In the semiconductor manufacturing industry, efficient operation of wafer transfer robots has a direct impact on productivity and product quality. Ball screw misalignment anomalies are a critical factor affecting precision transport of robots. Early diagnosis of these anomalies is essential to maintaining system efficiency. This study proposed a method to effectively diagnose ball screw misalignment anomalies using 1D-CNN and 2D-CNN models. This method mainly uses binary classification to distinguish between normal and abnormal states. Additionally, explainable artificial intelligence (XAI) technology was applied to interpret diagnostic decisions of the two deep learning models, allowing users to convince prediction results of the AI model. This study was based on data collected through acceleration sensors and torque sensors. It compared accuracies of 1D-CNN and 2D-CNN models. It presents a method to explain the model"s predictions through XAI. Experimental results showed that the proposed method could diagnose ball screw misalignment anomalies with high accuracy. This is expected to contribute to the establishment of reliable abnormality diagnosis and preventive maintenance strategies in industrial sites.
This paper investigates the relationship between the preload level of a ball screw drive and the detected natural frequency of the system in an axial direction. A dynamic model to study the preload variation of the system is derived, and then a preload feature is proposed for extracting preload conditions based on the detected natural frequency of the system. A modified double-nut ball screw drive system with adjustable preload level is constructed. This is for the purpose of experimental verification. An accelerometer is attached to the ball screw nuts of the drive system to acquire vibration signals. The signals are analyzed to obtain the natural frequency of the ball screw drive system in an axial direction. By investigating the variation of the detected natural frequency, it is shown that the preload level can be diagnosed by the proposed preload feature. Both the experiment results and mathematical model show a direct correlation between the natural frequency and preload levels. Natural frequency increases when the preload level increases. This study provides a method to monitor the preload of a ball screw system which can be used as an indicator of the health status of the drive system.
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Elastic contact analysis and fatigue life prediction of ball screws in automotive REPS systems Helong Wu, Xinrong Long, Xiaoyan Peng, Zheng Zhang, Minwei Wan, Wei Wu, Wangxin Jiang Mechanics Based Design of Structures and Machines.2025; : 1. CrossRef
A Novel Methodology for Incipient Ball Screw Backlash Measurement Using Capacitive Sensor Marcella Miller, Xu Han, Gregory William Vogl, Anita Penkova, Xiaodong Jia IEEE Transactions on Instrumentation and Measurement.2025; 74: 1. CrossRef
Robust feature design for early detection of ball screw preload loss Xu Han, Marcella Miller, Gregory W. Vogl, Guanyu Chen, Xiaodong Jia Manufacturing Letters.2024; 41: 1225. CrossRef
Detection of inception of preload loss and remaining life prediction for ball screw considering change in dynamics due to worktable position Pradeep Kundu, Marcella Miller, Prayag Gore, Xiaodong Jia, Jay Lee Mechanical Systems and Signal Processing.2023; 189: 110075. CrossRef
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A review of fault diagnosis, prognosis and health management for aircraft electromechanical actuators Zhengyang Yin, Niaoqing Hu, Jiageng Chen, Yi Yang, Guoji Shen IET Electric Power Applications.2022; 16(11): 1249. CrossRef
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Lumped parameters modelling of the EMAs’ ball screw drive with special consideration to ball/grooves interactions to support model-based health monitoring Antonio Carlo Bertolino, Massimo Sorli, Giovanni Jacazio, Stefano Mauro Mechanism and Machine Theory.2019; 137: 188. CrossRef
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