Real-time digital-twin structural health monitoring and autonomous maintenance system
A structural-health-monitoring system is disclosed for real-time detection and autonomous maintenance of physical structures. The system includes a sensor network comprising at least one strain gauge and one tri-axial accelerometer mounted on the structure to generate real-time sensor signals. A perception module filters and normalizes the signals and extracts numerical features such as peak amplitude and dominant frequency. A digital-twin module maintains a finite-element model updated in response to the extracted features. A data-driven surrogate model predicts sensor behavior and refines itself using machine-learning techniques. An anomaly-detection module computes an anomaly score from model residuals or classifier outputs. Upon exceeding a threshold, a maintenance module initiates a maintenance action, including generating an inspection schedule or issuing a control signal to an autonomous inspection or repair device. A learning module continuously improves system performance using reinforcement learning based on historical outcomes. The system supports predictive diagnostics, robotic repair, and automated optimization for long-term structural integrity.
1 . A structural-health-monitoring and autonomous-maintenance system for a physical structure, comprising:
(a) a multi-sensor network mounted on the structure and including at least one strain gauge, one tri-axial accelerometer and one temperature sensor that stream time-stamped signals to a processing device;
(b) processing device comprising a processor and a non-transitory memory storing instructions that, when executed by the processor, cause the processor to:
(i) filter the signals with a Gaussian-filter and normalize the filtered signals by z-score normalization and
(ii) extract a frequency-domain feature vector by fast-Fourier transform;
(c) instructions executed by the processor to implement a physics-informed neural-network surrogate structural model that receives the feature vector and is retrained by stochastic-gradient descent at intervals of less than one second;
(d) instructions executed by the processor to implement an anomaly score by computing a residual between an output of the surrogate structural model and the feature vector;
(e) instructions executed by the processor to, in response to inspection feedback, adaptively adjust both (i) a sampling interval of the multi-sensor network and (ii) a threshold applied to the anomaly score;
(f) instructions executed by the processor to implement a finite-element digital-twin model that, when the anomaly score exceeds the threshold, locally refines a mesh around a region corresponding to the anomaly score and derives stress-redistribution commands for that region; and
(g) instructions executed by the processor to convert the stress-redistribution commands into
(i) flight-path instructions transmitted to an autonomous aerial drone carrying non-destructive-evaluation sensors and
(ii) actuator-level instructions transmitted to an autonomous crack-sealing robot configured to deposit repair material at the region identified by the digital-twin model.
2 . The system of claim 1 , wherein the processing device stores each aggregated sensor vector, feature set, anomaly score, inspection result and surrogate-model parameter in a historical database for use by the reinforcement-learning module.
3 . The system of claim 1 , wherein a three-dimensional color heat-map is rendered of the anomaly score super-imposed on a geometric model of the structure via a graphical user interface.
4 . The system of claim 1 , retraining of the physics-informed neural-network surrogate model using the historical database after each inspection feedback cycle is triggered.
5 . The system of claim 1 , wherein each sensor of the multi-sensor network further includes a humidity sensor, and the perception module incorporates humidity as an additional input feature.
6 . The system of claim 1 , wherein the physics-informed neural-network surrogate is executed on an edge processor disposed on the structure.
7 . The system of claim 1 , wherein a maintenance schedule is generated that includes an estimated remaining useful life for each region where the anomaly score exceeds the threshold.
8 . The system of claim 1 , wherein the autonomous aerial drone carries both a high-resolution visual camera and an ultrasonic transducer for non-destructive evaluation.
9 . The system of claim 1 , wherein the physics-informed neural-network surrogate receives the temperature sensor data as boundary-condition input to the model.
10 . The system of claim 2 , further comprising a cloud-based analytics server that periodically receives the historical database for long-term trend analysis while the edge processor maintains real-time operation when a cloud connection is unavailable.
11 . The system of claim 2 , wherein the historical database associates each aggregated sensor vector with contemporaneous ambient temperature and humidity values to provide environmental context for model retraining.
12 . The system of claim 1 , wherein the flight-path instructions comprise distinct waypoint sets automatically generated for each of a plurality of autonomous aerial drones so that the drones concurrently inspect mutually exclusive sub-regions around the region identified by the digital-twin model.
13 . The system of claim 12 , wherein, when any autonomous aerial drone deviates from an assigned waypoint set, the maintenance module re-orders the waypoint sets in real time to maintain full inspection coverage by the drone swarm.
14 . A distributed-sensor-swarm structural-health-monitoring system for a physical structure, comprising:
(a) a plurality of sensor agents affixed to the structure, each sensor agent including a strain gauge, a tri-axial accelerometer, a microcontroller with a non-transitory memory and a low-power wireless transceiver, the sensor agents forming a self-healing mesh network;
(b)—instructions stored in the memory of each microcontroller that when executed by the microcontroller, cause the sensor agent to:
(i) locally filter local raw signals with a Gaussian filter-and fast-Fourier transform the filtered signals to obtain a vibration-feature vector,
(ii) compute a likelihood of a crack, and
(iii) broadcast the likelihood of a crack to neighboring sensor agents via the transceiver;
(c) instructions executed by one or more microcontrollers on the mesh to assign a crack-candidate location for each time window when a plurality of sensor agents in a neighborhood broadcast a likelihood of a crack exceeding a threshold;
(d) instructions executed by one or more microcontrollers that, in response to inspection feedback, adaptively adjust both (i) a broadcast interval of the sensor agents and (ii) the consensus threshold; and
(e) instructions executed by one or more processors operatively coupled to the mesh network to convert the crack-candidate location into flight-path instructions transmitted to an autonomous aerial drone swarm carrying non-destructive-evaluation sensors to inspect the crack-candidate location.
15 . The system of claim 14 , wherein each sensor agent further includes a temperature sensor and a humidity sensor, and the likelihood of a crack incorporates temperature- and humidity-normalized vibration features.
16 . The system of claim 14 , wherein the mesh network operates on an IEEE 802.15.4 low-power wireless protocol with dynamic channel hopping.
17 . The system of claim 14 , wherein the crack-candidate location is declared only when at least 70 percent of the sensor agents in the neighborhood broadcast that there is likely to be a crack.
18 . The system of claim 14 , further comprising a historical database that stores, for each time window, the vibration-feature vectors and the corresponding consensus results, the reinforcement-learning policy module using the database to retrain a corresponding control policy.
19 . The system of claim 14 , wherein the likelihood of a crack is computed by a physics-informed neural network executed locally on the micro-controller of each sensor agent.
20 . The system of claim 14 , wherein the flight-path instructions comprise distinct waypoint sets automatically generated for each of a plurality of autonomous aerial drones so that the drones concurrently inspect mutually exclusive sub-regions around the crack-candidate location.
21 . The system of claim 20 , wherein, when an autonomous aerial drone deviates from an assigned waypoint set, the maintenance module re-orders the waypoint sets in real time to maintain full inspection coverage by the drone swarm.
22 . The system of claim 14 , wherein an average energy-consumption metric of the sensor agents is minimized while maintaining a target crack-detection probability.
23 . The system of claim 14 , wherein a machine-readable control signal is transmitted to an autonomous crack-sealing robot configured to deposit repair material at the crack-candidate location.
24 . The system of claim 18 , further comprising a cloud-based analytics server that periodically receives the historical database for long-term trend analysis, while the mesh network continues real-time operation during cloud disconnection.
25 . The system of claim 18 , wherein the physical structure is a trailer.
26 . The system of claim 1 , wherein the physical structure is a trailer.
27 . The system of claim 18 , wherein the physical structure is an antenna.
28 . The system of claim 1 , wherein the physical structure is an antenna.
29 . The system of claim 18 , further comprising instructions executed by the processor to compute a maintenance priority score for multiple inspection locations based on the anomaly score, predicted remaining-useful-life values, and cost-benefit analysis of maintenance actions.
30 . The system of claim 18 , further comprising instructions executed by the processor to compute a maintenance priority score for multiple inspection locations based on the anomaly score, predicted remaining-useful-life values, and cost-benefit analysis of maintenance actions.
31 . A distributed-sensor-swarm structural-health-monitoring system for a physical structure, comprising:
(a) a plurality of sensor agents affixed to the structure, each sensor agent including a strain gauge, a tri-axial accelerometer, a microcontroller with a non-transitory memory and a low-power wireless transceiver, the sensor agents forming a self-healing mesh network;
(b) instructions stored in the memory of each microcontroller that when executed by the microcontroller, cause the sensor agent to:
(i) locally filter local raw signals with a Gaussian filter and fast-Fourier transform the filtered signals to obtain a vibration-feature vector,
(ii) compute an anomaly score, and
(iii) broadcast the anomaly score to neighboring sensor agents via the transceiver;
(c) instructions executed by one or more microcontrollers on the mesh to assign an anomaly-candidate location for each time window when a plurality of sensor agents in a neighborhood broadcast an anomaly score exceeding a threshold;
(d) instructions executed by one or more microcontrollers that, in response to inspection feedback, adaptively adjust both (i) a broadcast interval of the sensor agents and (ii) the consensus threshold; and
(e) instructions executed by one or more processors operatively coupled to the mesh network to convert the anomaly-candidate location into flight-path instructions transmitted to an autonomous aerial drone swarm carrying non-destructive-evaluation sensors to inspect the anomaly-candidate location.
32 . The system of claim 31 , wherein the anomaly is a crack.