An Intelligent Hybrid Scheme for Identification of Faults in Industrial Ball Screw Linear Motion Systems

Naveed Riaz,Muhammad Jawad Khan,Faisal Rehman,Syed Irtiza Ali Shah

doi:10.1109/access.2021.3062496

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.

Highlights

Linear motion system (LMS) is the most common choice in precision motion applications, especially where high speed repeatability is desired under load
This paper proposes a novel Residual Twin Convolutional Neural Network (CNN) (ResTCNN) architecture inspired from residual learning scheme
The suggested hybrid scheme was successfully trained to learn features from Ball screw (BS) linear drive dataset as well as IMS-UC dataset for each fault case

Summary

INTRODUCTION

Linear motion system (LMS) is the most common choice in precision motion applications, especially where high speed repeatability is desired under load. In order to deal with aforementioned concerns, this paper proposes data monitoring of BS linear drive using significant signal features from position error measurements These remarkable features represent changes in system dynamics due to any upcoming failure. A novel combination of improved deep learning and knowledge base systems is proposed that gives better feature extraction and accurate faults classification for BS linear drive system. This hybrid arrangement gives superior performance over other techniques.

PROPOSED ResT-CNN ARCHITECTURE

EXPERIMENTATION DETAILS

RESULTS

DISCUSSIONS

CONCLUSION