The vision system in the weaving loom industry

Gorbunov, Vladimir; Bobrikov, Dmitriy; Win, Thet Naing; Evgen, Ionov

doi:10.1109/eiconrus.2017.7910645

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…It better realizes the loom's innovative management and online fault diagnosis [1]. G. Vladimir et al built a fabric visual inspection system through a web camera and a microcomputer and designed a feasible scheme for automatic online monitoring of fabric quality [2]. P. P. J designed a shuttle loom control module with the peripheral interface and microcontroller, which can monitor and measure errors in warp and weft [3].…”

Section: A Development Status Of Loom Monitoring Systemmentioning

confidence: 99%

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Liu

Wang

et al. 2022

IEEE Access

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In order to improve the effect of intelligent monitoring and condition analysis of textile machinery, some solutions have been proposed to mitigate incomplete monitoring positions, insufficient decision accuracy, uncertainty reasoning and generalization of the current loom monitoring system. Firstly, a model of the weaving machine spindle dynamics was constructed, and the types and sources of monitoring data were specified. Secondly, an improved rough set method is proposed for processing the collected loom attribute data. A genetic multi-objective optimization method combined with a genetic algorithm is proposed to improve the problem of too many reduction results of the rough set method and improve the monitoring system's reliability. In order to solve the problem that new objects do not have unique matching rules in the constructed rule base, a fusion of Dezert-Smarandache Theory (DSmT) for uncertainty inference is proposed, which increases the distinguishability of decision support probabilities. Experiments show that the improved rough set method based on DSmT and genetic multi-objective optimization has higher classification accuracy and better recognition than the traditional rough set method for weaving machine condition monitoring.

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Section: A Development Status Of Loom Monitoring Systemmentioning

confidence: 99%

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Liu

Wang

et al. 2022

IEEE Access

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“…Through the relay switching unit, the control unit shut down the machine. The status of the shuttle and warp sensor is shown on the LCD [12]screen, and the monitoring message is shown on the screen as well.…”

Section: B Power Control and Data Log System Design In Loom Industrymentioning

confidence: 99%

Automatic Yarn Cut Detection in Power Loom using Iot

Prithivi¹,

Thenmozhi²,

Vanathi³

et al. 2020

IJEAT

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Textile trade has occupied second place next to agriculture. Due to the increase in population growth, textile trade in today's world is growing in plenty. A power loom is one of the main advances within weaving industrialization. It employs the country's more than thirty-five million people. Trade's main objective is to know its high-productivity power. The biggest downside which a textile trade is facing is that once the thread is cut the material gets broken. This results in the production of unnecessary cloth. The designed system is meant in such a simple way that it stops the device and avoids damage. This uses Raspberry pi 3 as the primary principal unit. The proposed system uses AC to DC Rectifier, A / D Converter and raspberry pi to signify automatic yarn cut detection in a loom. Using raspberry pi, this device mainly aims to detect the yarn cut in a loom. In this paper, the use of a single controller controls four power looms. Once the yarn cuts, the fault is detected, the supply may cut off immediately and then the fault is corrected.

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“…Objects in photographs are isolated to obtain an image of the object for analysis to acquire quantitative data such as size, shape, and the direction of movement of objects in the photograph. We can subsequently use these quantitative data for analysis and to create systems for use in various applications, such as the fingerprint recognition system, the quality inspection system of products in the manufacturing process of industrial plants, the grade separation or inspection system of the quality of agricultural crops, the automatic postal code reading system that sorts letters from images of the postal code on the envelope, the data collection system of vehicles that enter and exit a building using photographs of the vehicle license plate, the road traffic monitoring system in which the number of cars on the road is counted in a photograph taken by closed-circuit television (CCTV), the face recognition system for surveillance of terrorists in buildings, at landmarks, or immigration areas, the advanced license plate recognition system for car parking, (8) the image processing technique in a real-time vision system for automatic weeding strategy, (9) the vision system in the weaving loom industry, (10) and the vision system for monitoring intermodal freight trains. (11) These systems require much image processing that must be repeated.…”

Section: Vision Systemmentioning

confidence: 99%

Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection

Saenthon¹,

Kiatwanidvilai²,

Pumyoy³

2020

Sensors and Materials

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We studied and inspected the evaluation of safety during the operation of a rail system. In addition, in order to find patterns in the interaction between wheels and rails, the interaction is investigated using an automatic vision system to analyze the patterns that occur. The model of a faster region with a convolutional neural network (R-CNN) can be used to design artificial intelligence (AI) models with vision systems for detecting abnormal operations that occur, causing rail accidents, and to monitor the measurement of tracks and interactions between wheels and tracks in real time.

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The vision system in the weaving loom industry

Cited by 4 publications

References 2 publications

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

A Condition Monitoring Strategy of Looms Based on DSmT Theory and Genetic Multiobjective Optimization Improves the Rough Set Method

Automatic Yarn Cut Detection in Power Loom using Iot

Development of Artificial-intelligence Vision System for Measurement On-service Train for Train-track Inspection

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