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

Abstract

Performance degradation assessment based on condition monitoring plays an important role in ensuring reliable operation of equipment, reducing production downtime and saving maintenance costs, yet performance degradation has strong fuzziness, and the dynamic information is random and fuzzy, making it a challenge how to assess the fuzzy bearing performance degradation. This study proposes a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description (FSVDD) and running time. FSVDD constructs the fuzzy-monitoring coefficient ε̄ which is sensitive to the initial defect and stably increases as faults develop. Moreover, the parameter ε̄ describes the accelerating relationships between the damage development and running time. However, the index ε̄ with an oscillating trend disagrees with the irreversible damage development. The running time is introduced to form a monotonic index, namely damage severity index (DSI). DSI inherits all advantages of ε̄ and overcomes its disadvantage. A run-to-failure test is carried out to validate the performance of the proposed method. The results show that DSI reflects the growth of the damages with running time perfectly.

Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications

“…Moreover, some computational intelligence (CI) approaches have been exploited in the process of prognostics. For example, a monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and damage severity index was given in [110]. In [111], an incremental rough support vector data description was designed based on the rough support vector description, and a new assessment indicator was also proposed.…”

An application of nonlinear feature extraction - A case study for low speed slewing bearing condition monitoring and prognosis

“…However, RMS has shortcoming that is unable to provide the information of incipient fault stage while it decreases with the fault development [12]. [10], [11], [12], [15] [20]…”

“…( ) ( ) [12], [15], [17], [19], [20] The DI of RMS, variance, skewness and kurtosis feature are presented in Fig. 5.…”

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

“…In practice, for fault identification problem, the fault feature whose value is monotonic to fault degree is more suitable. 12,13 In terms of thruster fault degree estimation, classification model-based methods act as an important role. They establish a fault degree classification model and then estimate fault degree according to the model.…”