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SkanEye – Medical Equipment Monitoring System

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project-details
project-details

Project Type

IoT Medical Equipment Monitoring

Client

Skanray Technologies, Mysore, Karnataka, India

Duration

7 Months

Task

IoT Gateway Development, Node-RED Integration, Log Processing, Cloud Platform Development, Machine Learning Analytics, Equipment Onboarding

An advanced IoT solution for remote monitoring and predictive maintenance of Skanray's medical X-ray equipment, specifically their Surgical C-Arm systems.

SkanEye transforms equipment maintenance from reactive to proactive through comprehensive data collection, real-time monitoring, and machine learning-powered analytics. The system employs an IoT gateway using Node-RED framework to interface with the equipment's CAN bus, capturing critical communication data, detecting abnormal patterns, and identifying errors before they cause equipment failures. The platform performs sophisticated log file processing from console systems, analyzing events, errors, and patterns to predict component failures.

Additionally, the system monitors and reports poor quality DICOM images to R&D teams for analysis and continuous improvement. Built on a full-stack architecture using Node.js backend, Vue.js frontend, and MongoDB database, the cloud platform provides comprehensive dashboards for equipment status monitoring, alert management, analytics visualization, and equipment onboarding.

The solution enables Skanray to deliver superior customer support through predictive maintenance, reduce equipment downtime, and continuously improve product quality based on field data insights.

The Challenge

Skanray Technologies needed visibility into their deployed Surgical C-Arm X-ray equipment performance in the field to prevent failures, reduce downtime, and improve customer satisfaction. Traditional reactive maintenance approaches resulted in unexpected equipment failures, prolonged service delays, and customer dissatisfaction. The company lacked real-time insights into equipment health, communication errors on the CAN bus, console system logs, and DICOM image quality issues.

The challenge required developing an IoT solution that could interface with proprietary CAN bus protocols, process complex log files for meaningful patterns, implement machine learning algorithms for predictive maintenance, monitor DICOM image quality for R&D feedback, create a scalable cloud platform for managing distributed equipment, and provide actionable insights through intuitive dashboards while handling the complexity of medical device data security and reliability requirements.

Our Solution

Node-RED based IoT gateway for CAN bus integration

Real-time abnormal communication detection and alerting

Automated log file collection and intelligent processing

Machine learning-powered predictive maintenance analytics

DICOM image quality monitoring and R&D reporting

Comprehensive error and failure alerting system

Cloud-based equipment monitoring dashboards

Equipment onboarding and lifecycle management

Pattern recognition for failure prediction

Vue.js responsive dashboard for real-time insights

Development Process

Our development approach focused on creating a robust IoT platform that bridges embedded medical equipment with cloud analytics, enabling proactive maintenance through intelligent data collection and machine learning-powered insights.

01

IoT Gateway & Integration

Designed and deployed IoT gateway using Node-RED framework for flexible data collection from Surgical C-Arm equipment. Implemented CAN bus sniffing capabilities to capture internal communication between equipment components. Created protocols for detecting abnormal communications and error conditions. Developed automated log file collection mechanism from console systems. Established secure data transmission pipeline to cloud platform. Integrated DICOM image quality assessment and reporting workflows for R&D feedback.

02

Cloud Platform Development

Built comprehensive cloud platform using Node.js backend for data ingestion, processing, and API services. Developed Vue.js frontend with real-time dashboards for equipment monitoring, alert visualization, and analytics. Implemented MongoDB database schema for time-series equipment data, logs, events, and maintenance records. Created equipment onboarding workflows for registering and managing deployed systems. Developed machine learning models for analyzing log patterns and predicting component failures. Implemented alerting system for errors, failures, and predictive maintenance recommendations.

03

Analytics & Deployment

Implemented machine learning algorithms for predictive maintenance based on historical patterns and real-time data. Created analytics dashboards visualizing equipment health metrics, failure predictions, and maintenance insights. Developed reporting modules for R&D teams analyzing DICOM image quality issues. Deployed cloud infrastructure with scalability for growing equipment fleet. Conducted field testing and validation of gateway installations. Established monitoring and alerting for both equipment health and platform operations. Trained Skanray teams on platform usage and maintenance workflows.

Project Outcomes

Successfully deployed SkanEye as a comprehensive IoT monitoring solution transforming Skanray's approach to equipment maintenance and customer support. The platform provides real-time visibility into Surgical C-Arm equipment health across all deployment locations, enabling proactive maintenance interventions before failures occur.

Machine learning-powered predictive analytics identify potential component failures early, significantly reducing equipment downtime and improving customer satisfaction. The automated log processing and CAN bus monitoring detect issues that would previously go unnoticed until catastrophic failures.

DICOM image quality reporting provides valuable feedback to R&D teams for continuous product improvement. The cloud dashboards enable support teams to respond quickly to issues with data-driven insights. The solution demonstrates our expertise in industrial IoT, medical device integration, and cloud-based analytics platforms for mission-critical applications.

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