Spinning Mill Fault Localization Using Yarn Parameter Correlation

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Solution Overview

Problem

Existing systems in spinning mills lack an efficient and flexible method to quickly locate functional disorders within the spinning and winding sections, including spinning machines, winding machines, and the transportation of yarn delivery bobbins, which can impact yarn quality and production efficiency.

Innovation Solution

An electronic device that receives and compares spinning and winding parameters to detect abnormalities, using neural networks and fuzzy logic algorithms to identify the source of functional disorders at spinning machines, winding machines, or transportation processes, and supports automatic, semi-automatic, or manual corrections.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional monitoring systems are used in spinning mills, then system complexity is reduced, but the ability to quickly locate functional disorders and improve productivity is worsened

Engineering Contradiction:

Improvespeed of locating functional disordersVSAvoidcomplexity of electronic device with neural networks

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The electronic device pre-processes and stores spinning and winding parameters in structured databases before disorders occur. Neural networks and fuzzy logic algorithms are pre-trained on historical data to enable rapid diagnosis when abnormalities are detected, eliminating the need for complex real-time analysis during actual disorder events.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary electronic device that acts as a bridge between traditional spinning/winding machines and the decision-making system. This intermediary layer collects parameters, processes them through neural networks, and provides diagnostic recommendations, isolating the complexity from the core production machinery while maintaining simplicity at the machine level.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Measurement precision

If comprehensive parameter monitoring is implemented across spinning and winding sections, then measurement precision of yarn quality improves, but device complexity and cost increase

Engineering Contradiction:

Improveprecision of yarn quality parametersVSAvoidcomplexity of monitoring system

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The electronic device is designed as a universal platform that handles multiple types of parameters (spinning parameters, winding parameters, yarn quality metrics) through a single integrated system. The neural network architecture is multi-functional, capable of analyzing different parameter combinations to diagnose various types of functional disorders, reducing the need for separate specialized monitoring systems.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Solution Approach 2:

The system creates digital copies of physical yarn quality parameters through structured data representation. Instead of complex physical analysis, the electronic device uses digital models and algorithms to replicate and analyze yarn properties, enabling precise measurement through information processing rather than elaborate physical instrumentation.

Inventive Principle:

Principle #26Copying

3Loss of time

If automated disorder location system is deployed, then loss of time in identifying issues is reduced, but initial investment and device complexity increase

Engineering Contradiction:

Improvetime to identify functional disordersVSAvoidcomplexity of automated system

Core Design Contradiction:

Loss of timeVSDevice complexity

Solution Approach 1:

The electronic device implements continuous feedback loops where spinning and winding parameters are constantly monitored, compared against learned patterns from neural networks, and used to automatically update diagnostic models. This feedback mechanism enables the system to learn from each incident and improve its diagnostic speed and accuracy over time, reducing the need for manual intervention and expertise.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The automated system performs self-diagnosis and self-improvement through the neural network's ability to learn from historical data and automatically adjust its diagnostic algorithms. The system serves itself by automatically identifying functional disorders without requiring external expert intervention, and continuously improves its capabilities through accumulated operational data, offsetting the initial complexity investment.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS20250236994A1An electronic device for locating a functional disorder within a spinning and winding section of a spinning mill and a method for locating the same

Publication Date: 2025.07.24 MASCHINENFABRIK RIETER AG

AI summary

An electronic device for locates a functional disorder within a spinning and winding section of a spinning by receive spinning parameters from spinning machines related to production of yarn delivery bobbins at the spinning machines, and receiving winding parameters from winding machines related to production of yarn bobbins at the winding machines. For the yarn bobbins, the electronic device compares a winding parameter with a normal state level value for the winding parameter to detect a winding abnormality at the respective winding machine that produced the yarn bobbin. Upon detection of the winding abnormality, the electronic device the yarn delivery bobbins used for production of the yarn bobbin and compares at least one of the spinning parameters of the identified yarn delivery bobbins with a normal state level value to locate the functional disorder within the spinning and winding section.