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

The review, led by Sameer Prabhu with co-authors Ravdeep Kour, Pierre Dersin, and Ramin Karim, screened 1,285 research articles published since 2015 and ultimately synthesized 74 studies that applied data-driven Prognostics and Health Management (PHM) to mining processing machines. PHM is the engineering framework that converts raw sensor data into meaningful health indicators, detects anomalies, diagnoses their causes, and predicts how much longer a component can function before failure — its remaining useful life, or RUL. The approach underpins predictive maintenance, where interventions are scheduled based on the actual health of the machine rather than fixed calendar intervals or reactive repairs after breakdown.

The mining processing pipeline itself is a well-established sequence of transformations. Raw ore arriving at the plant undergoes crushing and grinding to reduce large rock fragments into smaller particles, sizing and classification to separate particles into size-based fractions using screens or hydrocyclones, concentration to enrich the ore through methods like froth flotation, gravimetric separation, and magnetic or electrostatic separation, and finally dewatering through thickening, filtration, or drying. Each stage relies on massive mechanical equipment operating under harsh, variable, load-intensive conditions. Components inevitably degrade over time, and failure — the termination of an item’s ability to perform its required function — can expose personnel to hazards while halting production across the entire plant.

What the researchers found is a field heavily skewed toward one narrow slice of the problem. Fault diagnostics — identifying and isolating which component is failing — received the highest research attention, accounting for 49.2 percent of task assignments across the reviewed studies. Fault detection, spotting abnormal conditions before significant damage occurs, came second at 35.6 percent. Prognostics — actually predicting the future state of the system and its remaining useful life — represented just 15.3 percent of the literature. This imbalance means that while substantial progress exists in identifying and classifying faults, predicting RUL under varying loads, heterogeneous material properties, and environmental conditions remains a significant unsolved challenge for mining processing machines in real-world settings.

The sensing landscape is equally lopsided. Vibration signals account for roughly half of all data modalities used across the reviewed studies, dominating the field to a degree that limits what can be observed. Acoustic, torque, and temperature measurements appear far less frequently, while force, belt tension, airflow, and other modalities collectively form the remainder. Multimodal sensing — combining vibration with electrical signatures, thermal imaging, visual, or 3D data — remains uncommon in mining processing machines. This narrow focus restricts the capability of fault detection and prediction for failures that manifest in electrical, hydraulic, pneumatic, control, or software subsystems, which are increasingly relevant in modern mining equipment yet barely represented in the word cloud of investigated assets.

On the modelling side, supervised machine learning and deep learning methods dominate, particularly for detection and diagnostics. Support vector machines, convolutional neural networks, long short-term memory networks, and deep belief networks appear most frequently. These algorithms depend heavily on high-quality labelled datasets — which are scarce in reality given the confidentiality constraints, safety-critical environments, and the predominance of scheduled preventive maintenance practices in mining operations. Half of the reviewed studies relied on public datasets, a quarter on laboratory test rigs, and only around 16 percent used real industrial data. Public datasets most commonly employed are general rotating-machinery bearing collections rather than mining-specific ones, raising serious questions about whether models trained under controlled conditions with stable loads and well-defined faults can generalize to the variable, multi-component load fluctuations, contaminated signals, and unknown disturbances of in-situ mining environments.

Perhaps the most striking finding concerns health management — the decision-making process that translates estimated system health and expected future operating conditions into maintenance and operational actions. Only 27 percent of the reviewed papers explicitly link PHM outputs to decision-making such as maintenance scheduling optimization, spare parts logistics, resource allocation, or cost-risk trade-off analysis. The majority focus on monitoring, detection, or diagnosis without extending their contributions to actionable operations and maintenance planning. This gap means that even where RUL estimates are produced, they rarely inform when to repair, replace, shut down, or reallocate resources — the very decisions that determine system availability and operational continuity in a processing plant.

The review also uncovered a clear methodological progression in the literature, from feature engineering with classical classifiers through convolutional and recurrent architectures to attention mechanisms, graph networks, transformers, and meta-learning. Yet comparative baselines are typically drawn from the same or adjacent model families, and well-tuned classical alternatives are rarely retained as competitive benchmarks. Where classical methods are evaluated directly against learned representations on identical data, they remain competitive — random forest was reported as the strongest performer among models in at least one conveyor bearing study. The literature does not currently establish that architectural advancement is the binding constraint on PHM performance in this domain; data availability and quality appear to be the more restrictive factor.

Looking forward, the researchers identify four research directions ordered by expected practical impact and feasibility. Reducing dependence on labelled data appears the most immediately tractable, as self-supervised learning, meta-learning, and generative approaches are already represented in the corpus and the main constraint is access to data rather than modelling capability. Strengthening health management would connect prognostic outputs to maintenance decisions but requires cost and operational information alongside condition data, demanding closer collaboration with industry. Moving beyond component-centric monitoring depends on multi-sensor infrastructure not yet widely deployed in processing plants. Exploiting emerging technologies — large language models, agentic AI, augmented reality interfaces, LiDAR, and thermal cameras — is the least evidenced direction, with only one of the 74 reviewed studies applying a large language model and none addressing agentic AI or augmented reality.

The stakes could hardly be higher. Mining is increasingly receptive to AI-driven decision support, and the convergence of digital technologies, data analytics, and machine learning has positioned data-driven approaches as a central enabler of intelligent asset management. But this review makes clear that the field remains fragmented and component-centric, with most studies focusing on isolated elements like bearings, gearboxes, or motors rather than the interconnected systems that actually determine plant performance. Until PHM research in mining processing machinery matures from laboratory benchmarks into system-level health management — where prognostics information is translated into maintenance and operational decision-making — the industry will continue leaving availability, safety, and cost improvements on the table. The machines are ready to talk. The question is whether the field is ready to listen at scale.

Subject of Research: Artificial Intelligence

Article Title: Data-driven prognostics and health management for mining processing machinery: a systematic review

Article References: Prabhu, S., Kour, R., Dersin, P., & Karim, R. (2026). Data-driven prognostics and health management for mining processing machinery: a systematic review. Frontiers in Manufacturing Technology, 6, Article 1936330. https://doi.org/10.3389/fmtec.2026.1936330

Image Credits: AI Generated

DOI: 10.3389/fmtec.2026.1936330

Keywords: artificial intelligence, machine learning, deep learning, predictive maintenance, mining, prognostics and health management, industrial IoT, systematic review, Data-driven, prognostics, health, management

The researchers, affiliated with Hefei University of Technology, call their model a physics-informed uncertainty-aware patch-level graph temporal network, abbreviated UP2-GTN. Its central insight is that aero-engines are not clean laboratory objects. They are sprawling, tightly coupled systems whose sensors are buffeted by measurement noise, sudden electrical interference, sensor aging, thermal drift, and constantly shifting operating conditions. Most existing deep learning approaches to RUL prediction, whether built on convolutional neural networks, recurrent architectures, temporal convolutional networks, or Transformers, are optimized for point prediction accuracy under relatively ideal data. When deployed in the messy real world, their reliability can degrade sharply, because the noise and disturbances that pervade actual sensor streams are rarely modeled explicitly.

The new framework attacks uncertainty on three fronts simultaneously: at the level of the raw data, at the level of time, and at the level of the engine’s physical structure. During training, the model deliberately contaminates its inputs with a cocktail of engineered noise types. Gaussian noise mimics the continuous, random measurement errors that accumulate as sensors age. Impulse noise injects high-amplitude outliers resembling electrical interference or sudden external shocks. Scaling noise perturbs signal amplitudes to simulate gain errors, while drift noise adds a slowly growing offset that mirrors the systematic biases sensors develop over years of service. The team even blends these perturbations in weighted combinations, weighting Gaussian and scaling noise most heavily, to produce training data that better reflects the multi-source uncertainty of genuine flight operations.

The temporal innovation is perhaps the most elegant. Degradation in an aero-engine unfolds on multiple time scales at once: slow, global trends of performance decline coexist with sharp, short-lived fluctuations caused by local disturbances. Traditional point-wise models process sensor readings one instant at a time, which allows a single noisy measurement to ripple through an entire long sequence, accumulating error as it goes. UP2-GTN instead slices the sensor history into short, non-overlapping segments called patches. Within each patch, a one-dimensional convolution extracts local temporal features, while auxiliary average and max pooling branches capture the segment’s overall trend and its extreme excursions. A gated fusion mechanism then adaptively balances these dynamic and statistical views, allowing transient anomalies to be dampened before they can contaminate the broader degradation narrative.

The structural layer is where physics enters the picture. Rather than letting a graph neural network freely infer which sensors are related based on statistical correlation alone, a strategy that can manufacture spurious connections when data are noisy, the model builds its sensor graph under explicit physical constraints derived from the engine’s architecture. Sensors are treated as nodes, and edges are permitted only where genuine physical coupling exists: sequential coupling along the airflow path, energy coupling through heat and power transfer between components, and control-feedback coupling imposed by the engine control system. When an intermediate physical variable cannot be directly observed, the physical path is compressed to connect the observable sensor nodes, preserving the dominant dependency pathway. A multi-head attention mechanism then learns the dynamic strength of these physically admissible connections, so the graph can evolve as operating conditions change while never inventing relationships the hardware does not actually support.

Once the patch-level graphs are constructed, a temporal convolutional network models their evolution over time. Causal convolutions ensure that predictions depend only on past information, a requirement for real-time online prognostics, while dilated convolutions expand the receptive field to capture long-range degradation trends without exploding computational cost. The final features pass through convolutional and fully connected layers to produce a single RUL estimate, trained end-to-end with a mean squared error objective. Notably, the framework achieves its robustness without probabilistic output layers or Bayesian machinery; uncertainty is suppressed at the representation and structure level rather than merely quantified at the output.

The experimental evidence is drawn from the two most widely used aero-engine benchmarks, C-MAPSS and its high-fidelity successor N-CMAPSS, which simulate run-to-failure trajectories under single and multiple operating conditions. Across the four C-MAPSS subsets, the model achieved root mean square errors of 11.12, 13.61, 11.09, and 17.30 cycles, outperforming a long list of recent graph-based and Transformer-based competitors on most subsets, with particularly strong gains on the challenging multi-condition FD002 and FD004 scenarios. On N-CMAPSS, the model posted an overall RMSE of 5.57 and, strikingly, a score function of 6669.42 on the aggregated test set, a metric that penalizes late, safety-critical predictions asymmetrically and where the model’s advantage over prior methods was most dramatic.

Ablation studies confirmed that each of the three mechanisms earns its place. Removing uncertainty augmentation, patch-level modeling, or the physics-informed graph constraints each degraded performance, with the patch mechanism proving especially important for stabilizing features under noise. Sensitivity analyses showed that patch length involves a genuine trade-off: short patches preserve fine-grained dynamics but are more vulnerable to impulse noise, while longer patches smooth out transient disturbances at the cost of temporal resolution. Under noise intensities up to ten percent, the simpler single-condition subsets remained remarkably stable, with RMSE fluctuations generally within one cycle, while the multi-condition subsets revealed drift noise as the most damaging disturbance, precisely because its slow accumulation corrupts the long-term degradation trends the model is trying to learn.

Perhaps most importantly for real-world deployment, the model is computationally modest. It contains roughly 305,000 trainable parameters, requires under five million floating-point operations per input window, and delivers inference in about 2.3 milliseconds per sample on a consumer-grade GPU, with peak inference memory of just 173.5 megabytes. That combination of accuracy, robustness, and efficiency makes the approach a plausible candidate for onboard or near-line prognostic systems. The authors acknowledge that challenges remain, including adaptively selecting degradation scales and modeling richer uncertainty and component coupling, but the study marks a meaningful step toward predictive maintenance systems that do not just forecast failure, but know how much they can trust their own forecast when the data grow noisy.

Subject of Research: Physics-informed deep learning for uncertainty-aware remaining useful life prediction of aero-engines

Article Title: Physics-informed uncertainty-aware patch-level graph temporal network for aero-engine remaining useful life prediction

Article References: Peng, P., Fan, X., Lin, B., Sun, X., Yin, Z., & Guo, Z. (2026). Physics-informed uncertainty-aware patch-level graph temporal network for aero-engine remaining useful life prediction. Results in Engineering, 32, Article 113421. https://doi.org/10.1016/j.rineng.2026.113421

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113421

Keywords: aero-engine, remaining useful life, predictive maintenance, graph neural network, uncertainty quantification, physics-informed machine learning, temporal convolutional network, sensor noise, prognostics and health management, C-MAPSS, N-CMAPSS, deep learning

The researchers argue that the economic promise of reusability cannot be realized unless reliability, operability, and cost-effectiveness are engineered into the vehicle from the earliest design phase. Retrofitting a maintenance philosophy onto a finished rocket, they contend, leads to inefficiency and unnecessary expense. Their starting point is an observation drawn from decades of experience with high-reliability mechanical systems such as aerospace engines: it is neither practical nor economical to re-evaluate the reusability of every single component after every mission. Some parts degrade quickly and predictably, others fail rarely, and still others show no meaningful wear at all. Treating them all identically wastes resources on components that need little scrutiny while potentially under-serving those that need the most.

To resolve this, the team built an integrated maintenance framework that classifies components into four categories based on the severity and frequency of their failures, using NASA engine data as the evidentiary foundation. Components are sorted into On-Condition, Hard-Time, Servicing-Lubrication, and Non-Significant groups, each carrying a corresponding level of maintenance activity. The classification approach draws on the principles of reliability-centered maintenance, a discipline formalized in the aviation industry and popularized by John Moubray’s influential 1997 work, which holds that maintenance policies should be dictated by the failure characteristics of each item rather than by a uniform schedule applied across the board.

The On-Condition category is the most demanding. These are components whose health cannot be guaranteed by a fixed calendar or usage limit alone, but must instead be verified through inspection and measurement before each subsequent flight. For these parts, the researchers apply condition-based maintenance, an approach in which sensors and diagnostic algorithms determine whether a component retains sufficient remaining useful life, commonly abbreviated RUL, to fly again. The Hard-Time category covers items with well-defined life limits that are replaced or overhauled after a set number of cycles regardless of apparent condition, while Servicing-Lubrication items require routine replenishment and upkeep. Non-Significant components, finally, are those whose failure history and consequences justify minimal dedicated maintenance activity.

As a representative case study for the On-Condition class, the authors selected the fuel supply system of a launch vehicle, the subsystem that typically demands the most intensive maintenance effort. The fuel supply system is a natural choice for such scrutiny: it comprises turbopumps, valves, lines, and pressurization hardware operating under extreme pressures, cryogenic temperatures, and violent vibration, and a failure here can be catastrophic. The paper develops a detailed scenario for assessing the effective remaining useful life of components within this system, showing how sensor data gathered during and after a flight can be fed into a condition-based maintenance workflow to produce a defensible go or no-go decision for the next launch.

The scenario rests on a foundation of failure mode and effect analysis, a structured engineering method for enumerating the ways a component can fail, the mechanisms behind those failures, and their consequences. By pairing this analysis with sensor-based health monitoring, the framework links each critical failure mode to observable physical indicators, such as pressure anomalies, temperature excursions, or vibration signatures, that can be tracked across flights. Prognostics and health management, the broader field to which this work contributes, has matured rapidly in recent years, with techniques ranging from Kalman filter-based fault detection to particle-filter diagnosis of rocket engine startup transients appearing throughout the recent literature on liquid propellant engines.

What distinguishes the Korean team’s contribution is its insistence on a complete, rational procedure rather than an isolated technique. Previous studies have advanced individual pieces of the puzzle, including model-based and data-driven simulators for rocket engine health management, multi-physics system-level modeling for electromechanical actuators, and reliability-centered maintenance frameworks designed to cope with operational uncertainty. By assembling maintenance classification, reusability assessment, and a concrete sensor-based evaluation scenario into a single coherent workflow, the new paper offers launch operators a template they can adapt to their own vehicles. The authors anticipate that this integrated procedure will enhance both the cost-effectiveness and the reliability of RLVs, directly supporting the ambitions of the so-called New Space era.

The stakes are considerable. Launch cost analysis has repeatedly shown that the price of reaching space is the dominant constraint on the growth of the commercial space industry, and reusable systems are the most credible path to orders-of-magnitude reductions. Regulatory frameworks are evolving in parallel, with the United States Federal Aviation Administration’s Part 450 rules establishing launch and reentry licensing requirements that implicitly demand rigorous mission assurance practices from reusable vehicle operators. Standards guidance for the structural, propulsion, mechanical, and dynamics aspects of reusable launch vehicles has been developed within the aerospace community, and studies of mission assurance standards for both expendable and reusable vehicles highlight how much depends on disciplined post-flight evaluation. A repeatable, component-level reusability assessment procedure gives operators and regulators alike a common engineering language for those decisions.

The research is also notable for its provenance. It results from the Commissioned Research Project on the Advancement of the Korean Launch Vehicle Program, hosted by the Korea Aerospace Research Institute with funding from the Korea AeroSpace Administration, with additional support from the National Research Foundation of Korea and the Brain Korea 21 FOUR project at Korea Aerospace University. As Korea develops its own launch capabilities, including the KSLV-II program whose 75-ton engine system has been the subject of prior reliability modeling, the transfer of maintenance science from commercial aviation into launch vehicle engineering reflects a maturing domestic space sector preparing for reusable systems of its own.

For the industry at large, the message of the study is that reusability is not a binary property of a rocket but a property that must be assessed, component by component, flight after flight, with the intensity of scrutiny matched to the risk each part carries. The fuel supply system, sitting at the sharp end of that risk, illustrates the approach in action: sensors watch for the signatures of degradation, condition-based maintenance converts those observations into estimates of remaining useful life, and only components with demonstrated margin are cleared to fly again. If frameworks of this kind take hold, the vision of rockets operating more like airliners, turned around quickly and cheaply with maintenance targeted precisely where the data says it is needed, moves one step closer to routine reality, and the cost curve that has already transformed access to orbit may bend further still.

Subject of Research: Reusability assessment and condition-based maintenance of reusable launch vehicles

Article Title: Assessment Procedure of Reusability of Reusable Launch Vehicles (RLVs) and Sensor-Based Evaluation Scenario in Fuel Supply System

Article References: Park, H.-B., Choi, J.-H., & Yoon, H.-S. (2026). Assessment Procedure of Reusability of Reusable Launch Vehicles (RLVs) and Sensor-Based Evaluation Scenario in Fuel Supply System. International Journal of Aeronautical and Space Sciences. https://doi.org/10.1007/s42405-026-01305-5

Image Credits: AI Generated

DOI: 10.1007/s42405-026-01305-5

Keywords: reusable launch vehicles, reusability assessment, condition-based maintenance, remaining useful life, fuel supply system, prognostics and health management, reliability-centered maintenance, failure mode and effect analysis, rocket engine maintenance, New Space, launch costs, aerospace engineering