An inverse damage location problem applied to AS-350 rotor blades using bat optimization algorithm and multiaxial vibration data

Abstract In this study, a damage identification method is proposed using both the finite element method and the bat optimization algorithm applied to the AS-350 helicopter main rotor blade. First, the structure is numerically modeled and evaluated with and without the presence of induced damages. In a second approach, an inverse problem of optimization is constructed in order to identify certain damages in terms of its position and severity level. Three different objective functions are evaluated according to the modal parameters of the rotor blade (vibrations in x, y and z directions). Numerical results, through analysis of variance, showed that local damage significantly modifies the modal response into a non-linear aspect. The modal response used was able to identify, with great efficiency, the actual (noise simulated) damages induced in terms of location and severity. Accordingly, a damage identification method is developed in order to better handle any measurement data (to find/regarding) structural changes (or damages) in complex aerospace structures. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy, even under high measurement noise.

[1]  V. Ho-Huu,et al.  Damage Detection in Laminated Composite Plates Using Modal Strain Energy and Improved Differential Evolution Algorithm , 2016 .

[2]  Radoslaw Zimroz,et al.  Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings , 2018 .

[3]  Sebastiao Simões da Cunha,et al.  An estimate of the location of multiple delaminations on aeronautical CFRP plates using modal data inverse problem , 2018, The International Journal of Advanced Manufacturing Technology.

[4]  Guilherme Ferreira Gomes,et al.  Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks , 2020, Neural Computing and Applications.

[5]  Ranjan Ganguli,et al.  Genetic fuzzy system for damage detection in beams and helicopter rotor blades , 2003 .

[6]  Kenneth Reifsnider,et al.  Characterization and Analysis of Damage Mechanisms in Tension-Tension Fatigue of Graphite/Epoxy Laminates , 1984 .

[7]  Ali Kaveh,et al.  Damage detection based on MCSS and PSO using modal data , 2015 .

[8]  G. F. Gomes,et al.  Dynamic behavior investigation of spot welding machines and its influence on weld current range by modal analysis , 2017 .

[9]  Damodar Maity,et al.  Ant lion optimisation algorithm for structural damage detection using vibration data , 2018, Journal of Civil Structural Health Monitoring.

[10]  Ranjan Ganguli,et al.  Genetic fuzzy system for online structural health monitoring of composite helicopter rotor blades , 2007 .

[11]  Sebastiao Simões da Cunha,et al.  Optimized damage identification in CFRP plates by reduced mode shapes and GA-ANN methods , 2019, Engineering Structures.

[12]  Ting-Hua Yi,et al.  Multi-stage structural damage diagnosis method based on "energy-damage" theory , 2013 .

[13]  Nguyen-Thoi Trung,et al.  Efficiency of Jaya algorithm for solving the optimization-based structural damage identification problem based on a hybrid objective function , 2017 .

[14]  Ting-Hua Yi,et al.  Optimal sensor placement for structural health monitoring based on multiple optimization strategies , 2011 .

[15]  Myeongsu Kang,et al.  Reliable fault diagnosis for incipient low-speed bearings using fault feature analysis based on a binary bat algorithm , 2015, Inf. Sci..

[16]  Sebastiao Simões da Cunha,et al.  A robust optimization for damage detection using multiobjective genetic algorithm, neural network and fuzzy decision making , 2019, Inverse Problems in Science and Engineering.

[17]  Sebastiao Simões da Cunha,et al.  A sunflower optimization (SFO) algorithm applied to damage identification on laminated composite plates , 2019, Engineering with Computers.

[18]  Guilherme Ferreira Gomes,et al.  Development of a 3D reinforcement by tufting in carbon fiber/epoxy composites , 2018, The International Journal of Advanced Manufacturing Technology.

[19]  Jorge Daniel Riera,et al.  Damage detection by means of structural damping identification , 2008 .

[20]  Samir Khatir,et al.  Structural health monitoring using modal strain energy damage indicator coupled with teaching-learning-based optimization algorithm and isogoemetric analysis , 2019, Journal of Sound and Vibration.

[21]  Sebastiao Simões da Cunha,et al.  A multiobjective sensor placement optimization for SHM systems considering Fisher information matrix and mode shape interpolation , 2018, Engineering with Computers.

[22]  SamirĀ Khatir,et al.  Multiple damage detection and localization in beam-like and complex structures using co-ordinate modal assurance criterion combined with firefly and genetic algorithms , 2016 .

[23]  Amar C. Garg,et al.  Delamination—a damage mode in composite structures , 1988 .

[24]  Guilherme Ferreira Gomes,et al.  Sensor placement optimization and damage identification in a fuselage structure using inverse modal problem and firefly algorithm , 2020, Evol. Intell..

[25]  Oral Büyüköztürk,et al.  Structural Damage Detection Using Modal Strain Energy and Hybrid Multiobjective Optimization , 2015, Comput. Aided Civ. Infrastructure Eng..

[26]  Robert D. Adams,et al.  The location of defects in structures from measurements of natural frequencies , 1979 .

[27]  C. M. Mota Soares,et al.  Development of a numerical model for the damage identification on composite plate structures , 2000 .

[28]  Wim Desmet,et al.  Vibration-based damage detection for a composite helicopter main rotor blade , 2016 .

[29]  K. P. Herrmann,et al.  Delaminations induced by constrained transverse cracking in symmetric composite laminates , 1999 .

[30]  Hongnan Li,et al.  A modified monkey algorithm for optimal sensor placement in structural health monitoring , 2012 .

[31]  D. Bray Nondestructive Evaluation , 2018 .

[32]  Ranjan Ganguli,et al.  Helicopter rotor blade frequency evolution with damage growth and signal processing , 2005 .

[33]  Guilherme Ferreira Gomes,et al.  Sensor placement optimization applied to laminated composite plates under vibration , 2018 .

[34]  Magd Abdel Wahab,et al.  A damage identification technique for beam-like and truss structures based on FRF and Bat Algorithm , 2018, Comptes Rendus Mécanique.

[35]  G. Kino Nondestructive Evaluation , 1979, Science.

[36]  John B. Kosmatka,et al.  In-Situ Health Monitoring of Aerospace Structures via Dynamic Strain Measurements , 2019, AIAA Scitech 2019 Forum.

[37]  Nobuo Takeda,et al.  Initiation and growth of delamination from the tips of transverse cracks in CFRP cross-ply laminates , 1994 .

[38]  S. Mohammadi,et al.  Damage tolerance and classic fatigue life prediction of a helicopter main rotor blade , 2016 .

[39]  Guilherme Ferreira Gomes,et al.  A Review of Vibration Based Inverse Methods for Damage Detection and Identification in Mechanical Structures Using Optimization Algorithms and ANN , 2019 .

[40]  Samir Khatir,et al.  Fast simulations for solving fracture mechanics inverse problems using POD-RBF XIGA and Jaya algorithm , 2019, Engineering Fracture Mechanics.

[41]  Zhongqing Su,et al.  A Hybrid Particle Swarm Optimization (PSO)-Simplex Algorithm for Damage Identification of Delaminated Beams , 2012 .

[42]  Ranjan Ganguli,et al.  Structural damage detection in a helicopter rotor blade using radial basis function neural networks , 2003 .

[43]  Xin-She Yang,et al.  Mathematical Analysis of Nature-Inspired Algorithms , 2018 .

[44]  Milad Jahangiri,et al.  The efficiency of a novel identification method for structural damage assessment using the first vibration mode data , 2019, Journal of Sound and Vibration.

[45]  Ling Yu,et al.  A new structural damage detection strategy of hybrid PSO with Monte Carlo simulations and experimental verifications , 2018, Measurement.

[46]  Samir Khatir,et al.  A computational approach for crack identification in plate structures using XFEM, XIGA, PSO and Jaya algorithm , 2019, Theoretical and Applied Fracture Mechanics.

[47]  Nam-Il Kim,et al.  Vibration-based damage detection of planar and space trusses using differential evolution algorithm , 2019, Applied Acoustics.

[48]  Constantinos Soutis,et al.  The effect of delaminations induced by transverse cracks and splits on stiffness properties of composite laminates , 2000 .

[49]  Ling Yu,et al.  A hybrid self-adaptive Firefly-Nelder-Mead algorithm for structural damage detection , 2016 .

[50]  Charles R. Farrar,et al.  A summary review of vibration-based damage identification methods , 1998 .

[51]  Magd Abdel Wahab,et al.  Damage detection in CFRP composite beams based on vibration analysis using proper orthogonal decomposition method with radial basis functions and cuckoo search algorithm , 2018 .

[52]  Dmitri Tcherniak Rotor anisotropy as a blade damage indicator for wind turbine structural health monitoring systems , 2016 .

[53]  Magd Abdel Wahab,et al.  Numerical study for single and multiple damage detection and localization in beam-like structures using BAT algorithm , 2017 .

[54]  Virgínia Infante,et al.  Numerical and experimental study of aircraft structural health , 2020 .

[55]  Leonardo D. Chiwiacowsky,et al.  Variations of Ant Colony Optimization for the Solution of the Structural Damage Identification Problem , 2015, ICCS.

[56]  Peng Xu,et al.  Structural health monitoring based on continuous ACO method , 2011, Microelectron. Reliab..