# Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier
## 1 Introduction
A neural-network fault diagnosis module and a reinforcement learning-based fault-tolerant control module were used in [14] as fault-tolerant control frameworks for robotic manipulators that are subjected to joint actuator faults. After the actuator fault is detected and diagnosed, the additive reinforcement learning controller will produce compensation torques to achieve system safety and maintain control. Using the proposed method an average accuracy of 97% was achieved. The SVM was used in [15] for fault detection of the robot manipulator and the results were compared with results obtained with ANN. The results showed that the recognition rate was higher in the case of SVM (99.6%). An adaptive neural network model for diagnosing faults (FD) in combination with adaptive, fuzzy, backstepping, variable control (FC) was proposed and used in [16] for fault-tolerant control. The variable structure observer (VSO) was used for the FD technique of the robot manipulator while higher-order VSO (HVSO) was used to solve the chattering phenomenon of VSO. The estimation performance of HVSO was improved by the implementation of a neural network in the FD pipeline. The improvement of FC was achieved using adaptive higher-order variable structure observer (AHVSO) and the results showed 27% to 29% improvement in faults detection when compared to HVSO and VSO methods. The faults in sensors and actuators of the scara robot were detected using ANN and fuzzy logic in [17]. The proposed approach successfully detects and isolates the actuator and sensor faults. The scientific papers in which the same dataset [18] was used as in this research are listed and described below. The self-organizing map (SOM), and genetic algorithm with SOM (GA-SOM) have been used in [19] to detect the fault operation of the robot manipulator. The GA-SOM outperforms the SOM with an achieved classification accuracy of 91.95%. In [20], the performance of base-level and meta-level classifiers were compared on [18]. The ML classifiers that have been used were Naive Bayes, boosted Naive Bayes,bagged Naive Bayes, SVM, boosted SVM, bagged SVM, decision table (DT), boosted DT, bagged DT, decision tree (DTr), boosted (DTr), bagged DTr, plurality voting, stacking meta decision trees, and stacking ordinary decision trees. The bagged Naive Bayes method achieved the highest classification accuracy of 95.24%. The deep convolutional neural networks (DCNN) are used in [21] to detect robot manipulator execution failures using sensor data from force and torque sensors. The best classification accuracy of 98.82% was achieved with one-dimensional CNN followed by two-dimensional CNN with 98.77% classification accuracy. In [22], 24 neural network (NN) architectures with seven learning algorithms were used to predict execution failures. The 10-8-5-4 NN architecture with the Bayesian regularization algorithm achieved a classification accuracy of 95.45%. The multi-layer perceptron (MLP) was used in [23] to predict robot execution failures. The highest classification accuracy with MLP was 90.45%. The deep-belief neural network (DBN) for detection of the robotic manipulator's failure execution was investigated in [24]. The performance of DBN was compared with other standard ML classifiers (C-support vector classifier, logistic regression, decision tree classifier, K-Nearest Neighbor Classifier, MLP, AdaBoost Classifier, random forrest classifier, bagging classifier, voting classifier) and results showed that the approach has a higher detection accuracy (80.486%) than other algorithms. In [3], the MLP, SVM, CNN, and Siamese neural network (SNN) were utilized for the detection of faults during the operation of the robotic manipulator. The highest \(F1-Score\) value (1.0) was achieved with SNN.