System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes

Abstract

In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from the motor control signal acquired during the operation, diagnosing the current health of each component using the features, and estimating the associated degradation in the robot system’s performance. Finally, a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. The framework is demonstrated using the example of a wafer transfer robot in the semiconductor industry, in which the robot is operated under faults with various severities for two critical components: the harmonic drive and the timing belt. Features are extracted for the motor signal using wavelet packet decomposition, followed by feature selection by considering the trendability and separability of the fault severity. An artificial neural network model and Gaussian process regression are employed for the diagnosis of the components’ health and the system’s performance, respectively.

Linear Axis Guide Rail Misalignment Detection and Localization Using a Novel Signal Segmentation Analysis Technique

“…The algorithm demonstrated that, when applied alongside diagnosis models such as Support Vector Machines (SVM) and k-Nearest Neighbour (kNN), prediction accuracies above 98.5% are achieved. Similarly, the work by [12] showcased the application of an artificial neural network (ANN) and Gaussian process regression model to diagnose the transfer robot's multi-component health condition and system performance. Another interesting application of machine learning is in the work by [13],…”

Research on Motion Control and Wafer-Centering Algorithm of Wafer-Handling Robot in Semiconductor Manufacturing

“…More related research must be conducted on Active Wafer Centering abroad, and the AWC algorithm requires only two sensors to obtain the relative distance between the wafer center and the manipulator. This paper focuses on the motion control and AWC wafer calibration algorithm for semiconductor equipment handling robots, aiming to achieve fast, stable, and efficient transmission and operation in the manufacturing environment [13][14][15][16][17][18][19]. The paper analyzes the transmission mechanism and kinematics of the semiconductor handling robot using the geometric method, and the results form the basis of the AWC algorithm.…”

Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application

“…A total of six research papers in various fields of machine condition monitoring and fault diagnosis including fault prediction, the fatigue analysis of machinery, signal processing, and the classification of faults are presented in this Special Issue. Lee et al [1] proposed a system-level fault diagnosis framework for industrial robots. Useful features are extracted from the motor control signals obtained during operation, the current health status of each component is diagnosed by an artificial neural network, and the related degradation of the robot system's performance is estimated through Gaussian process regression.…”