This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure.

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A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing

Application of robotics in offshore oil and gas industry- A review Part II

Fault diagnosis and prognosis are some of the most crucial functionalities in complex and safety-critical engineering systems, and particularly fault diagnosis, has been a subject of intensive research in the past four decades. Such capabilities allow for detection and isolation of early developing faults as well as prediction of fault propagation, which can allow for preventive maintenance, or even serve as a countermeasure to the possibility of catastrophic incidence as a result of a failure. Following a short preliminary overview and definitions, this article provides a survey of recent research on fault prognosis. Additionally, we report on some of the significant application domains where prognosis techniques are employed. Finally, some potential directions for future research are outlined.

Failure Prognosis and Applications—A Survey of Recent Literature

Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Hua Wang

Papers

Incipient fault diagnosis of rolling bearings based on adaptive variational mode decomposition and Teager energy operator

Degradation evaluation of slewing bearing using HMM and improved GRU

Fatigue life analysis of slewing bearings in wind turbines

Disturbance compensation based asymptotic tracking control for nonlinear systems with mismatched modeling uncertainties

The effect of environmental medium on fatigue life for u-shaped bellows expansion joints