Condition monitoring of Industrial machine’s transmission elements – MSDF feature extraction approach

Abstract

ABSTRACT Predictive maintenance is a modern Industry 4.0 strategy in which machines are continuously monitored to detect flaws and prevent breakdowns before they occur. A single sensor focuses on a single variable while neglecting larger information components, resulting in poor data quality and an increased risk of issues in critical rotating equipment. Multi-sensory configuration technology has been developed to collect huge amounts of data from a machine in order to enhance monitoring capabilities in terms of precision, resolution, efficiency, resilience, and trustworthiness of the overall system. The goal of this work is to provide an integrated perspective on machine monitoring using the Multi Sensor Data Fusion (MSDF) technique. On four fault bearings, a case study contrasts the results of single and multiple sensors. A feature-level data fusion method is used, in which computations using time-domain vibration signature data are utilised to build a fusional vector, which is then classified using SVM and analysed with Gaussian kernels. The experimental results suggest that the proposed Gaussian kernel with SVM technique outperforms single sensor data interpretation in terms of classification accuracy and generalisation capability. It is an efficient way for finding defects in rotating machinery in excessively noisy environments.