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.

Development of a Novel Methodology for Remaining Useful Life Prediction of Industrial Slurry Pumps in the Absence of Run to Failure Data

Smart remaining useful life (RUL) prognosis methods for condition-based maintenance (CBM) of engineering equipment are getting high popularity nowadays. Current RUL prediction models in the literature are developed with an ideal database, i.e., a combination of a huge “run to failure” and “run to prior failure” data. However, in real-world, run to failure data for rotary machines is difficult to exist since periodic maintenance is continuously practiced to the running machines in industry, to save any production downtime. In such a situation, the maintenance staff only have run to prior failure data of an in operation machine for implementing CBM. In this study, a unique strategy for the RUL prediction of two identical and in-process slurry pumps, having only real-time run to prior failure data, is proposed. The obtained vibration signals from slurry pumps were utilized for generating degradation trends while a hybrid nonlinear autoregressive (NAR)-LSTM-BiLSTM model was developed for RUL prediction. The core of the developed strategy was the usage of the NAR prediction results as the “path to be followed” for the designed LSTM-BiLSTM model. The proposed methodology was also applied on publically available NASA’s C-MAPSS dataset for validating its applicability, and in return, satisfactory results were achieved.

Investigation on Online DGA Monitoring to Determine Transformer Health Index Using Machine Learning

The Dissolve Gas Analysis (DGA) to determine the ageing and degradation of the transformer is standard and routine periodic maintenance. In general, there are two DGA analysis methods which are conventional (lab-based) and online monitoring. DGA monitoring will be able to access to detect incipient fault and transformer failure. Several techniques are available to analyse, interpret and diagnose the DGA result, such as IEEE standard, IEC 60599 standard, Key Gas Method, and Duval methods. There are several Machine Learning (ML) techniques has been explored such as Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Neural Neighbours (KNN), Random Neural Network (RNN), and Fuzzy Logic for determining the transformer condition, including fault diagnostic and fault detection. However, there are unexplored studies to combine the commercial device to determine the Health Index (HI) of Transformer. In this study, an ML method with the available input feature from the commercial device to the network is trained to determine the HI. In general, the benchmark dataset from the existing work is employed to validate the proposed investigation. There are 730 datasets comprising five different classes; 1) Very Good, 2) Good, 3) Fair, 4) Poor, 5) Very Poor in determining the HI of a transformer. Conventional rule to partition the train and testing dataset with a 70:30 ratio is employed in this study. The maximum accuracy results and method for 1) M1 is 66.67% for ANN, 2) M2 is 68.49% for ANN, 3) M3 is 76.71% for KNN, 4) M5 is 76.26% for ANN, 5) M6 is 79.00% for ANN and 6) M7 is 86.30% for ANN. In conclusion, the multi-gas device will have a good accuracy performance and provide a good HI indicator to classify the condition of the transformer, which can be used for preventive maintenance.

Airborne bacteria in show caves from Southern Spain

This work presents a study on the airborne bacteria recorded in three Andalusian show caves, subjected to different managements. The main differences within the caves were the absence of lighting and phototrophic biofilms in Cueva de Ardales, the periodic maintenance and low occurrence of phototrophic biofilms in Gruta de las Maravillas, and the abundance of phototrophic biofilms in speleothems and walls in Cueva del Tesoro. These factors conditioned the diversity of bacteria in the caves and therefore there are large differences among the CFU m-3, determined using a suction impact collector, equipment widely used in aerobiological studies. The study of the bacterial diversity, inside and outside the caves, indicates that the air is mostly populated by bacteria thriving in the subterranean environment. In addition, the diversity seems to be related with the presence of abundant phototrophic biofilms, but not with the number of visitors received by each cave.

Automatic Boundary Extraction for Photovoltaic Plants Using the Deep Learning U-Net Model

Nowadays, the world is in a transition towards renewable energy solar being one of the most promising sources used today. However, Solar Photovoltaic (PV) systems present great challenges for their proper performance such as dirt and environmental conditions that may reduce the output energy of the PV plants. For this reason, inspection and periodic maintenance are essential to extend useful life. The use of unmanned aerial vehicles (UAV) for inspection and maintenance of PV plants favor a timely diagnosis. UAV path planning algorithm over a PV facility is required to better perform this task. Therefore, it is necessary to explore how to extract the boundary of PV facilities with some techniques. This research work focuses on an automatic boundary extraction method of PV plants from imagery using a deep neural network model with a U-net structure. The results obtained were evaluated by comparing them with other reported works. Additionally, to achieve the boundary extraction processes, the standard metrics Intersection over Union (IoU) and the Dice Coefficient (DC) were considered to make a better conclusion among all methods. The experimental results evaluated on the Amir dataset show that the proposed approach can significantly improve the boundary and segmentation performance in the test stage up to 90.42% and 91.42% as calculated by IoU and DC metrics, respectively. Furthermore, the training period was faster. Consequently, it is envisaged that the proposed U-Net model will be an advantage in remote sensing image segmentation.

Peningkatan Kinerja Switched Reluctance Generator dengan Pergeseran Sudut Penyalaan

ABSTRAKDewasa ini teknologi energi terbarukan biasanya menggunakan mesin listrik sinkron Alternating Current (AC) pada pembangkit listrik tenaga angin. Generator listrik sinkron AC menggunakan brush pada proses eksitasi yang membutuhkan perawatan berkala sehingga rumit jika diaplikasikan pada Pembangkit Listrik Tenaga Angin. Pada penelitian ini generator akan menggunakan mesin Switched Reluctance yang dioperasikan sebagai Switched Reluctance Generator (SRG). Untuk menghasilkan keluaran arus yang optimal, SRG akan dioperasikan dengan mengatur sudut penyalaan fasa menggunakan metode single pulse. Metode pensakelaran ini diatur oleh input capture fasilitas mikrokontrol dsPIC 30F4012. Penelitian ini telah diverifikasi dengan simulasi Simulink MATLAB dan pengujian alat. Hasil pengujian optimal dibuktikan pada sudut penyalaan θon = 40 derajat dan θoff = 170 derajat menghasilkan arus discharging sebesar 7.6 A dengan kecepatan 1647.1 RPM ditandai dengan bentuk gelombang arus yang ideal. Hasil arus discharging yang optimal dapat meningkatkan kinerja SRG, sehingga metode ini dapat diaplikasikan pada pembangkit listrik tenaga angin.Kata kunci: SRG, dsPIC30F4012, sudut penyalaan, single pulse ABSTRACTNowadays, renewable energy technology usually uses AC synchronous electric machines in wind power. AC synchronous generator uses a brush in the excitation process, which requires periodic maintenance so it is complicated if applied to wind power. In this research, the generator will use a Switched Reluctance machine that operated as a SRG. To produce an optimal current, the SRG will be operated by adjusting the ignition angle using the single pulse method. This method is controlled by input capture of the microcontroller dsPIC 30F4012. This research has been verified by simulating Simulink MATLAB and testing device. The optimal test results are proven at the ignition angle θon = 40 degree and θoff = 170 degree produces a discharging current of 7.6 A with a speed of 1647.1 RPM characterized by the ideal current waveform. The optimal discharging current results can improve the performance of the SRG, so this method can be applied to wind power.Keywords: SRG, dsPIC30F4012, the ignition angle, single pulse

АНАЛІЗ НАДІЙНОСТІ ШАСІ РЕГІОНАЛЬНОГО ПАСАЖИРСЬКОГО ЛІТАКА АН-140 НА ПОЧАТКОВОМУ ЕТАПІ ЕКСПЛУАТАЦІЇ

The article presents an analysis of the reliability of the chassis of the regional passenger aircraft AN - 140 at the initial stage of operation, as well as general information about the aircraft, front and main supports of its landing gear. The average failure rate and the total number of failures over a period of time are used as reliability indicators. The calculation of reliability indicators is based on failures detected during periodic maintenance (PM) of aircraft on planned forms 1H - 4H in the maintenance organization Part - 145 and entered into defective statements in accordance with the Management Maintenance Organization. These forms are made in accordance with the Aircraft Maintenance Manual of AN - 140 with a frequency of 500±50 flight hours. To analyze the reliability of the chassis selected a fleet of six aircraft AN - 140 and AN - 140 - 100 with a total flight of 12,000 hours and is divided into three groups by the date of their manufacture and the improvements made on them - "Leader Planes," "Refined Aircraft" and "Serial Aircraft". The condition of sampling the fleet of aircraft is the flight time of 2000 hours for each instance and the execution of four forms of PM on a swoop to form 4N. The dependence of the number of failures of the landing gear of each aircraft on the shape of the PM, the average flight of failure of the fleet and each aircraft individually, the total number of failures of the landing system for each instance of the aircraft, the most denied elements of the main and front landing gear of the aircraft are presented.Based on the results of the analysis of these reliability indicators, both generalized conclusions on the distribution of the number of failures by groups and aircraft instances, and on the specific most denied elements of the chassis system as a whole are presented. The tendency to reduce the number of failures and increase the average flight to failure, depending on the duration of aircraft operation and the manufacture date, has been revealed.