Evolutionary approach for fault diagnosis and condition monitoring of machine-structure

L.B. Bhuyar1, S.V. Kshirsagar2

  1 Department of Mechanical Engineering, Prof. Ram Meghe Institute of Technology & Research Badnera, INDIA.
2 Department of Mechanical Engineering, Sinhgad College of Engineering Pune, INDIA.

[1] Corresponding author;
In this paper, an evolutionary approach for fault diagnosis and condition monitoring of a cantilever type of machine structure is investigated. Fatigue crack detection has received a lot of attention in recent years. A surface transverse open edge crack is considered for this study. Actual fatigue crack may behave non-linearly due to opening and closing under vibration. But due to small level of vibrations on structural beams it can be assumed that cracks remain open. To avoid non-linearity, and tackle most practical problems it is assumed that the crack is always open. Natural frequencies obtained from modal analysis as a criterion for crack detection has been extensively used in the past due to their simplicity. However condition monitoring using vibration signature is not straightforward. An efficient technique is necessary to obtain significant results. In this work, the first three natural frequencies were used for the fault detection. The crack parameters were accurately detected using the experimental natural frequencies as input to a genetic algorithm. To investigate the robustness of the proposed method, several experimental cases of cracked beam were considered. It is found that the method is capable of detecting crack in cantilever beam.
Cantilever beam, Fault diagnosis, Condition monitoring, Genetic Algorithm

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