MATEC Web Conf.
Volume 237, 20182018 3rd International Conference on Design, Mechanical and Material Engineering (D2ME 2018)
|Number of page(s)||6|
|Section||Chapter 3: Design Engineering|
|Published online||26 November 2018|
Assessment on Structural Integrity of In-service Machine Using De-noised Vibrational Modal Data and Artificial Neural Network
1 Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
2 Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia
a Corresponding author: firstname.lastname@example.org
The recent oil price drop creates a demand for swift action within oil and gas industry to shift focus from increasing daily production rates, to optimizing existing assets in achieving growth. Industrial machinery, one of the industry’s key asset many times failed due to high amplitude vibration that contributes to accelerated wear and tear and subsequently results in high cycle fatigue failure. As such there is a need to develop a structural integrity assessment for in–service machinery for continuous and safe operation. Vibration–based method such as Experimental Modal Analysis (EMA) is widely used for damage detection on civil and piping system under stationary environment. However, in industrial applications, system shutdown is very costly. EMA is also undesirable in this case due to the dominant ambient and system disturbances on the in–service system. An alternative method called Impact-Synchronous Modal Analysis (ISMA) is developed to perform modal analysis under noisy environment. Applying the ISMA technique in de-noising the non–synchronous disturbances at upstream could generate a cleaner and static–like modal data downstream for analysis. Artificial Neuron Networks (ANN) is then applied extensively in structural damage identification purposes based on changes in modal data due to its excellent pattern recognition ability. By leveraging on the latest technologies, i.e. ISMA and ANN as proposed, it allows real–time monitoring of assets, in this case, the machines, as well as the ability to transform continuous streams of data into useful information to predict damages.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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