Structures
Impact Technologies develops and deploys solutions for structural health management and prognosis for aerospace structures that uses an optimized suite of integrated COTS sensors coupled with advanced damage detection and modeling algorithms. Impact’s SHM methodology utilizes a monitoring approach based on acceleration and inherent resistivity measurements that are analyzed using advanced signal processing and dispersive wave theory models that capture frequency and orientation dependent wave propagation effects. The acceleration measurements and associated processing modules are used to provide immediate detection and isolation estimates, while energy amplitude and resistivity features from the carbon fibers themselves allow for assessments of damage severity after the impact has occurred.
Through the application of embedded wave theory models and adaptive signal processing algorithms, a more
accurate understanding of the time-frequency behavior of the dispersive waves produced at impact is gained.
Damage localization is performed based on the comparison between the predicted and measured wave group velocities, with a genetic algorithm used to optimize the parameters of a triangulation procedure. This combination of model and feature-based algorithms allows the system to make use of limited, but readily available accelerometer and resistivity data. This procedure also minimizes the learning and modeling difficulties associated with other techniques that are based solely on models or measurements.
Applications have included:
Structural Integrity Prognosis System
Aircraft structural fatigue due to corrosion, stress-corrosion cracking or corrosion-initiated fatigue significantly impact maintenance downtime and structural life limitations of aging aircraft. Both legacy and new air platforms such as the Joint Strike Fighter (JSF), realize that corrosion will likely continue to be a structural challenge that warrants a structural health management system to provide accurate, cost effective assessments of a platform’s current (diagnosis) and future (prognosis) readiness. Impact has developed and customized specific corrosion/fatigue models that can predict failure progression in realistic environments based on material characteristics, usage profiles and environmental conditions.
The approach also addresses prediction uncertainty management in the field where factors such as loading are far less certain and damage state awareness much more imprecise. With the goal of improving the accuracy of useful life estimates or time to inspection, the Impact prognosis approach fuses imperfect state information such as global/local environmental measurements with physics of failure models to enable adaptive prognosis. Under the DARPA Structural Integrity Prognosis System (SIPS) program, a probabilistic corrosion/fatigue model has been adapted though calibration of initial conditions as well as internal state variables given measurements of temperature, and periodic local damage estimates using a Kalman filter. When coupled with the stochastic analysis, the prognostic model output provides time to a given structural damage level with confidence bounds from which informed operational and maintenance decisions can be made.
Self-Diagnosis of Damage in Composite Structures Using Resistivity
Self-diagnosing structures that utilize innovative “material as sensor” concepts is a revolutionary opportunity area critical to both safety and cost-effective application of composites in aerospace applications. The fact that the inherent electrical resistance of carbon-fibers in composite structures change with damage has been documented by several studies leading to some empirical correlations.
Impact Technologies has established quantitative relationships between specific damage type, size and location with the electrical resistivity measurements. An electrode grid pattern is designed for taking the measurements. Based on the implementation of the chosen design grid, electrode measurements are processed with a combination of damage detection and isolation techniques, including neural networks and stochastic classifiers, to detect and isolate all damage types. The figure below compares the location, size and shape of damage predicted using triangulation and neural network analyses using the electrical resistance measurements made on a CFRP panel. The approach uses intelligent analysis of the signatures of the coupled electromechanical response using machine learning algorithms that provide identification and quantification of multiple damage mechanisms.
Structural Impact Damage Detection, Localization and Prediction
Impact Technologies has developed a series of structural monitoring technologies for identifying (a) damage initiation, (b) damage location and (c) severity of damage in structures. The figure below shows the implementation stages for the damage detection and localization process that uses both data-driven and model-based approaches. Specifically, our robust monitoring concept is based on a proven and reliable accelerometer-based sensing system that can provide comprehensive insight into the structure’s observed health state.
Two different, but collaborative approaches have been developed in terms of their respective abilities to detect, localize and assess severities of structural damage events. The first approach is based on advanced signal processing techniques that utilize wavelet packets, high-order moment statistics, neural networks, and a triangulation procedure. This is also referred to as a “feature-based” approach. The second approach is referred to as “model-based” and utilizes either a Finite Element (FE) model or dispersive wave theory equations of motion to evaluate and extract similar impact damage characteristics.
Corrosion Monitoring Platform - CorrSem™
Impact has developed a low-power corrosion sensing module for tracking corrosion and corrosion drivers on stationary and mobile assets that are difficult or expensive to inspect.
The CorrSem™ system is designed to collect, process, analyze and store data from a variety of electrochemical and environmental (T, P, %RH) sensing elements. Electrochemical corrosion sensing techniques include electrical resistance, electrochemical noise, and electrochemical impedance. The CorrSem system is designed to operate for several years from two AA-sized battery cells through the use of low-power circuitry and intelligent sleep modes.
Contact Impact Technologies to discuss potential application of automated corrosion monitoring within your
Condition-Based Maintenance strategy.
Related Technical Publications & Presentations:
Application Solutions
> Aerospace
> Ground Vehicles
> Marine Systems
> Power & Industrial
> Electronic Systems
> Maintenance Management
> Design & Systems Eng.
> Commercial Systems
Integration
 |
Propulsion |
 |
Avionics |
 |
Flight Controls |
 |
Structures |
 |
Drive Train |
 |
Accessories |
Related Technology
> Structural Health Management
