Design of an Aircraft Rolling Bearings Platform and its Thermal Performance Evaluation

The thermal instability is one crucial factor leading to low bearing operation performance. This paper presents a novel experiment device for thermal performance investigation of an aircraft rolling bearings. A bidirectional fixing structure was designed to balance the spindle thermal deformation. The hydraulic loading was used and the oil injection manner was adopted in the new device. Experimental test was conducted using the new device and experimental results were compared with the calculation based on the temperature and thermal nodes theory. The comparison demonstrates that the temperature distribution trends between the theoretical and experimental results remained the same; specifically, the error between the theoretical and experimental results was 1.0 % under the condition of 200 kg load and 2250 rpm driving speed. Consequently, the analysis result shows that the new device is feasible and reliable to provide precise thermal characteristics for the aircraft rolling bearings

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin

Modeling wear state evolution using real-time wear debris features

Because wear is one of the most typical causes of decreasing performance in running machines, monitoring wear is regarded as a crucial technology in maintaining the health of machines. However, monitoring wear is not a fully mature process because quantifying the development of wear in real time is a challenging task because there is no universal indicator. To meet this need, wear-oriented dynamic modeling with online ferrographic images was used to investigate and then describe a real-time wear state. This investigation was carried out by combining three wear indices to describe the wear rate, the wear mechanism, and the severity of wear. A binary classifier method is also proposed to classify these wear stages in the three extracted indices. A strategy to identify the dynamic transition of wear states with adaptive parameters is also developed and then a four-ball wear test is carried out to verify the method. The results indicate that this modeling strategy can accurately identify a developing wear state that is characterized by stages. This proposed method is better at monitoring the health evolution of a machine system than just detecting faults

Effect of carbon nanotubes on friction and wear of a piston ring and cylinder liner system under dry and lubricated conditions

Abstract This study involves the application of carbon nanotubes (CNTs) to a piston ring and cylinder liner system in order to investigate their effect on friction and wear under dry and lubricated conditions. Carbon nanotubes were used as a solid lubricant and lubricant additive in dry and lubricated conditions, respectively. Simulation and measurement of friction and wear were conducted using a reciprocating tribometer. Surface analysis was performed using a scanning electron microscope and an energy dispersive spectrometer. The results indicate that carbon nanotubes can considerably improve the tribological performance of a piston ring and cylinder liner system under dry sliding conditions, whereas improvement under lubricated conditions is not obvious. Under dry friction, the effective time of the CNTs is limited and the friction coefficient decreases with an increase in CNT content. Furthermore, the dominant wear mechanism during dry friction is adhesive

A prototype of on-line extraction and three-dimensional characterisation of wear particle features from video sequence

Wear particles in lubricants carry valuable information about machine wear status which is useful in machine condition monitoring. For wear analysis, wear particles are often imaged and their features are extracted. However, the particle morphology acquired from current 2-dimensional (2-D) images does not contain thickness information which can be critical in wear mechanism interpretation. In this paper, we present the development of a video based system to extend the particle information in 3-dimension (3-D). The proposed method contains three main procedures including: particle extraction using a Gaussian mixture model, multiple particle tracking with Kalman filter, and 3-D feature reconstruction by the shape-from-silhouette method. This framework ensures that wear particles are correctly extracted, and their 3-D morphological features are obtained. It also can be regarded as a potential option for on-line particle monitoring. The performance of this method was demonstrated by analysing wear particles generated from a four-ball machine and a spur gear box, and verified by computer simulations. Results indicated that 3-D features of wear particles were obtained with satisfactory accuracy

Study on vibration mechanism induced by skidding in pure rolling contact

Skidding in a rolling bearing often causes unexpected surface wear and uneven vibration. To explore the fundamental interaction mechanisms, an investigation is conducted experimentally and numerically on the tribo-dynamic responses of a simplified sliding-rolling contact. Vibration analyses show that the tangential vibration contains not only the rotational frequency of the roller but also the higher harmonics excited by the sliding-rolling contact. Surface contact stress analysis is also carried out with a twin-discs model. Results show that, with sliding occurring in the rolling contact, the unstable shear stress variation is induced and becomes the root cause of the tangential vibration. Further wear checks reveal that the wear failure under sliding-rolling contact coincides with the shear effect of contact interfaces

Optimized CNN model for identifying similar 3D wear particles in few samples

Typical wear particles can be considered as distinctive indicators of on-going wear faults in machines. However, the small number of samples has limited the identification accuracy for similar fault particles. Besides, the three-dimensional (3D) characterization of wear particles may face huge challenges due to excessive surface parameters. Focusing on the problems of high similarity particles and few samples, a CNN-based particle classification method is developed with an example set of fatigue and severe sliding particles, which are the product of severe wear of machines. For data reduction, imaged 3D particle surfaces are firstly converted into 2D depth maps without losing surface information. Virtual fault particle images are then synthesized using a Conditional Generative Adversarial Networks (CGAN), according to the particle generation mechanism and distinctive particle features. Furthermore, a non-parametric particle identification model is established with the optimization of the CNN structures and training method, and the network is further optimized with the particle image standardization and the network visualization. Validation experiments reveal that the proposed method can accurately identify all tested fatigue and severe sliding particles with their typical characteristics