A Hybrid Intelligent Model for Crack Diagnosis in a Free-Free Aluminium Beam Structure

A Hybrid Intelligent Model for Crack Diagnosis in a Free-Free Aluminium Beam Structure

Sanjay K. Behera* Dayal R. Parhi Harish C. Das

Mechanical Engineering Department, Siksha ‘O’ Anusandhan Deemed to be University, Bhubaneswar, Odisha 751030, India

Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology, Rourkela, Odisha 769008, India

Mechanical Engineering Department, National Institute of Technology, Shillong, Meghalaya 793003, India

Corresponding Author Email: 
3 April 2018
17 June 2018
30 June 2018
| Citation



In a damaged beam structure, vibration characteristics like natural frequencies and mode shapes undergoes a sharp change due to presence of cracks. In the current investigation, a hybrid intelligent model has been proposed for detection of crack in an aluminium beam structure with free-free boundary conditions. A theoretical investigation has been carried out initially to mathematically model the vibrational parameters of a beam structure. The theoretical model is also supported by an experimental investigation using a free-free aluminum beam of specified dimension in presence and absence of crack. The impact of variations in crack depths and crack locations on natural frequency and mode shapes have been studied extensively. The hybrid intelligent model consisted of Fuzzy logic, Genetic algorithm and Rule based technique in different combinations. Relative natural frequencies of the beam structure are fed as inputs to the hybrid model, and relative crack depth and crack locations are generated as the outputs. Finally, the paper also gives an insight into the comparison of vibrational parameters obtained from numerical and experimental result with that of the proposed hybrid intelligent model.


crack, fuzzy logic, genetic algorithm, natural frequency, rule base

1. Introduction
2. Theoretical Overview for the Determination of Vibration Characteristics of the Cracked Free-Free Beam
3. Experimental Setup
4. Results and Discussions From Numerical And Experimental Analysis
5. Fuzzy Logic
6. Genetic Algorithm
7. Adaptive Rule Base Technique
8. Proposed Hybrid Controller
9. Conclusions

[1] Luo W, Shi S, Sun J, Lv Y. (2017). Experimental analysis and numerical simulation on impact response of sand-filled aluminium honeycomb sandwich structure. AMSE Journals-AMSE IIETA publication-2017-Series: Modelling B 86(2): 517-534.

[2] Nan G, Haixin J, Hongliang Z. (2017). The research on flexural behavior experiment of pre-stressed glue-lumber beams after long-term loading. AMSE Journals-AMSE IIETA publication-2017-Series: Modelling B 86(1): 49-62.

[3] Ganguli R. (2001). A fuzzy logic system for ground based structural health monitoring of a helicopter rotor using modal data. Journal of Intelligent Material Systems and Structures 12(6): 397-407.

[4] Sazonov ES, Klinkhachorn P, GangaRao HV, Halabe UB. (2002). Fuzzy logic expert system for automated damage detection from changes in strain energy mode shapes. Nondestructive Testing and Evaluation 18(1): 1-20.

[5] Pawar PM, Ganguli R. (2003). Genetic fuzzy system for damage detection in beams and helicopter rotor blades. Computer Methods in Applied Mechanics and Engineering 192(16-18): 2031-2057.

[6] Chandrashekhar M, Ganguli R. (2009). Uncertainty handling in structural damage detection using fuzzy logic and probabilistic simulation. Mechanical Systems and Signal Processing 23(2): 384-404.

[7] Parhi DR, Kumar DA. (2009). Analysis of methodologies applied for diagnosis of fault in vibrating structures. International Journal of Vehicle Noise and Vibration 5(4): 271-286.

[8] He Y, Guo D, Chu F. (2001). Using genetic algorithms and finite element methods to detect shaft crack for rotor-bearing system. Mathematics and Computers in Simulation 57(1-2): 95-108.

[9] Hao H, Xia Y. (2002). Vibration-based damage detection of structures by genetic algorithm. Journal of Computing in Civil Engineering 16(3): 222-229.

[10] Krawczuk M. (2002) Application of spectral beam finite element with a crack and iterative search technique for damage detection. Finite Elements in Analysis and Design 38(6): 537-548.

[11] He RS, Hwang SF. (2006). Damage detection by an adaptive real-parameter simulated annealing genetic algorithm. Computers & Structures 84(31-32): 2231-2243.

[12] Perera R, Torres R. (2006). Structural damage detection via modal data with genetic algorithms. Journal of Structural Engineering 132(9): 1491-1501.

[13] Panigrahi SK, Chakraverty S, Mishra BK. (2009). Vibration based damage detection in a uniform strength beam using genetic algorithm. Meccanica 44(6): 697.

[14] Pawar PM, Ganguli R. (2005). Matrix crack detection in thin-walled composite beam using genetic fuzzy system. Journal of Intelligent Material Systems and Structures 16(5): 395-409.

[15] Wu Q. (2011). Hybrid fuzzy support vector classifier machine and modified genetic algorithm for automatic car assembly fault diagnosis. Expert Systems with Applications 38(3): 1457-1463.

[16] Lo CH, Chan PT, Wong YK, Rad AB, Cheung KL. (2007). Fuzzy-genetic algorithm for automatic fault detection in HVAC systems. Applied Soft Computing 7(2): 554-560.

[17] Suh MW, Shim MB, Kim MY. (2000). Crack identification using hybrid neuro-genetic technique. Journal of Sound and Vibration 238(4): 617-635.

[18] Saridakis KM, Chasalevris AC, Papadopoulos CA, Dentsoras AJ. (2008). Applying neural networks, genetic algorithms and fuzzy logic for the identification of cracks in shafts by using coupled response measurements. Computers & Structures 86(11-12): 1318-1338.

[19] Kolodziejczyk T, Toscano R, Fouvry S, Morales-Espejel G. (2010). Artificial intelligence as efficient technique for ball bearing fretting wear damage prediction. Wear 68(1-2): 309-315.

[20] Tada H, Paris PC, Irwin GR. (1973). The stress analysis of cracks. Handbook, Del Research Corporation.