STRUCTURAL HEALTH MANAGEMENT SYSTEM AND METHOD BASED ON COMBINED PHYSICAL AND SIMULATED DATA

• US 20140058709A1
• Filed: 02/28/2011
• Published: 02/27/2014
• Est. Priority Date: 02/28/2011
• Status: Abandoned Application

• Current Assignee: Critical Materials, Lda.
• Original Assignee: Critical Materials, Lda.
• Inventors: Machado Viana, Jlio Csar, Rodrigues Dias, Gustavo Alexandre, Da Rocha Soares Antunes, Paulo Jorge, Vieira Baptista, Manuel Alexandre, Mendes Ferreira, Nelson Jadir
• Application Number:
  US14/002,090
• Publication Number:
  US 20140058709A1
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G01M 5/0033 by determining damage, crack or wear
• CPC Class Codes: G01M 5/005 by means of external apparatus, e.g. test benches or portable test systems
• CPC Class Codes: G01M 5/0075 by means of external apparatus, e.g. test benches or portable test systems G01M5/005 takes precedence
• CPC Class Codes: G05B 23/0243 model based detection method, e.g. first-principles knowledge model
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06F 30/23 using finite element methods [FEM] or finite difference methods [FDM]

Abstract

System and method to manage the structural integrity of critical systems for decision-making support actions based upon their condition, in a structural health management system, analysing information from physical sensors through a computational model (101) of the system. The invention comprises an optimal sensor placement method, a data acquisition system with multi-sensor capability mounted/embedded in the critical system (100), a sampling algorithm for balanced compacted information flow from sensor to storage, a data exchange channel (12) linking the physical module (100) with the virtual one (101), a simulated model of the components/structures combined with the adequate solver for the actual physics involved on the problem and an optimization tool, a database to store data and manage results, a decision module for diagnostics and prognostics of the component/structure integrity status, and a data treatment and visualisation tools (22). These modules support decision-making actions (11, 23, 25) and new components/structures design (18).

Claims

1. A system for managing structural health, based on combined physical and simulated integrity data, of a critical structure (2) comprising:

a. a physical module (100) placed in said structure (2), which comprises a sensor network (1, 3, 4) mounted or embedded in said structure (2), a hardware platform (6) comprising a data acquisition (7) and sampling and signal processing (8) sub-modules with multi-sensor capability, and an interface (5) to the hardware platform (6);

b. a virtual module (101), which comprises a simulated representative virtual model (24) of the structure (2) combined with a suitable solver (15) able to reproduce the behaviour of the structure (2), and its components, based on the physical information gathered from the physical module (100);

wherein said simulated model (24) was previously established when defining the optimal locations of said sensors.

2. A system according to claim 1 further comprising:

a. a data storage sub-module (9) in the physical module (100);

b. a database (17) in the virtual module (101) storing and managing data, real or simulated;

c. a decision sub-module (19) comprising diagnostics (20) and prognostics (21) sub-modules of the structure (2).

3. A system according to claim 1, wherein the sampling sub-module (8) will trigger data gathering intervals and data acquisition frequency, based on predefined values or on a sporadic but relevant event.

4. A system according to claim 1, wherein signal processing operations are able to be performed in the sampling sub-module (8), in particular averaging, filtering, time-to-frequency transforms, or de-noising.

5. A system according to claim 1, wherein the solver sub-module (15) comprises an analytical or a numerical solution through a metamodel approximation, a Finite Element Method—

FEM, calculator, Finite Volume Method—

FVM, calculator, Boundary Element Method—

BEM, calculator, or a meshless structural calculator.

6. A system according to claim 1, wherein both physical (100) and virtual (101) modules are both mounted or embedded in said structure (2) and connected by a suitable data connection (12).

7. A system according to claim 1, wherein the virtual module (101) is mounted away from said structure (2), and connected to the physical (100) module by a suitable data connection (12).

8. A system according to claim 1, wherein the data connection (12) between the physical (100) and virtual (101) modules is asynchronous.

9. A system according to claim 1 further comprising an output layer sub-module (22), connected to the diagnostic (20) and prognostic (21) sub-modules on the integrity of the structure (2).

10. A system according to claim 1, wherein the simulated representative virtual model (24) of the structure (2) further comprises previously or regularly obtained interrogative procedure data.

11. A system according to claim 1 further comprising an interface for data exchange (10) with the exterior of the system.

12. A system according to claim 1, wherein the virtual module (101), comprises an interface for data exchange (13) with exterior, a data translation (14) tool.

13. A system according to claim 1, wherein the virtual module (101), comprises an optimization tool (16) able to support the damage diagnosis tool (20).

14. A system according to claim 1 further comprising the integrated and simultaneous management of the structural health of several structures/components of a critical system.

15. A system according to claim 1 further comprising the integrated and simultaneous management of the structural health of a set of critical systems.

16. A method for managing structure health based on combining physical and simulated integrity data of a critical structure (2) comprising the steps of:

a. acquiring (7), sampling and signal processing (8) physical integrity data from a plurality of sensors (1, 3, 4) mounted or embedded in the structure (2);

b. calculating and solving (15) a simulated representative virtual model (24) of the structure (2) reproducing the physical status of the structure (2), and its components;

c. combining real data from the two previous steps through an optimization algorithm for diagnosing—

detecting, locating and evaluating the severity—

of material or structure damage,

wherein said sensors (1, 3, 4) position was previously established by an optimization method,

wherein real sensor (1, 3, 4) responses are checked for sensor fault detection before being fed to the simulated virtual model (24),

wherein said simulated model (24) was previously established when defining the optimal locations of said sensors (1, 3, 4).

17. A method according to claim 16, wherein the sensor fault detection is performed in a physical module (100) at the data acquisition system level (17), or it is performed in a virtual module (101) at the data translation level (13), or in both physical and virtual modules (100) and (101), respectively.

18. A method according to claim 16, wherein the sensor (1, 2, 4) positioning on said structure (2) comprises any suitable optimization methodology that minimizes their number, whilst maximizing their sensing capability, being based on the simulated model (24) with a solver (15) representative of the material/structure.

19. A method according to claim 16, wherein the sensor placement optimises the response of the sensor network to excitation, over a range of frequencies, such that an entire, or the maximum, or a predefined set of vibration modes is captured.

20. A method according to claim 16 further comprising the steps of:

a. storing (9) the acquired (7) and sampled (8) data;

b. detecting sensor malfunctions and damages in the physical module (100) at the data acquisition level (7) or/and at the virtual module (101) at the data translation level (14), before inputting data into the RVM sub-module (24);

c. translating, to a suitable format for input into the simulated model (24), the acquired (7), sampled (8) and verified data;

d. determining diagnosis (20) and prognosis (21) of the structure (2) based on said simulated model (24);

e. outputting (22) said diagnostics (20) and prognostics (21) of the structure (2).

21-31. -31. (canceled)