POC

Anomaly Detection

One broken needle shouldn't break your entire production dreams.

Trusted By Clients, Driven By Excellence

Since 2012, SB Infowaves has been a leading Software Development & Best Digital Marketing company in Kolkata. We are an ISO 9001:2015 (Quality Management System) Certified firm to help companies i.

Material and Methods

This solution combines YOLO v10-based computer vision, multi-modal IoT sensors, real-time data analysis, and predictive algorithms to monitor needle anomalies and automate quality control in textile manufacturing. By embedding anomaly detection software and AI anomaly detection into the process, manufacturers can achieve reliable, accurate oversight of their production lines.

Our solution uses strategically positioned high-resolution cameras that capture live video feeds from production lines. The YOLO v10 object detection model processes these feeds in real-time, identifying broken or tilted needles with high accuracy. Complementary laser sensors provide precise alignment monitoring, while vibration sensors detect abnormal machine behavior patterns that often precede needle failures.

All sensor data streams to our centralized server infrastructure for immediate analysis. The system processes multiple video feeds simultaneously using optimized algorithms, ensuring rapid defect detection with minimal latency. When anomalies are detected, automated signals trigger immediate responses through connected microcontrollers, enabling instant production line adjustments. This layer of anomaly detection software further boosts operational reliability while maintaining low downtime.

The solution integrates with existing manufacturing systems through standard industrial communication protocols. Microcontroller interfaces manage automated responses, including production alerts and line stoppage when necessary. Wireless data transmission ensures flexible installation, while comprehensive logging captures all operational data for analysis and continuous improvement. The use of AI anomaly detection makes integration adaptive and future-ready.

The system tracks key performance indicators, including detection accuracy, response times, and production efficiency metrics. Intuitive dashboards provide operators with immediate visibility into quality metrics, enabling data-driven decisions and process optimization. Historical data analysis helps identify trends and potential improvement opportunities using advanced anomaly detection software to predict shifts and patterns in production.

Advanced analytics examine machine behavior patterns to predict potential equipment issues before they cause production disruptions. This proactive approach to maintenance helps optimize machine performance, reduce unexpected downtime, and maintain consistent production quality throughout operation cycles. With anomaly detection software powering predictive insights, manufacturers can safeguard production stability.

Expected Business Impact

The solution will reduce defects by 95%, optimize production efficiency by 25%, and save 30% on materials, while enhancing product quality, boosting customer satisfaction, and positioning the company as an industry leader.

Operational Efficiency

Defect Reduction:

Achieve up to 95% reduction in defective product output through early detection

Production Optimization:

Increase overall equipment effectiveness by 25% through predictive maintenance

Material Savings:

Reduce waste by 30% through immediate anomaly identification and correction

Quality Enhancement

Consistency Improvement:

Maintain uniform product quality through continuous monitoring

Customer Satisfaction:

Deliver higher quality products leading to improved customer retention

Brand Reputation:

Establish market position as a quality-focused manufacturer

Competitive Positioning

Market Differentiation:

Lead industry adoption of smart manufacturing and quality innovation

Operational Resilience:

Build robust production systems resistant to quality fluctuations

Technology Leadership:

Establish foundation for advanced manufacturing capabilities

Implementation Strategy

Implement YOLO v10 for accurate defect detection, expand sensor network for comprehensive monitoring, integrate advanced analytics for predictive maintenance, and establish seamless ERP and MES connectivity for optimized production and quality control.

Foundation Development

Computer Vision Deployment:

Implement YOLO v10 models specifically trained for textile manufacturing environments

Accuracy Optimization:

Achieve 99.8% detection precision through continuous model refinement using production data

Adaptive Learning:

Deploy self-improving algorithms that learn from new defect patterns automatically

Sensor Network Expansion

Multi-Modal Detection:

Integrate laser sensors with computer vision for comprehensive needle monitoring

Vibration Analysis:

Deploy accelerometer and gyroscope sensors for predictive maintenance insights

Environmental Monitoring:

Add temperature, humidity, and air quality sensors for complete production environment oversight

Advanced Analytics Platform

Predictive Maintenance:

Develop algorithms that forecast equipment failures before they occur

Process Optimization:

Create data-driven recommendations for production line efficiency improvements

Quality Forecasting:

Implement predictive models for batch quality assessment and yield optimization

Automated Response Systems

Intelligent Alerts:

Develop context-aware notification systems for operators and management

Dynamic Control:

Enable automatic production line adjustments based on detected anomalies

Adaptive Thresholds:

Implement quality parameters that adjust based on real-time production conditions

Enterprise Integration

ERP Connectivity:

Establish seamless integration with enterprise resource planning systems

Manufacturing Execution:

Direct interface with MES platforms for comprehensive production oversight

Quality Management:

Integration with ISO-compliant quality systems for regulatory compliance

Proof of Concept Results

Technology Integration Success

Our proof-of-concept demonstrates effective integration of computer vision, IoT sensors, and automated control systems. The solution successfully combines hardware components with sophisticated software algorithms to create a comprehensive quality monitoring ecosystem.

Performance Benchmarks

Testing results show 99.7% detection accuracy with sub-second response times during controlled scenarios. The system maintains consistent performance across different production speeds and lighting conditions, with zero false negatives recorded during extensive testing periods.

Scalable Architecture

The modular design allows for easy expansion across multiple production lines without performance degradation. Cloud-based processing ensures reliable data handling capabilities while maintaining cost-effective operational overhead as production scales.

Industry Standard Compliance

Full compatibility with Industry 4.0 frameworks enables smooth integration with existing enterprise systems, including ERP, MES, and quality management platforms, ensuring the solution fits naturally into established workflows.

Abstract

A single broken needle in your production line can cost thousands in defective garments, wasted materials, and missed deadlines. SB Infowaves has developed an intelligent IoT solution that detects needle anomalies in real-time, preventing costly defects before they occur. Our system combines YOLO v10 computer vision with smart sensors to transform reactive quality control into proactive manufacturing excellence. The result? Significant cost savings, improved efficiency, and consistently high-quality output that keeps your customers satisfied and your operations profitable.

However, many challenges remain to developing a system that can robustly distinguish PD motor symptoms from normal motion. Stronger feature sets may help to improve the detection accuracy of such a system. In this work, we explore several feature sets compared across two classification algorithms for PD tremor detection. We find that features automatically learned by a Convolutional Neural Network (CNN) lead to the best performance, although our handcrafted features are close behind.

Introduction

Every textile manufacturer knows the hidden costs of production defects. A tilted needle detected too late can result in hundreds of ruined garments, while a broken needle might go unnoticed for hours, silently creating rejects that only surface during final inspection. These scenarios aren’t just costly—they’re entirely preventable with the right technology.

Traditional quality control relies on periodic manual inspections, which means defects are caught after damage is done. What if you could spot problems the moment they occur? What if your production line could monitor itself, alerting you to issues before they become expensive mistakes?

Our IoT-powered anomaly detection system does exactly that. By continuously monitoring needle alignment and condition through advanced computer vision and sensor technology, we’ve created a solution that acts as your production line’s early warning system. This isn’t just about catching problems faster—it’s about preventing them from becoming problems at all. The technology integrates seamlessly with existing manufacturing setups, delivering immediate value without disrupting current operations.