1. An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems
- Author
-
Faisal Rehman, Muhammad Jawad Khan, Naveed Riaz, and Syed Irtiza Ali Shah
- Subjects
0209 industrial biotechnology ,General Computer Science ,Computer science ,Reliability (computer networking) ,Feature extraction ,02 engineering and technology ,Ball screw ,Fault (power engineering) ,law.invention ,020901 industrial engineering & automation ,Linear motion system ,classification accuracy ,law ,0202 electrical engineering, electronic engineering, information engineering ,General Materials Science ,reliability ,Bearing (mechanical) ,intelligent hybrid scheme ,business.industry ,feature extraction ,Deep learning ,General Engineering ,Control engineering ,Support vector machine ,Linear motion ,020201 artificial intelligence & image processing ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Artificial intelligence ,business ,lcsh:TK1-9971 - Abstract
Reliability of high precision linear motion system is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective linear drives and load bearings. A fault in these members severely affects the safe working of overall system. This paper gives a reliable intelligent approach to detect and classify faults for linear motion systems based on deep learning methods. Accuracy in faults identification is highly dependent on improved features extraction. For this purpose, a novel Residual Twin CNN (ResT-CNN) is proposed that uses combination of 1-D and 2-D CNN in parallel learning which improves features extraction performance; followed by knowledge base-Remnant-PCA (Kb-Rem-PCA) architecture in combination with multi-class support vector machine (Mc-SVM). This novel hybrid combination proved very effective in accurate faults identification. The performance of proposed methodology was also validated by IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) public bearing dataset. The results confirm the effectiveness of proposed scheme in comparison to existing state of the art techniques.
- Published
- 2021