Assessment on Structural Integrity of In-service Machine Using De-noised Vibrational Modal Data and Artificial Neural Network

Abstract

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.