The Loom Monitoring System Design with High Reliability and Easy Maintainability

Dong, Deyi; Shi, Zhongchao

doi:10.1109/iccase.2011.5997860

Cited by 4 publications

(3 citation statements)

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“…D. Dong and Z. Shi adopted the redundant structure of dual-bus and dual-region processors to realize the selfdiagnosis of the loom system, the automatic loading of working parameters, and the automatic shielding and recovery of loom interface faults. This system has high reliability and maintainability, but the degree of intelligence is insufficient [4]. K. Sun et al used the fault tree to judge the real-time data, which improved the data transmission and processing performance of the real-time information detection system of the loom [5].…”

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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“…The paper "automation in plain weaving machines using PLC" is intended to retrofit mechanical weaving machines into automatic weaving machines using a low-value PLC. Due to its power, usability, I/O interface, and reliable performance [5], PLCs are commonly used and favorite tools within the manufacturing automation industry.…”

Section: A Automation Of Plain Weaving Machine Using Plcmentioning

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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“…[19] improved the loom productivity by studying loom fault diagnosis methods. [20][21][22] studied loom monitoring system regarding loom working parameters, condition monitoring key technologies, reliability and maintainability respectively to achieve effective monitoring of loom operation status. [23][24][25] studied the loom fault diagnosis methods, advanced loom technologies and common electrical faults, and the fault diagnosis methods used are worthy of reference.…”

Section: Introductionmentioning

confidence: 99%

Research on rapier loom fault system based on cloud-side collaboration

et al. 2021

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The electrical control system of rapier weaving machines is susceptible to various disturbances during operation and is prone to failures. This will seriously affect the production and a fault diagnosis system is needed to reduce this effect. However, the existing popular fault diagnosis systems and methods need to be improved due to the limitations of rapier weaving machine process and electrical characteristics. Based on this, this paper presents an in-depth study of rapier loom fault diagnosis system and proposes a rapier loom fault diagnosis method combining edge expert system and cloud-based rough set and Bayesian network. By analyzing the process and fault characteristics of rapier loom, the electrical faults of rapier loom are classified into common faults and other faults according to the frequency of occurrence. An expert system is built in the field for edge computing based on knowledge fault diagnosis experience to diagnose common loom faults and reduce the computing pressure in the cloud. Collect loom fault data in the cloud, train loom fault diagnosis algorithms to diagnose other faults, and handle other faults diagnosed by the expert system. The effectiveness of loom fault diagnosis is verified by on-site operation and remote monitoring of the loom human-machine interaction system. Technical examples are provided for the research of loom fault diagnosis system.

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The Loom Monitoring System Design with High Reliability and Easy Maintainability

Cited by 4 publications

References 0 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

Research on rapier loom fault system based on cloud-side collaboration

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