A Model for Robot Arm Pattern Identification using K-Means Clustering and Multi-Layer Perceptron

Abstract

Predictive maintenance of industrial machines is one of the challenging applications in Industry 4.0. This paper presents a comprehensive methodology to identify robot arm (SCARA) movement patterns to detect the mechanical aging of the robot, which is determined by the abnormal movement of the robot arm. The dataset used is two robot arm movements that go from point A to B and then back to point A. Accelerometer data is used to measure the signal of SCARA actions, mainly focus on the non-linear movement. The identification of the movement pattern of the robot arm is made by combining k-means and multilayer perceptron. The proposed approach first extracts valuable features as characteristics of the two datasets from the time domain statistical value parameters. K-means clustering technique is initiated to label the training dataset. In this phase, the elbow curve is used to determine the number of clusters in the dataset, which is 2 clusters. Moreover, the assumption is used to determine which cluster is labeled as a normal and abnormal movement. Hence, a multilayer perceptron approach is proposed to predict the testing dataset. The proposed multilayer perceptron model yields an accuracy of 94.14%, whereas its cross-validation yields an accuracy of 96.12%.