Plane vibration features and degradation history modeling for rotating machinery remaining useful life prediction

Abstract

The ease of using sensors and collecting data provides a great support for industry reliability. Rolling bearings are indispensable components in various machinery systems, and accurately predicting their remaining useful life (RUL) is crucial for ensuring equipment safety and reliability through prognostic health maintenance (PHM). While traditional methods primarily rely on analyzing vibration signals for degradation modeling, they often miss out on crucial insights embedded in planar vibration patterns. In our research, we propose a groundbreaking approach that utilizes planar vibration analysis to capture a comprehensive range of degradation information. We introduce planar vibration features (PVF) which are derived from analyzing the scattering of acceleration vectors on a 2D plane. These PVFs reveal significant correlations with RUL. Meanwhile, unlike conventional methods that rely on single timestamps or windowed samples, our proposed model harnesses the power of multilayer convolutional neural networks (CNNs) to learn from the entire degradation history. To tackle the challenge of early-stage uncertainty, we have devised a weighted mean square error metric. Through validation on the PHM2012 challenge dataset, our approach demonstrates superior accuracy compared to recent methods for RUL prediction. This research sheds light on the importance of capturing multidirectional vibration information for effective RUL prediction, thereby offering a promising avenue for advancing machinery prognostics and maintenance practices.