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

A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings

An 8-point-contact slewing bearing is usually applied with strict requirements. The load distribution over raceways is the fundament to calculate both the static and dynamic capability of a slewing bearing. The paper discussed a method for calculating the load distribution in an 8-point-contact slewing bearing under general load conditions (turnover moment, axial load and radial load). Based on the method, some suggestions were proposed for optimization of the slewing bearing design.

Load Distribution over Raceways of an 8-Point-Contact Slewing Bearing

Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods

In this paper, a three-dimensional numerical simulation (using ABAQUS) of the radial and tangential wear between a ball bearing and a raceway is presented. The aim was to study the influence of the...

Pitch bearing/raceway fretting: Influence of contact angle:

Abstract For motion control of uncertain servomechanisms, nonlinear dynamics including smooth and nonsmooth types, external disturbances, signal measurement noises, asymmetric input saturation, and so on seriously hinder the further development of high-performance closed-loop control algorithms. However, already existing control strategies cannot address the above-mentioned issues at the same time. It greatly increases the difficulty of controller design especially when some states are not measurable. Inspired by above motivations, this paper exploits neural networks to deal with nonlinear dynamics including discontinuous types, and combines extended state observers to estimate disturbances and unmeasurable states for uncertain nonlinear servomechanisms. Meanwhile, the desired-command-based model compensation approach is integrated into the controller design. It is worth noting that the neural network weights are updated by the combination of the estimation error and tracking error to acquire better approximation accuracy. According to above technologies, a novel extended-state-observer-based neural network adaptive motion control algorithm will be synthesized. The bounded stability of the whole closed-loop system is proved strictly. In addition, the comparisons of the application results on an electro-hydraulic servo system verify the availability and superiority of the developed control algorithm. 

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Neuroadaptive control of information-poor servomechanisms with smooth and nonsmooth uncertainties

In view of the large low-speed slewing bearing, the vibration signals are always very weak and overwhelmed by other strong noise, which makes fault feature extraction from the signals very difficult. In order to solve this problem, a denoising method based on multi-scale principal component analysis (MSPCA) and the ensemble empirical mode decomposition (EEMD) is proposed with a new intrinsic mode functions (IMFs) selection strategy. After that, the vibration signal is reconstructed by the selected IMFs. Finally, a method of multi-scale fault frequency extraction of slewing bearing based on EEMD is applied to denoise the vibration signals. The application of this method is demonstrated with laboratory accelerated slewing bearing life test data. Results show that EEMD-MSPCA is more effective in multi-scale fault frequency extraction of low-speed slewing bearing.

Hua Wang

Papers

Load Distribution over Raceways of an 8-Point-Contact Slewing Bearing

Fired bullet signature correlation using the finite ridgelet transform (FRIT) and the gray level co-occurrence matrix (GLCM) methods

Pitch bearing/raceway fretting: Influence of contact angle:

Neuroadaptive control of information-poor servomechanisms with smooth and nonsmooth uncertainties

Multi-Scale Fault Frequency Extraction Method Based on EEMD for Slewing Bearing Fault Diagnosis