Presenters:

• Reece Teramoto, MathWorks 
• Rachel Johnson, MathWorks
• Bora Eryilmaz, MathWorks

Description:

This interactive demo will introduce you to a variety of statistical and AI-based techniques for analyzing time series data and designing anomaly detection algorithms using MATLAB and Predictive Maintenance Toolbox. Highlights include: 

• Organizing, analyzing, and preprocessing time series sensor data 
• Distance-based approaches for exploring anomalies in historical data 
• One-class machine learning and deep learning approaches for algorithm development 
• Comparing and testing algorithm performance 
• Deploying anomaly detection algorithms in a streaming environment 

Presenters:

• Peter Lucas (PHM Technology)
• Chris Stecki (PHM Technology)

Description:

Discover how to engineer smarter, more reliable systems, from concept to operation, in this live technical demonstration.

We’ll showcase how MADE transforms traditional RAMS and diagnostics engineering into a connected, data-driven workflow that links design, maintenance, and real-time operation. Starting in the design phase, you’ll see how MADE identifies and quantifies technical risk using FMEAs and FTAs, and then applies Reliability-Centered Maintenance (RCM) to define the most effective maintenance strategies. From there, we’ll demonstrate how to design and optimize a sensor suite, ensuring complete failure coverage for condition monitoring, and automatically generate the diagnostic logic needed to detect and isolate faults.

 Then, experience how Syndrome Diagnostics (SD) takes those models into operation. We’ll walk through Exploratory Data Analysis (EDA) to validate diagnostic accuracy and calibrate Failure Detection & Isolation (FDI),  before showing SD in action, working with live operational data.

Finally, you’ll see how Condition-based AI (CbAI) enables real-time, predictive diagnostics, turning data into actionable insights that maximize system availability, reliability, and safety. Join us to see how the MADE ecosystem creates a seamless bridge between design and operation, and experience firsthand how Digital Risk Twin technology enables smarter decisions across the product lifecycle.

Presenters:

• Omar Mendoza, GE Aerospace

Description:

Join a hands-on technology demonstration showcasing how GE Aerospace’s Software as a Service accelerates Safety, Quality, Delivery, and Cost outcomes across operations. First, see Asset Records in action: digitize and ingest millions of technical records, auto-index with OCR and AI/ML, reconcile against M&E systems, publish and package records for audits, lease returns, and MRO collaboration—all with enterprise-grade uptime and integrations. Then, experience FlightPulse, a pilot-centered, data-driven application that turns flight data into actionable insights to improve operational efficiency, safety culture, and fuel performance at scale. In 30–60 minutes, you’ll see how these solutions help airlines, lessors, and MROs move from paper and legacy workflows to trusted, searchable data and intuitive insights—ready for real-world decisions.

Key Asset Records capabilities demonstrated:

• Revolutionized the way Asset Records are utilized while keeping regulator compliance.
• Integrations and collaboration: AeroXchange, API-based M&E link publishing, eTechLogs, MRO Connect for send/receive records packages.
• Scale and reliability: 2.5B+ records, 1M+ records/day, 99.95% uptime, 40k+ end users across 6,200+ aircraft.

Key FlightPulse capabilities (Made for pilots by pilots):

• Secure, private access to individual flight data and peer-relative metrics in Post-flight, creating self-learning and continuous improvement moments.
• Aggregated historical insights in Pre-flight with interactive maps, filters by runway, weather, and time, plus common threats and events to inform safer departures.
• Pilot-friendly dashboards for fuel procedures like Engine Out Taxi In, showing individualized savings, fleet benchmarks, and missed-opportunity highlights.
• Outcomes: carbon emissions avoided, increased adoption of fuel-saving procedures, and strong pilot engagement.

Presenter:

• Leslie Forsberg (Boeing)
• Chip Flory (Boeing)

Description:

Boeing is preparing to leverage immersive virtual reality (VR) training to enhance mechanic proficiency in predictive health maintenance (PHM). VR simulations would recreate realistic aircraft systems and sensor behaviors, enabling technicians to practice diagnostic workflows and engage with scenario-based exercises using sample sensor records and simulated fault scenarios. This training is expected to accelerate skill acquisition, standardize troubleshooting techniques, and reduce time-to-fault identification. The approach is intended to improve maintenance decision quality, support safer condition-based interventions, and contribute to lower unscheduled removals and maintenance costs across the fleet.

Join us to review a high-level demonstration of a representative VR-enabled workflow that illustrates how immersive simulations can support technician skill development and PHM adoption.

Presenters:

• Anand Todkar (Siemens Corp., Technology)
• Jitendra Solanki (Siemens Corp., Technology)

Description:

The Siemens Industrial Edge platform is transforming industrial automation by seamlessly bridging the gap between IT and OT systems. This powerful solution delivers scalable, secure, and flexible edge computing, empowering manufacturers to process data locally, integrate effortlessly with cloud infrastructure, and deploy intelligent applications directly on the shop floor. Key innovations include virtual PLC, enabling hardware-independent control for unparalleled flexibility and scalability. Integrated Industrial AI applications, deployed directly at the edge, leverage advanced anomaly detection and machine learning for early fault detection and robust quality control, significantly reducing operational risks and enhancing overall asset performance. Furthermore, the strategic integration of Large Language Models (LLMs) and Multi Agent systems, expertly guided by a Model Context Protocol, is profoundly revolutionizing industrial operations. Join us to explore the future of smart manufacturing and data-driven decision-making, all consolidated within one Siemens Industrial Edge Platform. Discover how to easily expand existing PHM solutions to similar scenarios using Siemens’ Industrial AI portfolio. This demo and presentation are specifically designed for those who consolidate, manage, and orchestrate existing PHM solutions from the shop floor to the field.

Key components of the Industrial Edge ecosystem include

• Edge Devices & Management: A diverse portfolio of Siemens and third-party hardware, including IPCs, HMIs, and GPU-enabled systems, supports various industrial scenarios
• Edge Apps & Connectivity: A rich marketplace of Siemens and partner-developed apps enables connectivity with over 9,000 device types and supports more than 10 industrial protocols
• Use Cases & Solutions: Real-world implementations demonstrate benefits such as predictive maintenance, energy optimization, machine vision-based quality inspection, and low-code app development with Mendix.
• Industrial Information Hub: Acting as a central data layer, IIH enables semantic modeling, bidirectional communication, and seamless data orchestration across IT/OT layers
• Software-Defined Automation: The introduction of virtual PLCs allows for hardware-independent control, enhancing flexibility and scalability in automation environments
• AI on Edge: Integrated anomaly detection and machine learning capabilities support early fault detection and quality control, reducing operational risks and improving asset performance
• LLM, Model Context Protocol (MCP) & Agentic AI: Exploring the cutting-edge intersection of Large Language Model, Model Context Protocol (MCP) and Agentic AI, we delve into how multi-agent integration, spanning seamlessly from cloud to edge, is revolutionizing industrial intelligence