Implementation of Singular Spectrum Analysis in Industrial Robot to Detect Weak Position Fluctuations

A fault or mechanical flaw causes several feeble swings in the position signal. Identification of such swings by encoders can help to identify machine performance and health status and provide a convenient alternative to a vibration-based monitoring system. In operations, the trend is usually several orders higher than the interested magnitude swings, thus increasing the difficulty of identifying feeble swings without signal deformity. Moreover, the swings can be intricate, and the amplitude can be changed under a nonstationary operating condition. Singular spectrum analysis (SSA) for detecting feeble position swings from the rotary encoder signal is suggested in this paper to address this issue. It allows the complex signal of the encoder to be reduced to a variety of explainable noise-containing components, a collection of periodic oscillations, and a trend. The numerical simulation reveals the achievement of the technique. It demonstrates that the SSA is superior to the empirical mode decomposition in terms of accuracy and ability. In addition, rotary encoder signals from the robot arm are evaluated to identify the causes of oscillation at junctions during industrial robot movements. The proposed route for the robotic arm is proven to be feasible and reliable.