Improvement of an Industrial Robotic Flaw Detection System

Rotary encoders are commonly used for dynamic control and positioning of industrial robots. Results of this study suggested that rotary encoder signal can also be used to monitor the efficiency of industrial robot systems effectively after proper processing. A novel strategy using singular spectrum analysis (SSA) integrated with hierarchical hyper-Laplacian prior prototype (HHLP) is proposed in this study for defect detection in industrial robots. Sparsity-assisted techniques are efficient flaw-based extraction techniques that have been extensively investigated in recent years. However, the selection of an appropriate sparse prior from the point of view of probability theory remains unverified. First, SSA enables the separation of complicated encoding signals to several interpretable components, including a trend, a set of cyclic oscillations, and residual oscillations (noise). Second, we describe HHLP by maximizing posterior probability of robot flaw diagnosis. HHLP is proposed to extract noise interference, cyclic pulse, and harmonic interference from the residual signal using the SSA technique. We infer that the hyper-Laplacian prior in the prototype may present a more efficient prototype flaw than the Laplacian prior. In addition, HHLP incorporates physical characteristics necessary to distinguish harmonic intervention. This study primarily establishes a modern prototype that represents the former dispersed from the perspective of maximizing the probability of the latter. Meanwhile, generalized minimax-concave regularization inductive and kurtosis-based weighted sparse prototypes are compared and spectral kurtosis is used to confirm the efficacy of HHLP. Note to Practitioners—This study aims to solve the problem of industrial robot fault diagnosis during the operation process to avoid production delays. Rotary encoder sensor is attached to each joint to collect raw information and identify the robot position. Data from the rotary encoder sensor can also be used for the efficient health status assessment of the performance of the industrial robot system after proper processing. Therefore, a new approach using singular spectrum analysis combined with hierarchical hyper-Laplacian pre-induced prototype is proposed in this study. The residual signal extracted using the singular spectrum analysis method for processing in a hierarchical hyper-Laplacian pre-induced prototype is used to improve the weak fault feature. We describe a hierarchical hyper-Laplacian preprototype by maximizing the posterior probability of the flaw diagnosis. We introduce a hierarchical hyper-Laplacian prior that integrates physical characteristics to distinguish between harmonic interferences. The distribution of coefficients acquired through other dictionaries or transformations and selection of the optimal priority for the extraction of flaw characteristics will be the foci of future investigations.