A hybrid model with a weighted voting scheme for feature selection in machinery condition monitoring

Feature selection method has become the focus of research in the area of engineering data processing where there exists a large amount of high-dimensional data from the high-frequency acquisition system. For high-dimensional data processing, engineers often resort to feature extraction methods and statistical theories to convert the original features into new features. However, the converted data always lose the engineering meaning of the original features and the choice and use of conversion methods are challenging. In this paper, a hybrid feature selection model is presented to select the most significant input features from all potentially relevant features. The algorithm combines a filter model with a wrapper model. In the filter model, four variable ranking methods are used to pre-rank the candidate features. These four methods including Pearson correlation coefficient, Relief algorithm, Fisher score and Class separability, measure features from various angles, which leads to different ranking results. Therefore, a weighted voting scheme is introduced to re-rank features based on the degree of significance of the four methods on the classification error rate of Radial Basis Function (RBF) classifier. In wrapper model, a Binary Search (BS) method and a Sequential Backward Search (SBS) method are utilized to minimize the number of relevant features when promising to keep the classification error rate of RBF classifier below a given threshold. To demonstrate the potential of applying the method to large-scale engineering data processing, a case study is conducted

A multi-step learning approach for in-process monitoring of depth-of-cuts in robotic countersinking operations

Robotic machining is a relatively new and promising technology that aims to substitute the conventional approach of Computer Numeric Control machine tools. Due to the low positional accuracy and variable stiffness of the industrial robots, the machining operations performed by robotic systems are subject to variations in the quality of the finished product. The main focus of this work is to provide a means of improving the performance of a robotic machining process by the use of in-process monitoring of key process variables that directly influence the quality of the machined part. To this end, an intelligent monitoring system is designed, which uses sensor signals collected during machining to predict the amount of errors that the robotic system introduces into the manufacturing process in terms of imperfections of the finished product. A multi-step learning procedure that allows training of process models to take place during normal operation of the process is proposed. Moreover, applying an iterative probabilistic approach, these models are able to estimate, given the current training dataset, whether the prediction is likely to be correct and further training data is requested if necessary. The proposed monitoring system was tested in a robotic countersinking experiment for the in-process prediction of the countersink depth-of-cut and the results showed good ability of the models to provide accurate and reliable predictions