Development of damage identification scheme using de-noised modal frequency response function data with artificial neural network / Mohamad Izzudin Hussein Shah

Mohamad Izzudin , Hussein Shah (2018) Development of damage identification scheme using de-noised modal frequency response function data with artificial neural network / Mohamad Izzudin Hussein Shah. Masters thesis, University of Malaya.

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Abstract

Damaged identification scheme is used to monitor and locate the damage on a structure. Vibration based damage identification scheme which utilise vibrational modal data is popular due to its non-destructive nature. Past researches used natural frequency, mode shapes and damping ratio for their damage identification scheme. These modal parameters are considered as downstream data which is less sensitive and accurate than upstream data. Frequency Response Function (FRF), the upstream data, is directly measured from the vibration sensors has lesser error produced and high sensitivity. Experimental Modal Analysis (EMA) required the machine or system to be shut down, which lead to high downtime cost. Therefore, by applying Impact-Synchronous Modal Analysis (ISMA), the system does not have to be completely shut down, and yet could obtained EMA comparable vibrational modal data through signal de-noising process. On the other hand, by using the recent technology Artificial Neural Network (ANN), it can make any complex nonlinear input-output relationship by just learning from datasets given to it regardless any discontinuity and without any extra mathematical model. In this study, ANN is used to identify damage and its location on an in-service machine by feeding the de-noised ISMA FRF dataset to train and test the model. Thus, this study will be using the FRF data as the ANN input to identify damage on a running machine. Multilayer Perceptron (MLP) with backpropagation learning algorithm ANN is used in this study. Moreover, this study needs to minimize the number of samples used by reducing number of sensors and frequency range used without affecting the performance accuracy. Finding the relationship between sensor location and the performance accuracy by selecting the correct vibration mode is also one of the objective of this study. The experiment setup is done on a rectangular Perspex plate structure to simulate a structure of a vehicle. EMA and ISMA techniques were used to acquire both datasets, whereby later EMA datasets will be used as a training dataset as for ISMA datasets as the testingdatasets. Python language is used in this study and utilized the Keras library with Tensorflow backend. Results shows that this study managed to design a damage identification scheme by using FRF’s datasets with ANN. This study also managed to minimize the number of sensors from nine (9) sensors to a single sensor with a performance accuracy of 100%. Lastly, this study proved that there is a relationship the sensor location and the accuracy of the prediction by selecting the correct vibration mode.

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