Application of Phase Space Warping on Damage Tracking for Bearing Fault

Abstract

Nowadays, the significance of keeping equipment function properly each time is obvious. If equipment fails during its use, it may have disastrous consequences. Estimating remaining useful life (RUL) of equipment is a key to prevent such calamities, improve its reliability, provide security and reduce unnecessary maintenance and operational cost. The evolution and tracking of damage is the foundation of RUL predicting, and also is one of the most important content of mechanical fault diagnosis. Slow-time variable process of mechanical damage would lead the phase space reconstructed by fast-time variable vibrate signals warping. Search the dynamics characteristic law of damage evolution analysis in the phase space, and build the relationship between fast-time variable signals and slow-time variable damage, and then damage evolution tracking is possible. To validate the theory, simulation model of bearing damage evolution is built, the outer-race fault evolution signals is obtained, and the trend of evolution of degradation of bearing fault is described with Phase Space Warping (PSW) theory and Smooth Orthogonal Decomposition (SOD). The results proved the feasibility of the methodology of PSW in damage evolution tracking.

A Hybrid Method to Predict the Remaining Useful Life of Scroll Wheel of Control Rod Drive Mechanism

“…e phase space warping algorithm could establish a linear relationship between the tracking metrics and the actual physical damage by constructing tracking metrics through the phase space changes of the monitored vibration data. At present, the phase space warping algorithm has been applied to system fault identification [24], degradation tracking of simulated bearing signals [25,26], degradation tracking of electromechanical system [27,28], etc. In this paper, the phase space warping method is also used to calculate the damage value of the scroll wheel based on the vibration signals (see references [18,22,23] for details).…”

A Monotonic Degradation Assessment Index of Rolling Bearings Using Fuzzy Support Vector Data Description and Running Time

“…Then it is extended to describe the vibrations of multiple point defects [39]. The above models provide the cyclical shock, and have been successfully utilized to describe faults in rolling element bearings [40–43]: $x(t)=\sum _{i=1}^M{A}_i\cdot s(t-iT-{\tau }_i)+n(t)$where T is the period of impulse, s ( t ) is the vibration waveform and n ( t ) is external noise, A i is the amplitude modulator to simulate the possible modulated situation, τ i is the random fluctuation around average period T .…”