Digital Twin Generates 70,000 Fault Scenarios to Train Smarter Aircraft Health Monitoring

Aircraft are going electric, and one of the most consequential changes is happening in the hidden machinery of flight control. For a century, moving a flap or aileron meant pushing hydraulic fluid through miles of tubing. The More Electric Aircraft and Power-by-Wire paradigms now replace those networks with electromechanical actuators, or EMAs, devices that convert electrical power directly into precise mechanical motion using motors, gear trains, and leadscrews. The benefits are substantial: less weight, no hydraulic leaks, simpler maintenance, and seamless integration with digital control systems. But the transition creates a stubborn problem for safety engineers. EMAs fail in ways that span electrical and mechanical domains at once, and because the technology is still young, there are almost no real-world failure records to teach diagnostic algorithms what trouble looks like.

A team at Cranfield University’s Integrated Vehicle Health Management Centre, led by Chengwei Wang together with Ip-Shing Fan and Stephen P. King, has now demonstrated a way to manufacture that missing experience artificially. In a study published in Frontiers in Aerospace Engineering, they built a digital twin of an aircraft EMA, a physics-based virtual replica capable of simulating thousands of failure scenarios on demand. The framework, implemented in MATLAB Simulink and Simscape, generated a fully labelled dataset of 70,000 simulation runs covering healthy operation, three single fault modes, and three compound fault combinations. The result is a reproducible benchmark that aerospace researchers can use to train and validate health monitoring algorithms without ever degrading a physical actuator.

The digital twin concept traces its lineage to NASA’s Apollo program, where identical vehicles on Earth mirrored the spacecraft in orbit to help troubleshoot problems millions of miles away. Modernised by Michael Grieves in the early 2000s, the digital twin has evolved into a multi-physics simulation continuously informed by data from its physical counterpart. In prognostics and health management, twins serve two roles: as real-time mirrors for operational monitoring, and as simulation platforms for generating data. For new systems like EMAs, the second role is the urgent one. Machine learning diagnostics are hungry for labelled examples of faults, yet rare failure modes are precisely the events that historical records never capture. Simulation-generated data offers a legitimate foundation, especially when the underlying model is grounded in validated physics.

The Cranfield framework is organised as a four-layer architecture. At the top, an input definition layer supplies voltage and load profiles that mimic realistic flight control demands. Beneath it sits the electromechanical system layer, the heart of the model, which represents the DC motor with physical parameters including internal resistance, inductance, back-EMF constant, and torque constant, alongside a screw-nut transmission built from Simscape Multibody elements that capture nonlinear stiffness, backlash gaps, and damping. A fault injection layer then modifies these parameters dynamically during each run, and a data acquisition layer records voltage, current, power, displacement, velocity, and fault flags at 100 Hz, producing 3,000 data points per signal per 30-second cycle.

To anchor the simulation in reality, the team modelled a 30-second flap extension cycle inspired by the inboard flap of an Airbus A350XWB, using aerodynamic calculations to derive realistic mechanical loads of 8, 10, and 12 kilonewtons across four operational phases. The electrical architecture follows the standard 28-volt DC aerospace power bus, and the motor-gear-leadscrew topology aligns with validated EMA designs reported in the literature. Crucially, the model’s behavioural fidelity was checked against experimental testbeds: when mechanical backlash is injected, the simulated motor current surges above 150 amps while rotor speed dips sharply, reproducing the characteristic current-spike and velocity-drop signature that engineers observe in real jamming and binding events.

The fault catalogue covers three representative single modes and their combinations. Voltage disturbances, labelled FM1, involve a 10-second deviation drawn from a uniform distribution between 24.0 and 29.4 volts, causing torque underproduction and delayed positioning. Mechanical backlash, FM2, is simulated with an effect-based approach: rather than modelling the micro-geometry of gear wear, the framework injects a reverse torque of 50, 100, or 150 newton-metres into the drivetrain, capturing the diagnostic signature at a fraction of the computational cost. Resistance change, FM3, models cable degradation by shifting circuit resistance to one of four levels between 0.3 and 1.0 ohms, altering current draw and heating patterns. Compound faults FM4 through FM6 combine two or all three of these perturbations simultaneously, and because they act within the multi-domain dynamic equations, their interactions emerge naturally from the system’s physics rather than from artificial signal mixing.

The resulting signal behaviours reveal why compound faults are so diagnostically treacherous. Under voltage disturbance alone, displacement tracking falls short during high-load phases and velocity rise times stretch noticeably. Backlash produces stair-stepped displacement lags and oscillatory velocity during direction reversals. Resistance increase elevates RMS current and average power throughout the cycle. But in the triple-fault scenario FM6, the current waveform becomes a chaotic superposition: large spikes driven by backlash events ride on a baseline modulated by concurrent voltage and resistance faults, while the controller’s attempts to compensate for one disturbance are repeatedly disrupted by another. The study shows that fault signatures are not static; at high resistance, backlash spikes are suppressed and unstable, while at low resistance they are sharp and pronounced, meaning any diagnostic system must cope with severity-dependent symptoms.

The dataset construction protocol was designed with machine learning hygiene in mind. For each of the seven operating conditions, 10,000 independent simulation runs were executed, giving strict class balance across all categories. Fault onset time, magnitude, and duration were randomised independently in every run, and only a single 15-second analysis window was extracted per cycle, with no overlapping segments, eliminating the risk of data leakage between training and validation subsets. Every sample carries structured metadata, including fault mode, magnitude, injection timing, load profile identifier, and random seed, so the entire dataset can be regenerated deterministically. Twenty statistical and frequency-domain features were then extracted, and Principal Component Analysis confirmed dense, well-separated clusters for the fault classes, with UMAP visualisation reinforcing the same conclusion.

Classification experiments quantified the dataset’s diagnostic value. Using a Random Forest classifier with three physically meaningful variables, mean current, rotor angular velocity, and worm torque output, accuracy approached 100 percent even with Gaussian measurement noise up to 5 percent added to all signals, while a simpler linear discriminant analysis benchmark still exceeded 96 percent. Restricting the model to current alone collapsed accuracy to roughly 83 percent, with systematic confusion between electrically dominated faults and their mechanically coupled compound counterparts. That degradation is itself informative: it demonstrates the dataset is not trivially separable and that diagnostic power genuinely arises from multi-domain electromechanical interactions, validating the physics consistency of the twin itself.

The authors are candid about the framework’s boundaries. Thermal dynamics, including winding heating and thermal-mechanical coupling, are not yet modelled; the fault injections represent effect-level perturbations rather than progressive wear laws; parameter uncertainty and manufacturing tolerances remain unquantified; and the twin currently operates open-loop without assimilating real operational data. Each of these gaps maps onto a planned extension, from physics-informed degradation models enabling remaining useful life estimation to Bayesian updating and Kalman filtering for closed-loop recalibration. Even in its present form, however, the framework fills a pressing industrial need: a safe, scalable, and fully traceable source of fault-labelled actuator data that bridges the gap between expensive physical test rigs and the algorithms that will one day keep electric aircraft flying. As actuation goes all-electric, twins like this one may become the training grounds where the next generation of aircraft health monitoring is born.

Subject of Research: Digital twin-based fault simulation for aircraft electromechanical actuator health monitoring

Article Title: A digital twin-based fault simulation framework for aircraft electromechanical actuators: method and demonstration

Article References: Wang, C., Fan, I.-S., & King, S. P. (2026). A digital twin-based fault simulation framework for aircraft electromechanical actuators: method and demonstration. Frontiers in Aerospace Engineering, 5, Article 1708392. https://doi.org/10.3389/fpace.2026.1708392

Image Credits: AI Generated

DOI: 10.3389/fpace.2026.1708392

Keywords: digital twin, electromechanical actuator, More Electric Aircraft, fault diagnosis, prognostics and health management, synthetic dataset, MATLAB Simulink, Simscape, machine learning, aerospace engineering, fault injection, predictive maintenance