SSA-Sparse MHD: Singular Spectrum Analysis Paired with Sparse Maximum Harmonics Deconvolution for Detecting Feeble Defect Signals in Industrial Robots

This paper introduces a novel methodology for fault diagnosis in industrial robots, utilizing a combination of Single Spectrum Analysis (SSA) and Sparse Maximum Harmonics-Noise-Ratio Deconvolution (SMHD). The SSA technique is employed to decompose rotary encoder signals into their fundamental components: residual signals, periodic oscillations, and trends. Given that trends and oscillations represent monotonous waves, they are excluded from further analysis. Instead, the residual signal is processed using SMHD, which is an innovation inspired by the Minimum Entropy Deconvolution (MED) approach. This technique employs a new indicator as the objective function, iteratively choosing a Finite Impulse Response (FIR) filter to optimize the Harmonics-Noise Ratio (HNR) of the filtered signal. The proposed approach is designed to detect the flaw duration by computing the HNR of the signal’s envelope. During the iterative process, the fault period can be dynamically refined based on the HNR measurements of the envelope of the continuously updated filtered signal, particularly when the flaw duration is uncertain or challenging to estimate accurately. If an exact fault period is known, it simplifies the process by eliminating the need for estimation and refinement, repeatedly applying the known period in each iteration. A sparse parameter is also implemented to reduce chaos and enhance signal-to-noise ratio (SNR) after each filtering stage. Consequently, the method delineated herein advances as a feature-enhancement technique that does not require prior knowledge, offering substantial benefits and wider applicability compared to traditional method such as the Maximum Correlated Kurtosis Deconvolution (MCKD).