# FPGA-Based Methodology for Detecting Positional Accuracy Degradation in Industrial Robots
## 4 Conclusions  
The importance of robotic systems in modern manufacturing processes requires high accuracy and precision to perform tasks with high-quality standards, such as painting, welding, and machining. Achieving this level of precision is critical for the robot's performance, but various factors can degrade it, such as wear and failure of mechanical and electrical components or environmental conditions. In this sense, a methodology for the detection of joint deviation of the robot through the current analysis of the actuators and its implementation in a digital system based on an FPGA is introduced in this work. Synthetic signals, both with and without deviation, are generated using the dynamic model of a UR5 robot. Real-world data from a UR5 robot validates the methodology. To enhance the method's robustness, the signals are contaminated with different noise levels. Then the signals are processed using DWT to separate them into approximation and detail frequency bands, and statistical indicators such as RMS and variance are calculated for each band. Finally, the indicators are used to train and validate a classifier based on neural networks. From the results is concluded that:  
* The proposed method is able to work with signals containing noise due to the use of the DWT to decompose the signal into different frequency bands, which allows an analysis of the signals with a smaller amount of information than the original signal. Its implementation in hardware allows the decomposition and reconstruction of the signal with a single module, which allows better management of FPGA resources. * The statistical indicators used have proven to be effective in the detection of non-visible features in the analyzed signals, they are simple and their hardware implementation has been carried out through a module with a versatile architecture that allows the calculation of other statistical indicators if necessary. * The classifier, based on an MLP network, showed its effectiveness in differentiating between the different operating states of the signals provided by the statistical indicators. In this sense, the classifier structure is simple with a reduced number of neurons in the hidden layer, which makes its implementation in hardware not complex. * For the particular case of joint five of the real case, the result is in line with the findings reported in [53], which also highlights the deviation in that joint. * The designed modules and architectures have been implemented in a low-cost proprietary FPGA-based digital system and have demonstrated the feasibility of the proposed method using about 50% of the resources. In addition to achieving a high processing speed for all joints (less than one second) with minimal error attributed to truncation and fixed point operations. Moreover, the use of an FPGA system allows for concurrent execution and reconfiguration of the device; if an additional module is required, it can be incorporated into the system. Future work proposes the hardware implementation of a method capable of performing the analysis of the positional degradation of the robot using only a classifier based on neural networks, as well as analyzing the classifier's behavior when multiple joints deviate simultaneously. Furthermore, the proposal includes the exploration of other non-linear indicators, signal processing techniques, and methodologies based on machine learning and deep learning. Conceptualization, E.G.-U., R.A.O.-R. and L.M.-V.; methodology, E.G.-U., R.A.O.-R. and L.M.-V.; software, validation, and formal analysis, E.G.-U. and L.M.-V.; investigation, E.G.-U., R.A.O.-R. and L.M.-V.; resources, E.G.-U., R.A.O.-R. and L.M.-V.; writing--original draft preparation, E.G.-U., R.A.O.-R. and L.M.-V.; writing--review and editing, E.G.-U., R.A.O.-R. and L.M.-V.; visualization, E.G.-U. and R.A.O.-R.; supervision, R.A.O.-R. and L.M.-V.; project administration, R.A.O.-R. and L.M.-V.; funding acquisition, R.A.O.-R. and L.M.-V. All authors have read and agreed to the published version of the manuscript.