# Neural Network-Enhanced Fault Diagnosis of Robot Joints
## 1 Introduction  
With the wide application of informatisation, digitalisation and intelligitisation technologies, the automation level of manufacturing enterprises has been significantly improved, and flexible production lines aiming at rapid automated machining of multi-species precision parts have been proposed, which is of great significance and value for the transformation and upgrading of the manufacturing enterprises' production methods, equipment manufacturing capabilities and product performance [1]. In the processing of flexible production line, it is particularly important to maintain the product quality and quantity, and improve production efficiency, which directly affect the production efficiency and economic benefits of enterprises [2]. In the past decades, a large amount of research has been devoted to production systems, focusing on modelling, performance analysis, bottleneck identification, lean design, product quality inspection and production control of production systems [3]. However, these macro-analyses are based on industrial robots, as the most important players in the flexible production line, with their advantages of high efficiency, multi-functionality, and high operational accuracy as an indispensable part of the flexible production line, and the accuracy of the robot arm's movement trajectory is directly related to the product quality, production efficiency, and economic benefits of the entire flexible production system [4]. The kinematic accuracy of the robot is the basis for the accurate execution of the robot's movements, and kinematic reliability is a key indicator for evaluating the kinematic performance of the robot. In [5, 6], the end-effector of manipulator positional accuracy errors mainly include structural and joint kinematic parameter errors, such as temperature errors, arm shaft deformation, joint flexibility, gear clearance, wear, etc., which ultimately affect the robot kinematic parameters directly or indirectly. In [7], the reliability of motion accuracy is defined and factors affecting motion reliability represented by link flexibility factors, joint flexibility factors and joint clearance factors of the manipulator are proposed. These three factors are considered to be intermediate to the various internal and externalinfluences on the kinematic parameters of the robot. In other words, any influencing factor first affects the linkage flexibility factor, the joint flexibility factor and the joint clearance factor, and then affects the kinematic parameters, leading to a decrease in the reliability of the robot arm. Figure 1 shows the forward and inverse fault analysis processes. In this context, fault diagnosis is particularly important, the specific joint in question is known by analysing the error in the end-effector of the manipulator, and then the three parameters of that joint are calculated and compared to eliminate the influencing factors and improve the reliability of the motion accuracy. And, based on the back propagation neural network and the trajectory of the robotic arm, it is possible to diagnose faults in specific joints. The aim of this research is to develop and validate a neural network-based robotic arm joint fault diagnosis system by monitoring the position and attitude data of end-effector in real time, in order to achieve accurate identification and analysis of robotic arm joint faults so as to improve the operational efficiency and safety of the robotic arm. Several research questions exist in neural network-based fault diagnosis of robotic arm joints:  
1. How to use neural networks effectively to diagnose joint failures in robotic arms;
2. How to build the database needed for neural network training, validation and testing;
3. How to inject faults and enable neural networks to predict faulty joints;
4. What metrics to select to evaluate the performance of neural networks.