Tracking and Visualizing Signs of Degradation for an Early Failure Prediction of a Rolling Bearing

TL;DR

This paper introduces a novel predictive maintenance method that visualizes full-spectrum vibration data in 2D maps to detect early signs of machine degradation without requiring training data, enabling timely failure prediction.

Contribution

The paper presents an innovative failure prediction scheme combining full-spectrum vibration data visualization and real-time tracking, requiring no training data and allowing quick deployment.

Findings

Effective early failure detection demonstrated on real-world bearing data.

No training data needed, simplifying implementation.

Real-time visualization aids human understanding and decision-making.

Abstract

Predictive maintenance, i.e. predicting failure to be few steps ahead of the fault, is one of the pillars of Industry 4.0. An effective method for that is to track early signs of degradation before a failure happens. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of full spectrum of the vibration data caused by the machines and data visualization technologies. This scheme is featured by no training data required and by quick start after installation. First, we propose to use full spectrum (as high-dimensional data vector) with no cropping and no complex feature extraction and to visualize data behavior by mapping the high dimensional vectors into a 2D map. We then can ensure the simplicity of process and less possibility of overlooking of important information as well as providing a human-friendly and human-understandable output. Second, we propose Real-Time Data Tracker (RTDT) which predicts the failure at an appropriate time with sufficient time for maintenance by plotting real-time frequency spectrum data of the target machine on the 2D map composed from normal data. Third, we show the test results of our proposal using vibration data of bearings from real-world test-to-failure measurements provided by the public dataset, the IMS dataset.