Performance Improvement of Classifier in Fault Diagnosis of Rotating Machines Using Sensor Fusion Techniques
S. Meenakshi Sundaram1, M. Saimurugan2
1Meenakshi Sundaram*, Bachelor’s degree in Mechanical Engineering and a Master’s degree in Engineering design.
2Dr. Saimurugan M Associate Professor at Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 1136-1141 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8345088619/2019©BEIESP | DOI: 10.35940/ijeat.F8345.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The shaft, rotor, bearing and gear are the important elements of the rotating machines. Most of the problems in rotating machines are caused due to bearings and shaft. The failure of rotating machine causes production downtime and economic & safety issues. Vibration signal analysis is highly accepted technique in fault diagnosis of rotating machine. For automation of fault diagnosis, machine learning approach has been followed. Machine learning classifies fault based on variation in signatures pattern of the machine. But its effectiveness gets reduced when it is used for multi fault class problem. So in the present work, sound signals are also used along with vibration signals for applying sensor fusion techniques. In sensor fusion, signals from various sensors are fused in three levels such as data fusion, feature fusion and decision level fusion and the fused data sets are used for fault classification using machine learning algorithm. The performance of each technique is studied in detail and compared using classification accuracy. A new method is proposed by combination of fusion techniques to enhance the performance.
Keywords: Machine learning classifies fault based on variation in signatures pattern of the machine.