Modeling and Monitoring of Performance Limiting Factors for Ball-screw Linear Motion Systems

Abstract

Reliability of high precision linear motion systems is one of the main concerns in industrial and military systems. The performance and repeatability of these systems are influenced by their respective Ball Screw (BS) linear drives. A fault in these members severely affects positioning accuracy and safe working of overall system. BS linear drives perform flight / application critical job and are responsible to provide precise linear motion while carrying thrust loading. BS drives are specifically designed on the basis of desired operational parameters like power rating, drive torque, slew rate, efficiency, friction, and mechanical backlash etc. These operating parameters significantly affect the functional performance of linear electro-mechanical systems. At present, few techniques are available to monitor BS drives for aerospace and industrial systems. This research works to improve reliability of ball screw drive linear systems by modeling and monitoring the performance factors through analytical redundancy and intelligent deep learning. In the past, some traditional techniques have been employed to address these problems; however these techniques show limitations like insufficient data acquisition, requirement of dedicated model developer and poor domain adaptation. Recently, deep learning techniques have been introduced and are becoming more popular to detect and characterize various fault signal analysis problems due to their robustness and accuracy. The aim of this research is to provide solution of mechanical faults identification and classification problems for BS linear drives. A fault diagnostic algorithm is designed based on dynamic mathematical model and a remnant filter is implemented to detect signal errors. The remnant filter generates residual signal proportional to the error induced. Fault detection thresholds are set and decision logic is established based on position measurement corrections to compare residual signal with the lower and upper pre-defined threshold constants. 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). Current and Position signal data was collected under different load domains. This novel hybrid combination proved very effective in accurate faults identification and classification. The performance of proposed intelligent technique was successfully tested and validated on different datasets including IMS-UC (Intelligent Maintenance Systems – University of Cincinnati) publically published bearing dataset, Paderborn published multi-stage bearing dataset, Current signal dataset for multiple fault modes of BS drive and Position measurement data for multi faults cases for BS linear drive. The actual Signal and Model Fit Simulated Data for BSD system was compared. The testing results proved the effectiveness and superiority of proposed model against different state of the art techniques. The proposed novel framework was also tested for system's stability under different load domains. The results reveal highly competitive values greater than 95% in terms of accuracy and precision for different faults cases.