# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
###### Abstract  
Industrial processes involving manipulator robots require accurate positioning and orienting for high-quality results. Any decrease in positional accuracy can result in resource wastage. Machine learning methodologies have been proposed to analyze failures and wear in electronic and mechanical components, affecting positional accuracy. These methods are typically implemented in software for offline analysis. In this regard, this work proposes a methodology for detecting a positional deviation in the robot's joints and its implementation in a digital system of proprietary design based on a field-programmable gate array (FPGA) equipped with several developed intellectual property cores (IPcores). The method implemented in FPGA consists of the analysis of current signals from a UR5 robot using discrete wavelet transform (DWT), statistical indicators, and a neural network classifier. IPcores are developed and tested with synthetic current signals, and their effectiveness is validated using a real robot dataset. The results show that the system can classify the synthetic robot signals for joints two and three with 97% accuracy and the real robot signals for joints five and six with 100% accuracy. This system aims to be a high-speed reconfigurable tool to help detect robot precision degradation and implement timely maintenance strategies. FPGA; positional accuracy; discrete wavelet transform; industrial robot; degradation; neural networks +
Footnote †: journal: Applied Sciences  
model (HMM) are used to analyze acoustic emissions (EA) and identify failures in a rotate vector reducer (RV). A method based on artificial failure data, random forest regression (RFR), support vector regression (SVR), and deep neural network regression (DNNR) is proposed in [5] to identify overload anomalies in the robot's end effector and their effect on the joints. Moreover, in [6], a simulation of the torque degradation of the actuator of an industrial robot is carried out, where it is determined that the robot controller cannot deal with failures of this type. Other works related to methodologies that deal with mechanical failures in robots can be consulted for gears [7; 8; 9; 10] and RV [11]. Recent research on deep learning and fault detection techniques for robots includes several noteworthy works. In [12], the authors propose a method that combines a sparse auto-decoder with a support vector machine (SVM) to construct a fault detection model for the robot's reducer. They utilize signals obtained from an attitude sensor composed of an accelerometer, gyroscope, and magnetometer. Another study, presented in [13], introduces a method based on a one-dimensional convolutional neural network (1 DCNN) with matrix kernels. This approach is designed to diagnose faults in harmonic reducers used in industrial robots by analyzing vibration signals. In addition, [14] describes a fault diagnosis method that employs Deep Convolutional Neural Networks (DCNN) to detect faults in the robot's joints. The model is trained using a database generated by modeling the actuators and sensors. Meanwhile, [PERSON]. [15] presents a data-driven approach utilizing Deep Residual Neural Networks (DRNN) to detect faults in robot joints, also based on sensor and actuator modeling. Furthermore, [PERSON] and [PERSON] [16] propose a method for detecting joint bearing failures in industrial robots. They utilize deep belief networks (DBN) to analyze the robot joint vibration signals and identify potential issues. On the other hand, there have been works that directly address the problem of positional degradation of industrial robots, e.g., in [3], the dependent errors present in the robot produced by non-ideal deflections of the structure and movement are characterized using polynomials of Chebyshev for an advanced error model. [PERSON] et al. [17] use multivariable regression adjustment (MRA) and deep long short-term memory (DLSTM) to predict and model the displacement of an industrial robot and to estimate the residual error on the end-effector.