Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain

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Published Sep 22, 2019
Teng Wang, Mr. Zheng Liu Guoliang Lu, Dr.

Abstract

Most condition monitoring systems rely on system-driven generation of indicators or features for early fault detection. However, this strategy requires the prior knowledge on the system kinematics and/or exact structure parameters of monitored system. To address this problem, this paper presents a novel condition monitoring framework where the condition indicator is generated via data-driven method. In this framework, the time-frequency periodogram is extracted from raw vibration signal first. Then, the acquired time-frequency periodogram is mapped by pseudo Perron vector, which is learned from vibration data, to generate the condition indicator. Finally, the bearing can be monitored via analyzing this indicator using gaussian based control chart. Based on experimental results on a publicly-available database, we show the effectiveness of presented framework for early fault detection
in the continuous operation of rolling bearing, indicating its great potentials in real engineering applications.

How to Cite

Wang, T., Liu, Z., & Lu, G. (2019). Bearing Condition Monitoring based on the Indicator Generated in Time-frequency Domain. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.765
Abstract 205 | PDF Downloads 178

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Keywords

Bearing condition motioring, Fault detection, Condition indicator, Time-frequency analysis

Section
Technical Papers