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

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality of the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity of the manufacturing line, effective and efficient feature extraction of gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters of the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line of short filament fibres are used to evaluate the performance of the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable of accurate gear fault det ection in fibre manufacturing lines

A new swarm intelligence optimized multiclass multi-kernel relevant vector machine: An experimental analysis in failure diagnostics of diesel engines

This work attempts to introduce a new intelligent method for condition monitoring of diesel engines. Diesel engine is one of the most important power providers for various industrial applications, including automobiles, ships, agricultures, construction, and electrical machinery. Due to harsh working environment, diesel engines are vulnerable to failures. This article addresses a significant need to improve predictive maintenance activities in diesel engines. A new failure diagnostics approach was proposed based on the manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine. Three manifold learning algorithms were first respectively used to fuse the features that extracted from the original vibration data of the diesel engines into a new nonlinear space. The fused features contain the most distinct health information of the engine by discarding redundant features. Then, the swarm intelligence optimized multiclass multi-kernel relevant vector machine was proposed to identify the failures using the fused features. The contribution of this research is that the dragonfly algorithm is employed to optimize the weights of the multi-kernel functions in the multiclass relevant vector machine. It was also applied to establishing a weighted-sum model by combining the outputs of swarm intelligence optimized multiclass multi-kernel relevant vector machine models with different manifold learning algorithms. Robust failure detection of diesel engines is achieved owing to combined strengths of multiple kernel functions and weighted-sum strategy. The effectiveness of the proposed method is demonstrated by experimental vibration data collected from a commercial diesel engine. The failure detection capability of the proposed manifold learning and swarm intelligence optimized multiclass multi-kernel relevant vector machine method for diesel engines will potentially benefit the machine condition monitoring industry by improving budgeting/forecasting and/or enabling just-in-time maintenance

An Internet of Things Approach for Extracting Featured Data Using AIS Database: An Application Based on the Viewpoint of Connected Ships

Automatic Identification System (AIS), as a major data source of navigational data, is widely used in the application of connected ships for the purpose of implementing maritime situation awareness and evaluating maritime transportation. Efficiently extracting featured data from AIS database is always a challenge and time-consuming work for maritime administrators and researchers. In this paper, a novel approach was proposed to extract massive featured data from the AIS database. An Evidential Reasoning rule based methodology was proposed to simulate the procedure of extracting routes from AIS database artificially. First, the frequency distributions of ship dynamic attributes, such as the mean and variance of Speed over Ground, Course over Ground, are obtained, respectively, according to the verified AIS data samples. Subsequently, the correlations between the attributes and belief degrees of the categories are established based on likelihood modeling. In this case, the attributes were characterized into several pieces of evidence, and the evidence can be combined with the Evidential Reasoning rule. In addition, the weight coefficients were trained in a nonlinear optimization model to extract the AIS data more accurately. A real life case study was conducted at an intersection waterway, Yangtze River, Wuhan, China. The results show that the proposed methodology is able to extract data very precisely

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality o f the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity o f the manufacturing line, effective and efficient feature extraction o f gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line o f short filament fibres are used to evaluate the performance o f the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable o f accurate gear fault detection in fibre manufacturing lines.The Key Laboratory of Expressway Construction Machinery of Shaanxi Province (No 310825161123), NSFC (No 51505475), Yingcai Project of CUMT (YC2017001), Priority Academic Program Development of Jiangsu Higher Education Institutions and the UOW Vice-Chancellor’s Postdoctoral Research Fellowship.http://www.bindt.org/publications/insight-journalam2019Electrical, Electronic and Computer Engineerin

An image recognition method for gear fault diagnosis in the manufacturing line of short filament fibres

The manufacturing line is a fundamental element in short filament fibre production, in which the gearbox is the key mechanical part. Any faults in the gearbox will greatly affect the quality o f the short filament fibres. However, due to the harsh working environment, the gearbox is vulnerable to failure. Due to the complexity o f the manufacturing line, effective and efficient feature extraction o f gear faults is still a challenge. To this end, a new fault diagnosis method based on image recognition is proposed in this paper for gear fault detection in fibre manufacturing lines. In this method, wavelet packet bispectrum analysis (WPBA) is proposed to process the gear vibration signals. The bispectrum texture is obtained and then analysed by an image fusion algorithm for texture feature extraction. The grey-level co-occurrence matrix is used in the image fusion and the extracted texture features are four parameters o f the grey-level co-occurrence matrix. Finally, a support vector machine (SVM) is adapted to recognise the gear fault type and location. Experimental data acquired from a real-world manufacturing line o f short filament fibres are used to evaluate the performance o f the proposed image-based gear fault detection method. The analysis results demonstrate that the newly proposed method is capable o f accurate gear fault detection in fibre manufacturing lines.The Key Laboratory of Expressway Construction Machinery of Shaanxi Province (No 310825161123), NSFC (No 51505475), Yingcai Project of CUMT (YC2017001), Priority Academic Program Development of Jiangsu Higher Education Institutions and the UOW Vice-Chancellor’s Postdoctoral Research Fellowship.http://www.bindt.org/publications/insight-journalam2019Electrical, Electronic and Computer Engineerin

Arundic Acid Prevents Developmental Upregulation of S100B Expression and Inhibits Enteric Glial Development.

S100B is expressed in various types of glial cells and is involved in regulating many aspects of their function. However, little is known about its role during nervous system development. In this study, we investigated the effect of inhibiting the onset of S100B synthesis in the development of the enteric nervous system, a network of neurons and glia located in the wall of the gut that is vital for control of gastrointestinal function. Intact gut explants were taken from embryonic day (E)13.5 mice, the day before the first immunohistochemical detection of S100B, and cultured in the presence of arundic acid, an inhibitor of S100B synthesis, for 48 h. The effects on Sox10-immunoreactive enteric neural crest progenitors and Hu-immunoreactive enteric neurons were then analyzed. Culture in arundic acid reduced the proportion of Sox10+ cells and decreased cell proliferation. There was no change in the density of Hu+ enteric neurons, however, a small population of cells exhibited atypical co-expression of both Sox10 and Hu, which was not observed in control cultures. Addition of exogenous S100B to the cultures did not change Sox10+ cell numbers. Overall, our data suggest that cell-intrinsic intracellular S100B is important for maintaining Sox10 and proliferation of the developing enteric glial lineage