USABILITY APLIKASI ANDROID HONDA e-Care DENGAN USE QUESTIONNAIRE DAN IMPORTANCE PERFORMANCE ANALYSIS (IPA)

Honda Pondok Pinang, which is located on Jl. Ciputat Raya No.80 Pondok Pinang, South Jakarta, in customer service using an android application called HONDA e-Care. HONDA e-Care is very helpful in making it easier for consumers to access Honda service services. Customers can book services at Honda dealers online, get notifications of periodic maintenance schedules, and search for the nearest dealer from a location that can be seen on a digital map by first accessing the Honda e-Care application. But consumers often also face problems, such as when booking a service, but the ongoing booking service does not appear. Until now, no one has conducted research on the quality of the Honda e-Care application. This study aims to measure the extent of the usability of the HONDA e-Care android application and provide suggestions for developing the HONDA e-Care application in the future. Based on the gap analysis, it was found that in general the HONDA e-Care android application on the Honda Pondok Pinang was not satisfied with the quality of the current applications. Based on the IPA analysis, the priority scale for improving the quality of the HONDA e-Care android application can be mapped where quadrant I is the highest priority scale according to the user's perspective, namely: [2], [5], [7], [8] and [9]. These four items must be upgraded immediately by the manager to meet user expectations.

ESPDS

In this paper, we introduce a Microsoft Excel Workbook containing the software Equipment Selection Problem DS (ESPDS) that recognizes the special structure of the equipment selection problem. The ESPDS approach is based on the context of the Brazilian forestry sector using detailed equipment maintenance schedules. No special restrictions are needed on cost inputs over time or technologies. The output is an equipment schedule that can be used to project equipment investment needs, operational costs, and tree harvesting costs. ESPDS can be applied to support companies and contractors in order to choose the best option for their operations, as well as to achieve better equipment purchase agreements. We will show how ESPDS will also be useful in providing longer term estimates of production costs. The sensitivity analysis shows how different inputs and maintenance polices can affect the best alternative. A numerical example is included considering the entrance of a specific technology that increases the equipment productivity in order to examine whether it can change the solution. ESPDS is intuitive, flexible, and easy to calculate. Although designed for the forestry industry, the approach is readily transferable to other sectors. ESPDS may be found on the web at the following URL: <a target="_blank" rel="nofollow" href="https://www.researchgate.net/publication/350811380_ESPDS_workbook">https://www.researchgate.net/publication/350811380_ESPDS_workbook</a>.

Techniques for In Situ Monitoring the Performance of Organic Coatings and Their Applicability to the Pre-Finished Steel Industry: A Review

A review is carried out in this paper into techniques that currently exist for, of have the potential to be used for, monitoring the performance of organic coating. Specific attention is paid to the applicability of each method to pre-finished steel used in the construction industry as these are rarely monitored in situ and their expected performance is often only estimated from lab-based accelerated corrosion testing. Monitoring could allow more accurate estimates of building cladding lifespan and required maintenance schedules; provide customers with active performance data; additionally, with a better understanding of performance, more appropriate coatings or coating weights could be selected for a construction project, offering economic benefits as part of smart building developments. An introduction to coatings, their use for corrosion protection, failure mechanisms, and relevant monitoring techniques is given before current assessment techniques are described in terms of their working principles. Examples of recent work are presented for the techniques that have been investigated for monitoring or directly relatable purposes. The review concludes that there are several good reasons why an optimum corrosion monitoring technology does not currently exist, however, promising research is emerging in the field of wireless and embedded sensor design which is providing optimistic results.

The initialization of initial generating equipment maintenance schedules while a heuristic method is used

Currently, heuristic methods based on iterative changing of feasible solutions set provide a perspective tool for generation equipment maintenance scheduling in power systems. Wherein effectiveness of a heuristic method depends significantly on the initial set of possible schedules or in other words quality of the method initialization. In this case, a widely used methodology of building the initial array of solutions on the basis of pseudorandom uniform generation of control variables seems to be only palliative way to access the problem. This paper proposes alternative initialization procedure drawing on the example of generating units maintenance planning with heuristic differential evolution method. The principle of this method is to get initial set of solutions utilizing normal probability distribution to generate pseudorandom deviations from the suboptimal maintenance schedule which is to be preliminarily formed using directed search method. Following this approach allows to improve probabilistic characteristics of resultant maintenance schedule in particular to decrease median value of an objective function and its coefficient of variation, and to maximize probability to get the combination of units outage moments completely suiting operational constraints.

A New Method for Friction Estimation in EMA Transmissions

The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy.

Mapping of Meteorological Observations over the Island of Ireland to Enhance the Understanding and Prediction of Rain Erosion in Wind Turbine Blades

Leading edge erosion is becoming increasingly important as wind turbine size and rainfall are predicted to increase. Understanding environmental conditions is key for laboratory testing, maintenance schedules and lifetime estimations to be improved, which in turn could reduce costs. This paper uses weather data in conjunction with a rain texture model and wind turbine RPM curve to predict and characterise rain erosion conditions across Ireland during rainfall events in terms of droplet size, temperature, humidity and chemical composition, as well as the relative erosivity, in terms of number of annual impacts and kinetic energy, as well as seasonal variations in these properties. Using a linear regression, the total annual kinetic energy, mean temperature and the mean humidity during impact are mapped geospatially. The results indicate that the west coast of Ireland and elevated regions are more erosive with higher kinetic energy. During rain events, northern regions tend to have lower temperatures and lower humidities and mountainous regions have lower temperatures and higher humidities. Irish rain has high levels of sea salt, and in recent years, only a slightly acidic pH. Most erosion likely occurs during winters with frequent rain infused with salt due to increased winds. After this analysis, it is concluded that Ireland’s largest wind park (Galway) is placed in a moderate-highly erosive environment and that RET protocols should be revisited.

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.

IoT Bus Navigation System with Optimized Routing using Machine Learning

As the population in Egypt is ever expanding, it is reflected in the increase of the number of vehicles on the road. Public transportation is the solution and the number of available buses can cover a significant amount of the population demand. However, the outdated state of the transportation infrastructure, the static nature of the lines and indistinct schedules create a confounding and unappealing user experience which prompts the users to stray to cars for their needs. So, an Intelligent Urban Transportation System (IUTS) is a must. IUTS is a multi-layered system which provides the solution for most of these problems. It operates on different layers starting from a real time vehicle tracking for transparent and efficient management of assets, cash-less ticketing done through RFID cards, vehicle health and diagnostic data for creation of automated maintenance schedules and a friendly interactive driver interface. In this paper an approach based on combining all these technologies is discussed where the hardware component is implemented based on System-on-Chip technology with custom hardware to interface with the vehicle. The data collected from the on-board unit is sent to the cloud, and with the help of machine learning algorithms the dynamic responsiveness of the system is guaranteed. The proposed system outperforms other existing ones through the dynamic and optimized routing feature for the bus navigation to optimize the operating cost but still satisfy the passengers’demand.