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• W2912741532 abstract "Damaged identification scheme is used to monitor and locate the damage on astructure. Vibration based damage identification scheme which utilise vibrational modaldata is popular due to its non-destructive nature. Past researches used natural frequency,mode shapes and damping ratio for their damage identification scheme. These modalparameters are considered as downstream data which is less sensitive and accurate thanupstream data. Frequency Response Function (FRF), the upstream data, is directlymeasured 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 ModalAnalysis (ISMA), the system does not have to be completely shut down, and yet couldobtained EMA comparable vibrational modal data through signal de-noising process. Onthe other hand, by using the recent technology Artificial Neural Network (ANN), it canmake any complex nonlinear input-output relationship by just learning from datasetsgiven to it regardless any discontinuity and without any extra mathematical model. In thisstudy, ANN is used to identify damage and its location on an in-service machine byfeeding the de-noised ISMA FRF dataset to train and test the model. Thus, this study willbe 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 inthis study. Moreover, this study needs to minimize the number of samples used byreducing number of sensors and frequency range used without affecting the performanceaccuracy. Finding the relationship between sensor location and the performance accuracyby selecting the correct vibration mode is also one of the objective of this study. Theexperiment setup is done on a rectangular Perspex plate structure to simulate a structureof a vehicle. EMA and ISMA techniques were used to acquire both datasets, wherebylater 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 withTensorflow backend. Results shows that this study managed to design a damageidentification scheme by using FRF’s datasets with ANN. This study also managed tominimize the number of sensors from nine (9) sensors to a single sensor with aperformance accuracy of 100%. Lastly, this study proved that there is a relationship thesensor location and the accuracy of the prediction by selecting the correct vibration mode." @default.
• W2912741532 created "2019-02-21" @default.
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• W2912741532 date "2018-06-25" @default.
• W2912741532 modified "2023-09-24" @default.
• W2912741532 title "Development of damage identification scheme using de-noised modal frequency response function data with artificial neural network / Mohamad Izzudin Hussein Shah" @default.
• W2912741532 hasPublicationYear "2018" @default.
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