Hello everyone. Thank you for being here today. Before we dive in, let me quickly introduce myself. My name is Di Duran and I'm a technical architect with over 15 years of experience in IT industry, including the last 10 years, focused specifically on aviation technology over the past decade. I have had the opportunity to work closely on large scale digital transformation projects within the aviation sector, particularly in areas like predictive maintenance, aircraft system integration, and data driven operational optimization. Through this work, I've seen how the industry is evolving. Moving from traditional reactive maintenance models toward smarter predictive systems that leverage real time data and machine learning. One of the most powerful enablers of this shift is digital twin technology, and that's exactly what we will be focusing today, how digital twins are driving. The next revolution in aircraft observability, modern aircraft generates more than 200,000 data points and up to 1.2 terabytes of operational data every single day. That's a huge data. That's a massive opportunity and also a challenge. Digital twin technology unlocks that data's potential by creating virtual replicas of aircraft systems. These twins monitor, simulate and predict aircraft health with failure prediction accuracy of 97.3 percentage. That's not a science fiction that is happening now and today I will show you how this works. What benefits it brings and how industries beyond aviations are adopting the same tool. Let's begin with what makes digital twins so powerful. First, complete visibility. Digital twins replicates the full aircraft environment providing observability across every component, like mechanical, electrical, and environmental. Next one is predictive analysis. Twins can identify failure pattern 28 days in advance, which gives maintenance team a long runway to address issues before they escalate. System integration is key. Twins correlate data from thousands of SOS into one unified operational picture. Finally, real-time monitoring These systems continuously process telemetry data throughout the entire flight phase, not just during inspection. This combination of depth integration and immediacy is what sets digital twins Apart from traditional monitoring system, the impact of unified absorbability is measurable, dramatic. Digital twins reduce the mean time to detect issues by nearly 90 percentage. That is faster identification of problem. Meantime to resolve drops over 76. Percentage meaning issues are not only detected sooner, but resolve faster. There is a 61.8 percentage reduction in emergency maintenance, and perhaps more significantly that is an 84.6 percentage decrease in a OG events, which are extremely costly disruptions. This shift from fragmented monitoring systems to unified digital twin platform creates this kind of operational efficiency. So how do digital twins get such deep insights? It starts with multimodal telemetry, vibration analysis, which uses high frequency sensors to detect tiny deviations down to 0.05 millimeter, often indicating early stage bearing wear. At a 20 K kilohertz sampling rate, no subtile vibration goes unnoticed. Thermal imaging monitors temperature patterns, identifying electrical falls or fluid leaks with 0.1 degree Celsius resolution and full spectrum analysis. We catch problems that traditional sensors miss. Acoustic monitoring brings in sound. AI algorithms analyze frequencies beyond human hearing. With ultrasonic detection and pattern matching anomalies are identified well before they become audible or dangerous. All this data, vibration, heat sound. Are fused by advanced algorithms to create one unified signal even when individual sensors look normal. These algorithms can detect combined patterns that indicates future failure. Let's look at three examples that prove power of digital twins. A major European carrier implemented digital twins across 137 aircraft in one year. They saw a 93 percentage drop in unexpected component failures. They prevented 18 flight cancellation by catching early signs of fuel bump detection. Which is approximately save over 4.3 million Euro in a OG cost and North American cargo operator integrated engine and system monitoring. Despite legacy integration cha challenges, they reduced maintenance cost by 23 percentage and extended component life by 14 percentage. An Asian regional airline used a digital twins to detect a PU failure patent 21 days earlier than traditional systems with AR guided repairs and predictive part management. They cut maintenance turnaround time by 42 percentage. No matter the size or region, digital twins are delivering measurable, written off investment and operational improvement. Digital twins observability relies not just sensor. But also intelligent architecture onboard its computing allows raw data to be processed on the aircraft itself. Even in mid-flight, these distributed nodes can detect anomal immediately. Data filtering algorithms reduce transmission by 87 percentage only the most relevant data gets sent. Saving bandwidth. And avoiding Overloads Secure Transmission System uses multi-channel communication for resiliency for cloud integration, which enables fleet wide data aggregation and analyze on the ground. This edge to cloud architecture en enables continuous observability. Even in remote environments or during connectivity gaps, digital twins data becomes even more powerful when made with visual through augmented reality. Failure probability mapping uses historical and real time data to create component specific risk maps. Technicians knows exactly where to look. Visual translations, which converts complex analytics into simple overlays. No need to interpret drought data. AR procedure guidance provides interactive repair man manuals or instructions directly in the technician's field of view, which reduce error rates by 76.2 percentage. These tools have cut maintenance. By over 34 percentage. While improving consistency and quality, technicians are no longer reacting, they are anticipated. As with any advanced technology, there are challenges. Data accuracy must exceed 98.7 percentage. Recurs constant calibration. A small sensor drift can lead to a false alert or missed problem. Legacy integration is complex. Most airlines uses over 14 plus different legacy systems. Many built decades ago. APIs and middleware become essentials to unify the ecosystem. Telemetry flight is tough. Extreme temperatures and vibrations with demand, redundant sensors and advanced error correction to maintain data fidelity, but by tracking these heads on airlines achieve faster return of investment and greater long-term reliability. Implementing digital twins is not an overnight process, but it can be phased strategically. Assessment phase start by evaluating existing systems and data gaps. Prioritize systems with the highest failure impact. Data foundation, deploy the sensors, build storage, and standardize acquisition protocols. Ensure the data integrity from the start twin development, which creates model of critical system. Train them. Historical data and integrate AI and operational expansion scale to full aircraft and integrate with maintenance systems. The total implementation takes 18 to 24 months, but many airlines see early written of investment within seven to nine months long before. The full rollout is complete. What's exciting is that aviation now influencing other in industries in power generation turbines are monitored with the same techniques used in engines, which is delivering 93.6 percentage prediction accuracy and cutting unplanned outages by 42 percentage. In medical equipment, digital twins predicts scanner maintenance needs, which is reducing scan cancellation by 71.8 percentage in manufacturing production lines, uses predictive analytics to detect processors, which is reducing defects by over 37 percentage. The reliability and safety standards pioneered in aviation are now being applied to industries where uptime is critical, and failure is costly. Let's wrap up with some key takeaways. Digital twins deliver transformational observability with 97.3 percentage predictive accuracy. Integration is the linchpin. Success requires bridging a legacy system, but the written off investment is real. With an 84.6 percentage reduction in a OG events, the investment pays for itself quickly. Looking ahead, technologies like quantum computing. And next gen Mission learning will push prediction accuracy to 99.8 percentage. We also expect broader standardization with organizations like airing, driving adoption across manufacturers and maintenance providers. This will make digital twins easier to implement scale. And share across the industry. Thank you all for your time and attention. The future of aviation maintenance is intelligent, predictive, and interconnected, and digital twins are at the heart of the transformation. Thanks again.