Open AccessTracking and Visualizing Signs of Degradation for Early Failure Prediction of Rolling Bearings
Author(s) -
Sana Talmoudi,
Tetsuya Kanada,
Yasuhisa Hirata
Publication year - 2021
Publication title -
journal of robotics and mechatronics
Language(s) - English
Resource type - Journals
eISSN - 1883-8049
pISSN - 0915-3942
DOI - 10.20965/jrm.2021.p0629
Subject(s) - computer science , process (computing) , predictive maintenance , visualization , scheme (mathematics) , vibration , data mining , tracking (education) , allowance (engineering) , prognostics , artificial intelligence , real time computing , reliability engineering , engineering , mechanical engineering , psychology , mathematical analysis , pedagogy , physics , mathematics , quantum mechanics , operating system
Predictive maintenance, which means detection of failure ahead of time, is one of the pillars of Industry 4.0. An effective method for this technique is to track early signs of degradation before failure occurs. This paper presents an innovative failure predictive scheme for machines. The proposed scheme combines the use of the full spectrum of vibration data from the machines and a data visualization technology. This scheme requires no training data and can be started quickly after installation. First, we proposed to use the full spectrum (as high-dimensional data vectors) with no cropping and no complex feature extraction and to visualize the data behavior by mapping the high-dimensional vectors into a two-dimensional (2D) map. This ensures simplicity of the process and less possibility of overlooking important information as well as provide a human-friendly and human-understandable output. Second, we developed a real-time data tracker that can predict failure at an appropriate time with sufficient allowance for maintenance by plotting real-time frequency spectrum data of the target machine on a 2D map created from normal data. Finally, we verified our proposal using vibration data of bearings from real-world test-to-failure measurements obtained from the IMS dataset.