's) Joint Forces Ammunition Logistics information system by implementing the optimized innovative information technology open architecture design and integrating Radio Frequency Identification Device data technologies and real-time optimization and control mechanisms as the critical technology components of the solution. The innovative information technology, which pursues the focused logistics, will be deployed in 36 months at the estimated cost of $568 million in constant dollars. We estimate that the Systems, Applications, Products (SAP)-based enterprise integration solution that the Army currently pursues will cost another $1.5 billion through the year 2014; however, it is unlikely to deliver the intended technical capabilities.

The purpose of this paper is to demonstrate industrial Internet of Things (IIoT) solution to improve the equipment condition monitoring with equipment status data and process condition monitoring with plasma optical emission spectroscopy data, simultaneously. The suggested research contributes e-maintenance capability by remote monitoring in real time.

Semiconductor processing equipment consists of more than a thousand of components, and unreliable condition of equipment parts leads to the failure of wafer production. This study presents a web-based remote monitoring system for physical vapor deposition (PVD) systems using programmable logic controller (PLC) and Modbus protocol. A method of obtaining electron temperature and electron density in plasma through optical emission spectroscopy (OES) is proposed to monitor the plasma process. Through this system, parts that affect equipment and processes can be controlled and properly managed. It is certainly beneficial to improve the manufacturing yield by reducing errors from equipment parts.

A web-based remote monitoring system provides much of benefits to equipment engineers to provide equipment data for the equipment maintenance even though they are physically away from the equipment side. The usefulness of IIoT for the e-maintenance in semiconductor manufacturing domain with the in situ monitoring of plasma parameters is convinced. The authors found the average electron temperature gradually with the increase of Ar carrier gas flow due to the increased atomic collisions in PVD process. The large amount of carrier gas flow, in this experimental case, was 90 sccm, dramatically decreasing the electron temperature, which represents kinetic energy of electrons.

Semiconductor industries require high level of data security for the protection of their intellectual properties, and it also falls into equipment operational condition; however, data security through the Internet communication is not considered in this research, but it is already existing technology to be easily adopted by add-on feature.

The findings indicate that crucial equipment parameters are the amount of carrier gas flow rate and chamber pressure among the many equipment parameters, and they also affect plasma parameters of electron temperature and electron density, which directly affect the quality of metal deposition process result on wafer. Increasing the gas flow rate beyond a certain limit can yield the electron temperature loss to have undesired process result.

Several research studies on data mining with semiconductor equipment data have been suggested in semiconductor data mining domain, but the actual demonstration of the data acquisition system with real-time plasma monitoring data has not been reported. The suggested research is also valuable in terms of high cost and complicated equipment manufacturing.

The aim of this paper is to demonstrate the use of historical building performance data to identify potential issues with the build quality and operation of a building, as a means of narrowing the scope of in-depth further review.

The response of a room to the difference between internal and external temperatures is used to demonstrate patterns in thermal response across monitored rooms in a single building, to clearly show where rooms are under-performing in terms of their ability to retain heat during unconditioned hours. This procedure is applied to three buildings of different types, identifying the scope and limitation of this method and indicating areas of building performance deficiency.

The response of a single space to changing internal and external temperatures can be used to determine whether it responds differently to other monitored buildings. Spaces where thermal bridging and changes in use from design were encountered exhibit noticeably different responses.

Application of this methodology is limited to buildings where temperature monitoring is undertaken both internally for a variety of spaces, and externally, and where knowledge of the uses of monitored spaces is available. Naturally ventilated buildings would be more suitable for analysis using this method.

This paper contributes to the understanding of building energy performance from a data-driven perspective, to the knowledge on the disparity between building design intent and reality, and to the use of basic commonly recorded performance metrics for analysis of potentially detrimental building performance issues.

Most of the mechanical systems consist of common tribo components such as gear, bearing, seal, pump, etc. During operation, the failure of such a component may lead to failure of the system or any other components depending on the system structure or its connections. The transformation of functional quantities or parameters within and various components make these vulnerable to failures and breakdowns. The main purpose of this paper is to evaluate the reliability of a tribo‐pair during operation based on the operational parameters which takes into account the deviation in functional objective and considers the component structure explicitly.

Failure representation using digraph models have mainly been limited to chemical systems and processes but have been applied to a limited extent to mechanical systems. Researchers have considered the operating parameters such as pressure, temperature; feed rate, flow, etc. as the input/output operational parameter for failure cause identification of tribo mechanical system. However, these are not true in case of components like gear, bearing, etc. Therefore, this methodology has been refined in this paper, and is extended to a tribo‐pair by considering its functions along with operating parameters for reliability assessment. The tribo‐pair considered for the development of model is a gear pair, which is a common and important example of mechanical components.

Understanding of the failure modes helps the designer in identifying the root cause of failure and the operational parameters whose increase/decrease affects the functional objective. Consideration of the input and output parameters and their interrelations are used to develop input‐output model of a gear pair called gear pair model. This requires consideration of the input and output parameters, which are based on the gear pair functionality. These parameters have been identified. Four input parameters; five output parameters and one condition monitoring parameter are considered to model the gear pair. In addition to input and output parameters condition monitoring parameters are also considered to develop the gear pair model.

Reliability assessment of a tribo‐pair during operation will help the practicing engineers to take corrective action and minimize the occurrence of undesired failure symptom during operation. The decrease in possibility of undesired failure symptom will enhance the reliability of mechanical system.

The spatial resolution of seepage monitoring methods based on fiber Bragg grating (FBG) temperature sensing technology is limited by the distance between measurement points. Improving the spatial resolution for a given number of measurement points is a prerequisite for popularizing this technology in the seepage monitoring of rockfill dams. The purpose of this paper is to address this problem.

This paper proposes a mobile-distributed seepage monitoring method based on the FBG-hydrothermal cycling seepage monitoring system. In this method, the positions of the measurement points are changed by freely dragging the FBG sensing cluster within the inner tube of a dual-tube structure, consisting of an inner polytetrafluoroethylene tube and outer polyethylene of raised temperature resistance heating tube.

A seepage velocity calibration test was carried out using the improved monitoring system. The results showed that under a constant seepage velocity, the use of the dual-tube structure enables faster cooling, and the cooling rate accelerates with an increase in the diameter of the inner tube. The use of the dual-tube structure can improve the sensitivity of the seepage evaluation index ζ_v to the seepage velocity. When the inner diameter increases, ζ_v becomes more sensitive to the seepage velocity.

A mobile-distributed seepage monitoring method based on FBG sensing technology is proposed in which the FBG sensors are not fixed. Instead, the positions of the measurement points are changed to improve the spatial resolution. Meanwhile, the use of the dual-tube structure in the presented monitoring system can improve its sensitivity.

This study aims to focus on verifying the possibility of monitoring the condition of a turboprop engine using data recorded by on-board avionics Garmin G1000. This approach has potential benefits for operators without the need to invest in specialised equipment. The main focus was on the inter-turbine temperature (ITT). An unexpected increase in temperature above the usual value may indicate an issue with the engine. The problem lies in the detection of small deviations when the absolute value of the ITT is affected by several external variables.

The ITT is monitored by engine sensors and stored by avionics 1× per second onto an SD card. This process generates large amount of data that needs to be processed. Therefore, an algorithm was created to detect the steady states of the engine parameters. The ITT value also depends on the flight parameters and surrounding environment. As a solution to these effects, the division of data into clusters that represent the usual flight profiles was tested. This ensures a comparison at comparable ambient pressures. The dominant environmental influence then remain at the ambient air temperature (OAT). Three OAT compensation methods were tested in this study. Compensation for the standard atmosphere, compensation for the standard temperature of the given flight level and compensation for the speed of the generator, where the regression analysis proved the dependence between the ambient temperature and the speed of the generator.

The influence of ambient temperature on the corrected ITT values is noticeable. The best method for correcting the OAT appears to be the use of compensation through the revolutions of the compressor turbine NG. The speed of the generator depends on several parameters, and can refine the corrected ITT value. During the long-term follow-up, the ITT differences (delta values) were within the expected range. The tested data did not include the behaviour of the engine with a malfunction or other damage that would clearly verify this approach. Therefore, the engine monitoring will continue.

This study presents a possible approach to turbine engine condition monitoring using limited on board avionic data. These findings can support the development of an engine condition monitoring system with automatic abnormality detection and low operating costs.

This article represent a practical description of problems in monitoring the condition of a turboprop engine in an aircraft with variable flight profiles. The authors are not aware of a similar method that uses monitoring of engine parameters at defined flight levels. Described findings should limit the influence of ambient air pressure on engine parameters.