This work presents an investigation for a number of mechanical conditions (healthy, imbalance, misalignment, gear fault, bearing fault). The vibration conditions are simulated and based on real-time vibration data that are acquired from a Machinery Fault Simulator (MFS). The diagnosis process passes through two main stages after the data acquisition, where then the Continuous Wavelet Transform (CWT) method is applied to pre-process the obtained datasets (signals), and extract the features based on five statistical parameters namely: RMS, Kurtosis, Peak, Impulse Factor and Shape Factor. Then Artificial Intelligence (AI) based classification is applied using the Multilayer Feed-Forward Perceptron Neural Network (MLP) using different cases..
Major Objectives:
(i) To improve rotating machine fault detection by incorporating the criticality and its usage (ii) To detect operation changes using key performance indicators together with a data mining, clustering algorithm for machinery diagnostics and prognostic purpose (iii) To extract meaningful condition indicators from vibration and other performance signals from the machine and process them in a most effective manner using wave let analysis to extract fault features (iv) To develop an automated fault detection system to identify Performance Issues or anomaly with Machine based on Machine learning technique.
Research Team:
1. Dr. Moammer Ali Saud AL-Tubi 2. Prof. Dr. K.P.Ramachandran 3. Dr. Amuthakannan 4. Mr. Rene Sinogaya Pacturan 5. Mr. Saleh Salim Khamis Al Araimi 6. Dr Geetha Achuthan