Enhancing the diagnostic performance of Condition Based Maintenance through the fusion of sensor with maintenance data

Although Maintenance data is crucial for authoritative reporting reasons and is generally used to optimizemaintenance planning in terms of budget, scheduling and logistics, the potentials of the implicit given informationfor Prognostics and Health Management (PHM) frameworks are not yet completely leveraged. Traditional PHMframeworks typically rely only on sensor data to derive a system’s health status, while maintenance, repair andoverhaul (MRO) data is not investigated. However, maintenance data contains valuable information on which partof a system is checked, serviced or replaced. At the same time, maintenance data is necessary for the labelling ofsensor data, the differentiation of multiple failure modes and includes the expert knowledge of the worker. Theoverall goal of the presented work is enable a model update through the integration of this information into atraditional (sensor-based) PHM/condition monitoring framework.In this context, the underlying data bases and structures will be analyzed and a generalized methodology isproposed to include maintenance data directly into the forward-modelling phase of a PHM/condition monitoringframework. The main goal is not only to use the labels derived from maintenance data for evaluation purposes(which is a common practice in PHM research), but to use this data to build a memory of the maintenance andhealth state history and thereby enhance the diagnostic capabilities of the framework. Methods from the field ofProbabilistic Programming and Bayesian Statistics seem promising and are implemented in order to incorporatefor uncertainties and to enable a confidence level for the diagnosis. The proposed concept is developed, tested andassessed in a simulation environment, allowing to investigate the influence of data confidence and label uncertaintyon the results. Furthermore, this allows to derive specific requirements for the input data and hence for the dataacquisition in the real world. The proposed concept is described in a generic way to be applicable on differentengineering domains (e.g. wind turbine or production machinery industry), but it will be tested and evaluated on areal world aviation use case. This concluding use case is defined in the context of the project INDI at TU Darmstadt(Intelligent Data Utilization in Maintenance) in cooperation with the project partner Lufthansa Technik AG.

FITNESS AND COMFORT ASSESSMENT OF FOOTWEAR: AN ANTHROPOMETRIC APPRAISAL

Suitability determination of any product designed for specific types of consumers is possible through the effective use of anthropometric information. This study assessed anthropometric data utilization in footwear designs and patterns as an indicator of fitness and comfort in footwear production. The data collected for analysis were the length and the breadth dimensions of footwear design pattern from eighteen footwear cottage shops and the foot anthropometric parameter from a total of four hundred and thirty-three (433) (males (226) and females (207)) subjects. The instrumentation design for the data collection was a digital vernier caliper (model Mitutoyo 500-506-10). The analysis of the foot anthropometry dimension and the design footwear pattern data obtained showed a lack of bilateral symmetry for the male and female gender. The fitness and comfortable foot support function of the footwear analyzed using a paired samples t-test between the footwear design pattern dimensions, and the foot anthropometric parameters disclosed p > 0.05 in all cases – not significant. This study concluded that tailoring a product design to the users’ population reduces the mismatch challenges, grants fitness, and comfort to the users.

Improved control and monitor two different PLC using LabVIEW and NI-OPC server

<span>This paper proposes an improved control and monitors between two different PLCs, the Mitsubishi, and Omron. The main advantage is interoperability and communication between both PLC. The use of NI OPC server as the software interface reached interoperability and communication. There were developed two field applications to test interoperability. Laboratory virtual instrument engineering workbench (LabVIEW) uses as the software application for creating the user interface to control and monitor. This improvement show OPC server technology solves data compatibility issue between different driver controller’s and reducing development cost. Regardless of whether there are more than two different PLCs, it's enough to use the NI OPC server. So the benefit of the NI OPC server is not limited to two types of PLC used right now but can also use the other manufacturers. Besides, the improvement of the previous study is the use of the LabVIEW makes data from the OPC server displayed more realistic. The use of LabVIEW allows additional monitoring functions, one of which is LabVIEW vision. Data utilization becomes more flexible, and so it can use for more complex purposes. It is envisaged that this is very useful for Integrator engineer to implement this method in industrial automation</span>

Factors Related to Utilization of Maternal Child Health Handbook Data by Midwives

Midwives are one of the health workers utilizing the MCH (Maternal Child Health) handbook which records the data used to prevent infant, under-five, and maternal mortality. This study aimed to investigate factors related to utilization of the MCH handbook data, namely individual, organizational, and operational techniques. Quantitative approach was used in a cross-sectional survey with a structured questionnaire. Thirty-one midwives were selected as the sample from Active Alert Village in Temanggung district with rural and urban area representative consideration. Besides, descriptive data were tabulated, and inferential analysis was conducted by Pearson product-moment correlation. Result showed the rate of MCH Handbook data utilization was 78.54%. Also, data requirement perception, data filling skill, motivation, attitudes of midwives and facilities availablity (p < 0.05) are significantly related. The MCH handbook data utilization was determined by the individual factor, but with no significant relationship to operational and organizational factors except facilities availability. Therefore, efforts are required to improve data utilization by increasing data requirement perception, data filling skills, motivation and midwives attitudes.

Research on Creativity and Personality Convergence Education to Enhance University Students' Future Competencies

This study proposes some of the elements of competencies that university students must have for coping with life in a society of the future. Furthermore, it proposes a particular teaching method to effectively strengthen these future competencies. In order to carry out this research, a review of previous studies, as well as an analysis of pertinent literature, were conducted in order to ascertain the competencies in question. In addition, three Delphi surveys were conducted by 10 experts in each major field involved. The results showed that future university students would require the following competencies: creativity and personality competency (problem solving ability, curiosity, a challenging spirit, cognitive flexibility, responsibility, leadership, etc.), a common good competency (common good sensitivity, inclusiveness, empathy and communication ability, etc.), and convergence competency (humanities literacy, engineering literacy, aesthetic literacy, etc.), and digital technology application competency (digital literacy, big data utilization, computing thinking, etc.). The results of this study can be used to develop a plan for the advancement of university education-one that meets the various needs of students living in any given future society. Moreover, it can also provide some ideas for the development of educational content by applying certain future competencies and future teaching methods.

Determinants of Data Use for Decision Making in Health Facilities in Kitui County, Kenya

Background: Effective management of today’s health systems depends on the critical use of data for the policy-making, planning, monitoring of services, and making decisions. The use of data has been limited and faced with inadequacy hence vital health decisions often relying on political opportunism, donor demand, and infrequently repeated national studies which are insensitive to changes occurring over a shorter timescale. Objective: This study was designed to identify determinants of utilization of routine data for decision making by health care providers in health facilities in Kitui County in Kenya. Methods: The conceptual framework was constructed from the literature review. This was a descriptive cross-sectional study adopting quantitative and qualitative research paradigms. The district hospital was purposively selected. Multi-stage cluster sampling was used to sample the health facilities in sub-counties and simple random sampling to select the respondents from different cadres in health facilities. The 110 participants completed a self-administered questionnaire. Key informant interviews, focused group discussions, observation, and analysis of documents in health facilities were applied. Recordings were transcribed and key concepts identified. Questionnaire results were edited, coded, tabulated, and analyzed using the SPSS 18. Results: Respondents interviewed were different cadres of health care providers including doctors, clinical officers, nurses, and midwives from different levels of health facilities. Among the respondents, 70% were females, and 30% males. The majority, 64% had a diploma level of education. Nurses/ midwives were the majority, making up 81% of the workforce. The overall extent of data utilization was 66% not using data and 34% use the routine generated data. The following actors influence utilization of routine data: frequency of meetings held ( =42.036, df=1, p=0.001), data storage and analyzing methods ( = 30.582, df=1, p=0.001), and continuous professional training ( = 49.782, df=1, p=0.0001). Organization of the district health system influenced routine data utilization through the frequency of support supervision ( =30.000, df=1, p=0.001), issues assessed during supervision ( =49.164, df=2, p=0.002) and feedback report from the supervisors ( =5.236, df=1, p=0.022). Conclusion: The study demonstrated limited utilization of routine data for decisions in health facilities and recommended the need for capacity building for data utilization through on job training and strengthening the curriculum in health training institutions in data-related areas. The ministry of health can standardize the parallel reporting levels and unify the reporting tools and finally ensure the districts have structured meetings, support supervision, and feedback to the health care providers.