LAUSR

Industries are increasingly shifting towards unmanned and intelligent systems that require efficient processing and monitoring of structures across various applications, ranging from machine manufacturing to waste disposal. In order to achieve the goal of intelligent processing, it is crucial to accurately classify and differentiate various components and parts. However, existing studies have not focused on simultaneously classifying electro-mechanical machinery components. This poses a challenge as these components, including capacitors, transistors, ICs, inductors, springs, locating pins, washers, nuts, and bolts, exhibit high intra- and inter-class similarity, making their accurate classification a tedious task. Furthermore, many of these components have symmetrical shapes but are asymmetrical among different classes. To address these challenges, this article introduces a new double-single (D-S) pooling method that focuses on the higher resemblance of seventeen electro-mechanical component classifications with minimum trainable parameters and achieves maximum accuracy. The industrial machine component classification model (IMCCM) consists of two convolutional neural network (CNN) blocks designed with a D-S pooling method that facilitates the model to effectively highlight the differences for the higher similar classes, and one block of grey-level co-occurrence matrix (GLCM) to strengthen the classification outcome. The extracted fused features from these three blocks are then forwarded to the random forest classifier to distinguish components. The accuracy achieved by this proposed model is 98.15%—outperforming the existing state of the arts (SOTAs) models, and has 141,346 trainable parameters– hence, highly effective for industrial implementation.