Black Dawet App Android Based

Restaurants with large crowds may face queues and large orders. One of them is the Dawet Jembut Kecabut Restaurant which sells dawet ireng. This restaurant has not implemented technology in terms of queuing and customer orders so that there is not a maximum queue of buyers and sellers have to ask every order to the buyer. From the above problems, the Black Dawet App was made. Android based queue and order tracking application. The waterfall method is applied in building this application. This application uses the Flutter SDK which uses the Dart programming language as an android display and CodeIgniter for the backend and MySQL as database management. With this application you can maximize the order queue at the Dawet Jembut Kecabut Restaurant, because the buyer must have a queue number and enter the order when going to buy.

DEVELOPING ORDER AND CUSTOM PRODUCTION INFORMATION SYSTEM WITH ORDER TRACKING SYSTEM IN BATIK BALQIS COLLECTION

<em><span lang="EN-US">Batik Balqis Collection is a boutique and batik producer that most of the goods are self-produced. In conducting their business, Batik Balqis Collection provides services in the form of custom orders that are still carried out manually, so it is difficult to describe the details of the order design and inaccuracies in calculating the cost of goods manufactured for each order. This research aims to design and develop order and custom production information systems with tracking information for Batik Balqis Collection customers. The method used to develop the system is the Prototype method. The prototype method phase consists of initial requirement, design, prototyping, customer evaluation, review and update, system development, and system implementation. Data collection methods used are interviews and observation. The system's outputs are purchase report, order report, material inventory report, work in process inventory report, finished good inventory report, and cost of goods manufactured report. By implementing this system, users are expected to run their business processes more efficiently.</span></em>

Software Sensors for Order Tracking Applied to Permanent Magnet Synchronous Generator Diagnostics: A Comparative Study

The paper deals with software sensors which facilitates the diagnosis of electrical machines in non-stationary operating conditions. The technique targeted is order tracking for which different techniques exist to estimate the speed and angle of rotation. However, from a methodological point of view, this paper offers a comparison of several methods in order to evaluate their performance from tests on a test bench. In addition, to perform the tests, it is necessary to initialize the different methods to make them work correctly. In particular, an identification technique is proposed as well as a way to facilitate initialization. The example of this paper is that of a synchronous generator. Angular sampling allows the spectrum to be stationary and the interpretation of a possible defect. The realization of the angular sampling and the first diagnostic elements require the knowledge of two fundamental quantities: the speed of rotation and the angular position of the shaft. The estimation of the rotation speed as well as the estimation of the angular position of the shaft are carried out from the measurement of an electric current (or three electric currents and three voltages). Four methods are proposed and evaluated to realize software sensors: identification technique, PLL (Phase Locked Loop), Concordia transform and an observer. The four methods are evaluated on measurements carried out on a test bench. The results are discussed from the diagnosis of a mechanical fault.

A Tacholess Order Analysis Method for PMSG Mechanical Fault Detection with Varying Speeds

This article deals with the detection of mechanical faults in synchronous machines from single current measurement at variable speed. The proposed approach is based on an order tracking method in which the analysis signal is sampled as a function of the mechanical angle. In this case, the spectral components become independent of the speed and the frequency analysis can be exploited. Order tracking is generally implemented from a position measurement. In this work we present a method that allows us to estimate this position and the analysis signal from only one current measurement. The proposed approach allows an intuitive adjustment of the algorithm parameters. Secondly, a statistical method is used to finalize the diagnosis. At variable speed, this type of method is difficult to implement and we show that order tracking makes it possible to simplify the analysis. The procedure is tested in simulation and on a experimental test bench.

A Two-Stage, Intelligent Bearing-Fault-Diagnosis Method Using Order-Tracking and a One-Dimensional Convolutional Neural Network with Variable Speeds

When performing fault diagnosis tasks on bearings, the change of any bearing’s rotation speed will cause the frequency spectrum of bearing fault characteristics to be blurred. This makes it difficult to extract stable fault features based on manual or intelligent methods, resulting in a decrease in diagnostic accuracy. In this paper, a two-stage, intelligent fault diagnosis method (order-tracking one-dimensional convolutional neural network, OT-1DCNN) is proposed to deal with the problem of fault diagnosis under variable speed conditions. Firstly, the order tracking algorithm is used to resample the monitoring data obtained under different rotation speeds. Then, the one-dimensional convolutional neural network is adopted to extract features of the fault data. Finally, the fault type of collected data can be obtained by fully connected networks based on the features extracted. In the time domain, while the proposed algorithm only relies on the fault data collected under one speed as the training dataset, it is capable of doing fault diagnosis under different speed conditions. In the condition with the largest difference in speed with each dataset, the accuracy of the proposed method is higher than the baseline methods by 0.54% and 11.00%—on CWRU dataset and our own dataset respectively. The results show that the proposed method performs well in dealing with the fault diagnosis under the condition of variable speeds.