Investigation on magnetometer as potential sensors for infusion pump utilisation status

The utilisation of medical device e.g. an infusion pump is commonly treated as non-trivial process in service and maintenance section albeit in actuality, it is a crucial process for determining its correct maintenance cycle. Therefore, a discussion on the capability of magnetometer sensor as one of potential sensors to be used to keep track the utilisation status of infusion pump. The variation of magnetic flux density and its frequency produced by the stepper motor inside the infusion pump is measured and analysed through magnetometer sensor, embedded inside a Bluetooth Low Energy (BLE) device. Obtained result shows that magnetometer sensor has the potential to track the utilisation status of the tested infusion pumps.

The Effect of Transaction Costs on The Benefits of Hybrid Maize Farming in Dompu District, West Nusa Tenggara

The characteristics of imperfect markets are characterized by the presence of transaction costs. One sector that is an imperfect market is the agricultural sector. The purpose of this study was to analyze the structure of transaction costs and the implications of transaction costs on the benefits of hybrid corn farming. The research method uses transaction cost analysis and multiple linear regression. The research was conducted in Dompu Regency, West Nusa Tenggara with 120 respondents as corn farmers. The results show that implementation costs are the component of transaction costs that have the highest percentage of 43.35%, then supervision costs are 28.37%, transportation costs are 20.61%, negotiation costs are 3.70%, information costs are 3.41% and coordination 0.57%. These transaction cost components are distributed in each hybrid corn farming cycle, among others; (1) birth cycle; (2) cropping cycle; (3) maintenance cycle; (4) supervision; (5) harvest cycle; and (6) postharvest cycle. The postharvest cycle is the cycle that has the highest percentage of 23.17%, then the maintenance cycle is 18.53%, the harvest cycle is 17.65%, the cropping cycle is 17.28% and the control cycle is 13.99%. Transaction costs have a positive and significant effect on the profits of hybrid corn farming.

Automobile Predictive Maintenance using Deep Learning

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

Automobile Predictive Maintenance Using Deep Learning

There are three types of maintenance management policy Run-tofailure (R2F), Preventive Maintenance (PvM) and Predictive Maintenance (PdM). In both R2F and PdM we have the data related to the maintenance cycle. In case of Preventive Maintenance (PvM) complete information about maintenance cycle is not available. Among these three maintenance policies, predictive Maintenance (PdM) is becoming a very important strategy as it can help us to minimize the repair time and the associated cost with it. In this paper we have proposed PdM, which allows the dynamic decision rules for the maintenance management. PdM is achieved by training the machine learning model with the datasets. It also helps in planning of maintenance schedules. We specially focused on two models that are Binary Classification and Recurrent Neural Network. In Binary Classification we classify whether our data belongs to the failure class or the non failure class. In Binary Classification the number of cycles is entered and classification model predicts whether it belongs to the failure/non failure class.

A Study on the Predictive Maintenance Method of Fully Mechanized Mining Equipment based on Cost and Time Factors

In view of the problems of excessive maintenance and insufficient utilization of equipment service life caused by preventive maintenance of fully mechanized mining equipment with fixed cycle, a predictive maintenance method is proposed. Firstly, based on Weibull distribution function and evolution rules of equipment decay, the evolution model of equipment failure rate is established; Then, the single-objective decision-making models of equipment maintenance cost rate and maintenance downtime rate are established respectively. On this basis, the multi-objective predictive maintenance planning model of fully mechanized mining equipment with comprehensive cost and time factors is established, and the optimal predictive maintenance cycle planning sequence is obtained. Combined with the coal production continuation plan, this paper puts forward a method to determine the optimal maintenance time by making suitable choices between advance maintenance and delay maintenance. The result confirms the effectiveness and superiority of the proposed method.

Reliability allocation of rotary ultrasonic vibration-assisted EDM machine tool based on maximum entropy ordered weighted average and constraint under the index of overall cost

Rotary ultrasonic vibration-assisted EDM is a composite machining method which uses EDM technology to process metal surface and adds rotation and ultrasonic vibration. In this paper, the failure rate of each module of the rotary ultrasonic vibration-assisted EDM machine tool can be accurately predicted by the maximum entropy ordered weighted average algorithm so that the corrected maintenance cost can be predicted. And effective control of preventive maintenance costs can be achieved by selecting the best maintenance times under the premise of reliability. The results showed that the reliability of rotary ultrasonic vibration-assisted EDM machine tool is smoother at non-fixed maintenance intervals by varying the maintenance interval according to the comparison between non-fixed and fixed maintenance cycle. The cost of corrective maintenance can be reduced by varying the measurement to achieve cost optimization in the context of reliability with an optimum number of maintenance cycles and reliability.

Comparison between maintenance policies based on q-Weibull and Weibull models

PurposeThe purpose of this article is to compare maintenance policies based on Weibull and q-Weibull models.Design/methodology/approachThis paper uses analytical developments, several figures and tables for graphical and numerical comparison. Previously published hydropower equipment data are used as examples.FindingsModels for optimal maintenance interval determination based on q-Weibull distribution were defined. Closed-form expressions were found, and this allows the application of the method with small computational effort.Practical implicationsThe use of the q-Weibull model to guide the definition of maintenance strategy allows decision-making to be more consistent with sample data. The flexibility of the q-Weibull model is able to produce failure rate modeling with five different formats: decreasing, constant, increasing, unimodal and U-shaped. In this way, the maintenance strategies resulting from this model should be more assertive.Originality/valueExpressions for determining the optimal interval of preventive maintenance were deduced from q-Weibull distribution. Expected costs per maintenance cycle of Brazilian hydropower equipment were calculated with q-Weibull and Weibull distributions. These results were compared in terms of absolute values and trends. Although a large number of works on corrective and preventive maintenance have been proposed, no applications of the q-Weibull distribution were found in literature.

Viral Zoonoses

Human infection by zoonotic viruses—pathogens that normally infect animals—may result in no obvious illness, a nonspecific viral syndrome, or more severe illness typically characterized by hemorrhagic fever, encephalitis, or rash arthralgia. Transmission usually occurs by direct contact with or a bite from an infected animal or arthropod. Viral families discussed include Flaviviridae, Bunyaviridae, Arenaviridae, Filoviridae, Togaviridae, Rhabdoviridae, Paramyxoviridae, and Reoviridae, with consideration given to the epidemiology, diagnosis, treatment, and prevention of specific viruses. Hemorrhagic fevers addressed include dengue fever, dengue hemorrhagic fever, yellow fever, Crimean-Congo hemorrhagic fever, and Rift Valley fever; hantavirus infections; and the Marburg and Ebola viruses. Encephalitic fever–causing viruses discussed include La Crosse; Japanese; Murray Valley; St. Louis; tick-borne; West Nile; Powassan; eastern, western, and Venezuelan equine; rabies; Nipah; Barmah Forest; and Colorado tick fever. Rash arthralgia may be caused by the Barmah Forest, Chikungunya, Mayaro, O’nyong-nyong, Ross River, and dengue viruses. Other viral zoonoses considered include monkey B virus, ruminant and primate poxvirus, Newcastle, and foot-and-mouth diseases, as well as vesicular stomatitis virus infection. A diagram depicts the generalized arbovirus maintenance cycle. Tables list the important viral zoonoses that cause human disease, the principal hantaviruses that cause human disease, the arenaviruses that cause significant human illness, and the viral zoonoses endemic in the United States. This review contains 1 figure, 32 tables, and 80 references. Key words: dengue, diagnosis, encephalitis, epidemic, epidemiology, infection, rabies, virus, vaccine