The Challenge
Alstom, a global leader in the railway industry, sought to improve efficiency and accuracy in its Operations & Maintenance (O&M) processes. The company needed a digital solution to enhance real-time monitoring and automation for train maintenance operations, ensuring safety, reliability, and cost-effectiveness.
Key challenges included:
- Inefficient train interior inspection methods, relying on manual checks for damage detection, cleanliness, and regulatory compliance.
- Manual and error-prone brake pad dimension measurements, requiring frequent and precise assessments to meet safety and operational standards.
- Time-consuming routine maintenance tasks that involved extensive data entry, paper forms, and disparate monitoring systems.
To address these challenges, Sensfix collaborated with Alstom to implement its AI-powered Sens-Facility Suite, digitizing and automating key O&M tasks.
The Solution
Sensfix implemented its AI-driven Sens-Facility Suite to enhance key maintenance activities at Alstom.
Train Interior Inspection A mobile app enabled maintenance staff to record train cabin videos at specific walking speeds. AI analyzed these videos to detect and categorize various anomalies, ensuring a streamlined repair process. Key inspection tasks included:
- Label Damage Detection: AI identified torn, faded, or peeling labels that needed replacement.
- Seat Wear & Tear Analysis: Computer vision detected rips, stains, and cushion deterioration, classifying damage severity.
- Fastener Alignment Inspection: The system checked if fasteners were properly secured and flagged any misalignments.
- Defective Light Identification: AI pinpointed non-functional lighting within train compartments.
- Fire Extinguisher Label OCR: Automated text recognition ensured fire extinguisher labels were legible and within compliance dates.
- Window Visibility & Scratches Assessment: The system analyzed window conditions, identifying scratches or dirt affecting passenger visibility.
- Hand Support Damage Detection: Wear and tear on handrails were assessed, helping prevent structural failures.
- Red Tag Condition Check: AI verified the presence and positioning of safety tags on door handle boxes.
Brake Pad Dimension Measurement Sensfix introduced a computer vision-based system to automate brake pad wear assessments. A 3D/2D camera in train depots captured live footage, or operators used a mobile app to take photos. Computer vision technology processed these images to precisely measure brake pad dimensions. Calibration with real-life measurements ensured accuracy, while real-time dashboards provided stakeholders with measurement insights and automated alerts. When dimensions fell below predefined safety standards, the system auto-dispatched maintenance requests and updated inventory needs.
Routine Maintenance Digitization Maintenance staff previously relied on paper forms to document data from meters, sensors, and inspections. Sensfix introduced digital forms, barcode/QR scanning, and optical character recognition (OCR) to digitize data collection. These inputs fed into an analytics dashboard, allowing real-time monitoring and reporting. A rule engine was implemented to flag abnormal readings and automatically notify maintenance personnel for immediate attention.
Computer Vision-Based Damage Detection Advanced AI models were deployed to continuously improve brake pad measurement accuracy and detect damage in train interiors. Real-time analysis helped prioritize maintenance tasks, ensuring critical issues were addressed promptly.
Integrated Decision-Making & Predictive Maintenance Sensfix’s real-time data analysis empowered maintenance teams to make informed, proactive decisions. Predictive analytics enabled early identification of potential failures, optimizing resource allocation and reducing unplanned downtime.
The Outcome
The AI-powered Sensfix solution delivered substantial improvements in Alstom’s O&M processes:
- Elimination of Paper-Based Maintenance Workflows: All maintenance records were digitized, reducing manual errors and administrative burdens.
- Enhanced Safety Compliance: Automated brake pad wear monitoring ensured timely maintenance interventions, minimizing operational risks.
- Real-Time Monitoring Across Multiple Geographies: Stakeholders could access asset performance data remotely, improving visibility and decision-making.
- Automated First Responses: AI-driven rules enabled instant alerts and automatic maintenance dispatch, enhancing response times.
- Reduced Mean Time To Repair (MTTR): Faster issue detection and resolution improved train availability and reduced downtime.
- Optimized Resource Allocation: Maintenance personnel focused on critical tasks, reducing unnecessary manual inspections.
- Lower Equipment Maintenance Costs: Predictive analytics minimized unexpected failures, cutting overall maintenance expenses.
Through this partnership, Sensfix helped Alstom revolutionize its train maintenance operations, setting new standards in railway O&M efficiency.