Real-Time data-driven average active period method for bottleneck detection

Prioritising improvement and maintenance activities is an important part of the production management and development process. Companies need to direct their efforts to the production constraints (bottlenecks) to achieve higher productivity. The first step is to identify the bottlenecks in the production system. A majority of the current bottleneck detection techniques can be classified into two categories, based on the methods used to develop the techniques: Analytical and simulation based. Analytical methods are difficult to use in more complex multi-stepped production systems, and simulation-based approaches are time-consuming and less flexible with regard to changes in the production system. This research paper introduces a real-Time, data-driven algorithm, which examines the average active period of the machines (the time when the machine is not waiting) to identify the bottlenecks based on real-Time shop floor data captured by Manufacturing Execution Systems (MES). The method utilises machine state information and the corresponding time stamps of those states as recorded by MES. The algorithm has been tested on a real-Time MES data set from a manufacturing company. The advantage of this algorithm is that it works for all kinds of production systems, including flow-oriented layouts and parallel-systems, and does not require a simulation model of the production system

MATRIX METALLOPROTEINASES 2 AND 9 IN AVOCATION OF MULTITUDINAL COMPLICATIONS IN EXPLICITLY TO CARCINOMA: REVIEW

Matrix metalloproteinases (MMPs) are a large group of calcium-dependent zinc containing endopeptidases which are mainly concerned with the remodeling of tissue along with degradation of the extracellular matrix. At the present scenario, there is knowledge of about 26 MMPs which are found to be highly regulated by the growth hormones, cytokines, etc., present within the body. At times of normal homeostasis, their levels within the body are low, and their number usually increases at times of pathological conditions. Its generation is known to occur from the pro-inflammatory cells and connective tissues. They may even lead to the process of apoptosis by its interactions with surface receptors. In the clinical trials sectors, various MMPs along with their inhibitors are examined to import the properties of being a high biomarker in the cancer diagnosis, antiangiogenic agents, various other disorders such as chronic allograft nephropathy, diabetic nephropathy, cardiovascular diseases, neuropathic pain, wound healing, angiogenesis processes, immune response, corneal ulceration, embryonic development, and nervous system disorders. As a result, enormous number of studies on this particular enzyme in the marking of cancer and their elevation in the above-mentioned diseases has to be carried out so that it would remain as a useful tool in their diagnosis. The present work is designed to emphasize the concise review of MMPs, in particularly MMP-2 and MMP-9 along with their variant roles, keeping in mind, that it would be advantageous for the researchers to bring out more promising results and to intensify diagnosis of various infirmities, especially in cancer.Keywords: Matrix metalloproteinase-2, Matrix metalloproteinase-9, Biomarker, Matrix metalloproteinases, Carcinoma, Extracellular matrix, Malignancy, Gelatinases, Tumor

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

Data Analytics in Maintenance Planning – DAIMP

Manufacturing industry plays a vital role in the society, which is evident in current discussions on industrialization agendas. Digitalization, the Industrial Internet of Things and their connections to sustainable production are identified as key enablers for increasing the number of jobs in Swedish industry. To implement digitalized manufacturing achieving high maintenance performance becomes utmost necessity. A substantial increase in systems availability is crucial to enable the expected levels of automation and autonomy in future production. Maintenance organizations needs to go from experiences based decision making in maintenance planning to using fact based decision making using Big Data analysis and data-driven decision support. Currently, there is lack of maintenance-oriented research based on empirical data, which hinders the increased use of engineering methods within the area.The DAIMP project addresses the problem with insufficient availability and robustness in Swedish production systems. The main challenges include limited productivity, challenges in capability of introducing new products, and challenges in implement digital production. The DAIMP project connects data collection from a detailed machine level to system level analysis. DAIMP project aimed at reaching a system level analytics to detect critical equipment, differentiate maintenance planning and prioritize the most important equipment in real-time. Furthermore, maintenance organizations will also be supported in moving from descriptive statistics of historical data to predictive and prescriptive analytics.The main goals of the project are: Agreed data parameters and alarm structures for analyses and performance measures Increased back-office maintenance planning using predictive and prescriptive analysis Increased use of dynamic and data-driven criticality analysis Increased prioritization of maintenance activitiesThe goals were further divided into specific goals and six work packages were designed to execute the project.WP1 focused on the purchase phase and getting data structures and collaboration with equipment vendors correct from start.WP2 focused on the ramp-up phase of new products and production lines when predictive and prescriptive analytics are important to handle unknown disturbances.WP3 focused on the operational phase and to provide data-driven decision support for directing maintenance efforts to the critical equipment from a systems perspective.WP4 focused on designing maintenance packages for different equipment with inputs from WP3, including both reactive, preventive, and improving activities.WP5 focused on the evaluation and demonstration for different project resultsWP6 focused on coordination project managementIn WP1, models were developed to understand the missing element for the capability assessment from initiation of the machine tool procurement to the end of lifecycle. The information exchange and process of machine tool procurement from the end-users perspective was assessed. Additionally, the alarm structure is created using the capability framework and the ability model. In WP2, diagnostic, predictive and prescriptive algorithms were developed and validated. The algorithms were developed using manufacturing execution system (MES) data to provide system level decision making using data analytics. Improved quality of decisions by data-driven algorithms. Moved from experienced based decision to algorithmic based decisions. Identified the required amount data sets for developing machine learning algorithm. In WP3, data-driven machine criticality assessment framework was developed and validated. MES and computerised maintenance management system (CMMS) data were used to assess criticality of machines. It serves as data-driven decision support for maintenance planning and prioritization. It provided guidelines to achieve systems perspective in maintenance organization. In WP4, a component classification was developed. It provides guidelines for designing preventive maintenance programs based on the machine criticality. It uses CMMS data for component classification. In WP6, three demonstrator cases were performed at (i) Volvo Cars focusing on system level decision support at ramp up phase, (ii) Volvo GTO focusing on global standardization and (iii) a test-bed demo of data-driven criticality assessment at Chalmers. Lastly, as part of WP6, an international evaluation was conducted by inviting two visiting professors.The outcomes of the DAIMP project showed a strong contribution to research and manufacturing industry alike. Particularly, the project created a strong impact and awareness regarding the value maintenance possess in the manufacturing companies. It showed that maintenance will have a key role in enabling industrial digitalization. The project put the maintenance research back on the national agenda. For example, the project produced world-leading level in MES data analytics research; it showed how maintenance can contribute to productivity increase, thereby changing the mind-set from narrow-focused to having an enlarged-focus; showed how to work with component level problems to working with vendors and end-users

The Effects of Time Varying Curvature on Species Transport in Coronary Arteries

Alterations in mass transport patterns of low-density lipoproteins (LDL) and oxygen are known to cause atherosclerosis in larger arteries. We hypothesise that the species transport processes in coronary arteries may be affected by their physiological motion, a factor which has not been considered widely in mass transfer studies. Hence, we numerically simulated the mass transport of LDL and oxygen in an idealized moving coronary artery model under both steady and pulsatile flow conditions. A physiological inlet velocity and a sinusoidal curvature waveform were specified as velocity and wall motion boundary conditions. The results predicted elevation of LDL flux, impaired oxygen flux and low wall shear stress (WSS) along the inner wall of curvature, a predilection site for atherosclerosis. The wall motion induced changes in the velocity and WSS patterns were only secondary to the pulsatile flow effects. The temporal variations in flow and WSS due to the flow pulsation and wall motion did not affect temporal changes in the species wall flux. However, the wall motion did alter the time-averaged oxygen and LDL flux in the order of 26% and 12% respectively. Taken together, these results suggest that the wall motion may play an important role in coronary arterial transport processes and emphasise the need for further investigation

Automated Unmanned Railway Level Crossings

Railroad related mischance's are more hazardous than other transportation mishaps regarding seriousness and demise rate and so on. In this manner more endeavors are essential for enhancing security. There are numerous railroads crossing which are unmanned because of bolt of labor expected to satisfy the requests. Consequently numerous mishaps happen at such intersection since there is nobody to deal with the working of the railroad door when a prepare approaches the intersection. The principle goal of this paper is to computerize the control arrangement of railroad door utilizing microcontroller. The flag is considered as a contribution to this proposed framework and entryways are intended for the Indian street conditions and they are controlled by engines.

r-2,c-6-Diphenylpiperidine

In the title compound, C17H19N, the piperidine ring adopts a chair conformation. The phenyl rings substituted at the 2- and 6-positions of the piperidine ring subtend dihedral angles of 81.04 (7) and 81.10 (7)° with the best plane of the piperidine ring. The crystal packing features C—H...π interactions