Humanoid Robot Fault Prediction and Remaining Life Estimation- A Survey

Humanoid Robots are very helpful to the current World. These robots play a vital role in various fields. This kind of robot was introduced to the World to reduce the hard work of human beings. This paper discusses a robot's activity based on the sensor's acceleration components. Based on the activity, Robot’s Fault prediction can be done. Different ML models like Naive Bayes, K-Means, K-Nearest Neighbor( KNN), Support Vector Machine(SVM), Support Vector Regression(SVR), Artificial Neural Network(ANN), Random Forest, Decision Tree, and Gradient Boosting are done, and observations are made on accuracy, precision, and all the errors. In recent years there have been many techniques used to analyze fault prediction. In this paper ML models are performed and a comparison of the accuracy of each model is done to analyze the most suitable model. The activity of the Humanoid robot can be analyzed accurately by performing these models.