ORCID Profile
0000-0002-3059-5813
Current Organisation
University of New South Wales
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Mechanical Engineering | Dynamics, Vibration and Vibration Control | Mechanical Engineering | Tribology | Numerical Modelling and Mechanical Characterisation | Mechanical engineering | Neural Networks, Genetic Alogrithms And Fuzzy Logic | Biomaterials | Biomechanical Engineering | Tribology | Nanoscale Characterisation | Mechanical engineering asset management | Dynamics vibration and vibration control | Nanomaterials | Expert Systems | Nanotechnology | Composite and Hybrid Materials | Acoustics and Noise Control (excl. Architectural Acoustics) |
ANZSRC Socio-Economic Objective (SEO)
Agricultural machinery and equipment | Industrial machinery and equipment | Air Safety | Management of Noise and Vibration from Transport Activities | Air Force | Health related to ageing | Mining machinery and equipment | Wind Energy | Appliances and electrical machinery and equipment | Diagnostic methods | Expanding Knowledge in Technology | Expanding Knowledge in the Physical Sciences | Industrial Machinery and Equipment | Expanding Knowledge in Engineering | Mining Machinery and Equipment | Expanding Knowledge in the Biological Sciences | Skeletal system and disorders (incl. arthritis) | Air Passenger Transport
Related Links
Publications
Oxidation wear monitoring based on the color extraction of on-line wear debris
Publisher: Elsevier BV
Date: 05-2015
Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method
Publisher: Elsevier BV
Date: 10-2011
The investigation of the condition and faults of a spur gearbox using vibration and wear debris analysis techniques
Publisher: Elsevier BV
Date: 2006
Use of Cyclostationarity to Detect Changes in Gear Surface Roughness Using Vibration Measurements
Publisher: Springer International Publishing
Date: 12-11-2018
Frequency band selection based on the kurtosis of the squared envelope spectrum and its application in bearing fault diagnosis
Publisher: SAGE Publications
Date: 09-07-2015
Abstract: Selection of a suitable frequency band for envelope analysis is a key step towards successful diagnosis of rolling element bearing faults. The band selection requirements include: (a) effectively avoiding the frequency range which contains irrelevant periodic impulses, and (b) keeping the feature of impulsiveness for bearing signals. Popular frequency band selection approaches often aim at searching for the band which maximizes the envelope kurtosis or the kurtosis of the envelope spectrum, and thus may not satisfy those requirements simultaneously when weak bearing signals are masked by strong noisy impulses. To resolve this issue, the energy of envelope signal and its energy distribution in the spectrum were taken into consideration in the proposed approach. It was shown that, for weak impulsive signals, the kurtosis of squared envelope spectrum (KSES) decreases as the bandwidth increases provided that the initial band is wide enough to contain most of the signal energy. The decreasing trend of KSES was then used to determine bandwidths for predefined center frequencies. From the obtained narrow bands, the one with a large bandwidth and a low KSES was selected for further analysis by comparing the value of a quality index. The bandwidth determination method and the final band selection criterion effectively excluded irrelevant periodic impulses in the selected band and ensured the high impulsiveness of the extracted signal. The proposed method was validated using experimental data obtained from a bearing rig and a planetary gearbox test rig, and its advantages were highlighted by comparing with fast kurtogram and protrugram.
Frictional behaviour and friction mechanisms of rolling-sliding contact in mixed EHL
Publisher: Elsevier BV
Date: 10-2017
Use of mesh phasing to locate faulty planet gears
Publisher: Elsevier BV
Date: 02-2019
A new nonlinear blind source separation method with chaos indicators for decoupling diagnosis of hybrid failures: A marine propulsion gearbox case with a large speed variation
Publisher: Elsevier BV
Date: 08-2016
Three dimensional shape measurement of wear particle by iterative volume intersection
Publisher: SPIE
Date: 10-04-2018
DOI: 10.1117/12.2304561
A hybrid search-tree discriminant technique for multivariate wear debris classification
Publisher: Elsevier BV
Date: 12-2017
Progress and trend of sensor technology for on-line oil monitoring
Publisher: Springer Science and Business Media LLC
Date: 04-11-2013
Study on wear behaviour and wear model of nitrile butadiene rubber under water lubricated conditions
Publisher: Royal Society of Chemistry (RSC)
Date: 2014
DOI: 10.1039/C4RA01703J
Abstract: The main wear mechanism between the NBR pins and the 1Cr18Ni9Ti stainless steel rubbing pairs is severe adhesion tearing wear under the water-lubricated condition.
Optimised Spectral Kurtosis for bearing diagnostics under electromagnetic interference
Publisher: Elsevier BV
Date: 06-2016
Numerical analysis of cartilage surfaces for osteoarthritis diagnosis using field and feature parameters
Publisher: Elsevier BV
Date: 07-2011
A New Method of Nonlinear Feature Extraction for Multi-Fault Diagnosis of Rotor Systems
Publisher: SAGE Publications
Date: 11-2010
DOI: 10.1260/0957-4565.41.10.29
Abstract: Rotor systems have been extensively used in a variety of industrial applications. However an unexpected failure may cause a break down of the rotational machinery, resulting in production and significant economic losses. Efficient incipient fault diagnosis is therefore critical to the machinery normal operation. Noise and vibration analysis is popular and effective for the rotor fault diagnosis. One of the key procedures in the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanical fault diagnosis, and nonlinear feature extraction for rotor multi-fault detection has not been studied. This paper reports a new development based on a novel supervised manifold learning algorithm (adaptive locally linear embedding) applied to nonlinear feature extraction for rotor multiple defects identification. The adaptive locally linear embedding (ALLE) combines with the adaptive nearest neighbour algorithm and supervised locally linear embedding (LLE) to provide an adaptive supervised learning. Hence, distinct nonlinear features could be extracted from high-dimensional dataset effectively. Based on ALLE, a new fault diagnosis approach has been proposed. The independent component analysis (ICA) was firstly employed to separate the faulty components of the rotor vibration from the observation data. Then wavelet transform (WT) was used to decompose the recovered signals, and statistical features of frequency bands were hence calculated. Lastly, ALLE was applied to learn the low-dimensional intrinsic structure of the original feature space. The experiments on vibration data of single and coupled rotor faults have demonstrated that sensitive fault features can be extracted efficiently after the ICA-WT-ALLE processing, and the proposed diagnostic system is effective for the multi-fault identification of the rotor system. Furthermore, the proposed method achieves higher performance in terms of the classification rate than other feature extraction methods such as principal component analysis (PCA) and locally linear embedding (LLE).
A prototype of on-line extraction and three-dimensional characterisation of wear particle features from video sequence
Publisher: Elsevier BV
Date: 12-2016
Morphological Feature Extraction Based on Multiview Images for Wear Debris Analysis in On-line Fluid Monitoring
Publisher: Informa UK Limited
Date: 10-08-2016
Investigation of three-dimensional surface topographies and mechanical properties of hypothesized biological active wear particles from artificial joints
Publisher: Elsevier BV
Date: 04-2013
Tribological properties of aged nitrile butadiene rubber under dry sliding conditions
Publisher: Elsevier BV
Date: 2015
Investigations of micron and submicron wear features of diseased human cartilage surfaces
Publisher: SAGE Publications
Date: 02-2015
Abstract: Osteoarthritis is a common disease. However, its causes and morphological features of diseased cartilage surfaces are not well understood. The purposes of this research were (a) to develop quantitative surface characterization techniques to study human cartilages at a micron and submicron scale and (b) to investigate distinctive changes in the surface morphologies and biomechanical properties of the cartilages in different osteoarthritis grades. Diseased cartilage s les collected from osteoarthritis patients were prepared for image acquisition using two different techniques, that is, laser scanning microscopy at a micrometer scale and atomic force microscopy at a nanometer scale. Three-dimensional, digital images of human cartilages were processed and analyzed quantitatively. This study has demonstrated that high-quality three-dimensional images of human cartilage surfaces could be obtained in a hydrated condition using laser scanning microscopy and atomic force microscopy. Based on the numerical data extracted from improved image quality and quantity, it has been found that osteoarthritis evolution can be identified by specific surface features at the micrometer scale, and these features are litude and functional property related. At the submicron level, the spatial features of the surfaces were revealed to differ between early and advanced osteoarthritis grades. The effective indentation moduli of human cartilages effectively revealed the cartilage deterioration. The imaging acquisition and numerical analysis methods established allow quantitative studies of distinctive changes in cartilage surface characteristics and better understanding of the cartilage degradation process.
A new statistical model for acoustic emission signals generated from sliding contact in machine elements
Publisher: Elsevier BV
Date: 11-2018
The characterization of wear transitions in sliding wear process contaminated with silica and iron powder
Publisher: Elsevier BV
Date: 02-2005
Difference equation based empirical mode decomposition with application to separation enhancement of multi-fault vibration signals
Publisher: Informa UK Limited
Date: 10-11-2016
Optimised reflection imaging for surface roughness analysis using confocal laser scanning microscopy and height encoded image processing
Publisher: IOP Publishing
Date: 2005
Wear profile prediction of IC engine bearings by dynamic simulation
Publisher: Elsevier BV
Date: 10-2016
Mechanical stability and rheology of lithium–calcium-based grease containing ZDDP
Publisher: Royal Society of Chemistry (RSC)
Date: 2016
DOI: 10.1039/C5RA20288D
Abstract: Although lubricating greases with zinc dialkyl-dithiophates (ZDDP) have been used widely, the influence of ZDDP on the rheological properties of lithium–calcium-based greases (LCBG) has not been reported.
3D quantitative characterization of degraded surfaces of human knee cartilages affected by osteoarthritis
Publisher: Elsevier BV
Date: 11-2014
Wear state identification using dynamic features of wear debris for on-line purpose
Publisher: Elsevier BV
Date: 04-2017
An RFID-based remote monitoring system for enterprise internal production management
Publisher: Springer Science and Business Media LLC
Date: 04-04-2006
The nonlinear dynamics response of cracked gear system in a coal cutter taking environmental multi-frequency excitation forces into consideration
Publisher: Springer Science and Business Media LLC
Date: 28-10-2015
Mechanical and tribological performance of UHMWPE influenced by temperature change
Publisher: Elsevier BV
Date: 09-2017
Wear characterisation of articular cartilage surfaces at a nano-scale using atomic force microscopy
Publisher: Elsevier BV
Date: 07-2013
Dispersion state of multi-walled carbon nanotubes in the UHMWPE matrix: Effects on the tribological and mechanical response
Publisher: Elsevier BV
Date: 10-2018
Condition Monitoring of Rotating Machinery with Acoustic Emission: A British–Australian Collaboration
Publisher: Springer International Publishing
Date: 12-11-2018
Biomimetic characterisation of key surface parameters for the development of fouling resistant materials
Publisher: Informa UK Limited
Date: 2009
DOI: 10.1080/08927010802538480
Abstract: Material science provides a direct route to developing a new generation of non-toxic, surface effect-based antifouling technologies with applications ranging from biomedical science to marine transport. The surface topography of materials directly affects fouling resistance and fouling removal, the two key mechanisms for antifouling technologies. However, the field is hindered by the lack of quantified surface characteristics to guide the development of new antifouling materials. Using a biomimetic approach, key surface parameters are defined and quantified and correlated with fouling resistance and fouling removal from the shells of marine molluscs. Laser scanning confocal microscopy was used to acquire images for quantitative surface characterisation using three-dimensional surface parameters, and field assays correlated these with fouling resistance and fouling release. Principle component analysis produced a major component (explaining 54% of total variation between shell surfaces) that correlated with fouling resistance. The five surface parameters positively correlated to increased fouling resistance were, in order of importance, low fractal dimension, high skewness of both the roughness and waviness profiles, higher values of isotropy and lower values of mean surface roughness. The second component (accounting for 20% of variation between shells) positively correlated to fouling release, for which higher values of mean waviness almost exclusively dictated this relationship. This study provides quantified surface parameters to guide the development of new materials with surface properties that confer fouling resistance and release.
Surface roughness evolutions in sliding wear process
Publisher: Elsevier BV
Date: 07-2008
Quantitative characterization of collagen orientation in the superficial zone for studying early degenerative changes in articular cartilage
Publisher: World Scientific Pub Co Pte Lt
Date: 03-2006
DOI: 10.1142/S0218957706001674
Abstract: Collagen fibres in the superficial zone are critical to the durability and stability of articular cartilage. Early osteoarthritis is often characterized by lesions on the surface of articular cartilage as a result of deterioration or degeneration of the collagen meshwork in the superficial zone. Therefore, traditional histology employing visual inspection of the microstructure of collagen fibres is one of the methods frequently used to evaluate the physiological state of articular cartilage in the medical field. However, traditional histology is often limited to 2D observations. It requires physical sectioning and dehydration of articular cartilage for stereological study. In addition, visual inspection is subjective and time consuming. Confocal microscopy provides a way to study the three-dimensional collagen structure in bulk hydrated articular cartilage. Utilizing fibre optical laser scanning confocal microscopy, the aim of the present study was to apply the spectral moment as a method for quantitatively describing the orientation of the collagen fibres in the superficial zone in relation to the physiological status of the cartilage, such as distinguishing normal cartilage from the early osteoarthritic cartilage.
Vibration-based updating of wear prediction for spur gears
Publisher: Elsevier BV
Date: 04-2019
Mechanical Properties and Three-Dimensional Topological Characterisation of Micron, Submicron and Nanoparticles from Artificial Joints
Publisher: Springer Science and Business Media LLC
Date: 06-10-2013
INVESTIGATION INTO CHANGES IN COLLAGEN STRUCTURE OF ARTICULAR CARTILAGE AND WEAR PARTICLES OF KNEE JOINTS FOR OSTEOARTHRITIC WEAR ASSESSMENT
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2009
DOI: 10.1142/S0218957709002316
Abstract: Our bodies deteriorate from wear and tear processes, which give rise to ache and pain. This degenerative process is medically referred to as osteoarthritis (OA) and it is estimated to affect a large portion of the population at some stage in their life. There is a need for research into new and improved techniques that might be developed into a schema to aid in the early diagnosis and prognosis of a patient's condition. This can be achieved by studying the morphology of the collagen fibers in the wear particles generated to gain an insight into the osteoarthritic condition exhibited by the joint. The study has been conducted in three phases. Firstly, an animal model has been used to generate s les of cartilage and wear particles for the study. A suitable staining technique has then been developed that allows the three-dimensional visualization and quantitative analysis of the structure of the collagen matrix of sheep cartilage and in wear particles. Finally, correlation of the changes in the collagen matrix as per OA severity has been studied. The study has identified key numerical parameters to characterize distinctive wear features of the cartilage and wear debris. A good correlation of the wear features of the cartilage and wear particle s les has been found. The positive results attained by this study suggest that with the aid of further research and development, it is distinctly possible to develop improved diagnostic procedures for clinical osteoarthritic assessment.
A New System of Failure Warning and Monitoring Control System for Gas Turbine Compressor (GTC)/Auxiliary Power Unit (APU) of Aircrafts and a Sample Study
Publisher: Trans Tech Publications, Ltd.
Date: 04-2012
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMR.503-504.1633
Abstract: One of the key elements of servicing the aviation industry is the provision and maintenance of first class equipment. In order to ensure a secure and effective flight of aircrafts in aviation, the ground supporting equipment which enables the planes to get off the ground without delaying the flight plans, has to be kept ready at all times [1].
Investigation of wear particles generated in human knee joints using atomic force microscopy
Publisher: Springer Science and Business Media LLC
Date: 26-05-2013
Development of an expert system for automatic osteoarthritis diagnosis using numerical characterisations of articular cartilages and wear particles
Publisher: IEEE
Date: 07-2013
Experimental observation of surface morphology effect on crystallization fouling in plate heat exchangers
Publisher: Elsevier BV
Date: 2011
Wear particle classification in a fuzzy grey system
Publisher: Elsevier BV
Date: 04-1999
Vibration-Based Spall Size Tracking in Rolling Element Bearings
Publisher: Springer International Publishing
Date: 2015
Immunohistochemical analysis of structural changes in collagen for the assessment of osteoarthritis
Publisher: SAGE Publications
Date: 04-05-2011
Abstract: Collagen fibrillation within articular cartilage (AC) plays a key role in joint osteoarthritis (OA) progression and, therefore, studying collagen synthesis changes could be an indicator for use in the assessment of OA. Various staining techniques have been developed and used to determine the collagen network transformation under microscopy. However, because collagen and proteoglycan coexist and have the same index of refraction, conventional methods for specific visualization of collagen tissue is difficult. This study aimed to develop an advanced staining technique to distinguish collagen from proteoglycan and to determine its evolution in relation to OA progression using optical and laser scanning confocal microscopy (LSCM). A number of AC s les were obtained from sheep joints, including both healthy and abnormal joints with OA grades 1 to 3. The s les were stained using two different trichrome methods and immunohistochemistry (IHC) to stain both colourimetrically and with fluorescence. Using optical microscopy and LSCM, the present authors demonstrated that the IHC technique stains collagens only, allowing the collagen network to be separated and directly investigated. Fluorescently-stained IHC s les were also subjected to LSCM to obtain three-dimensional images of the collagen fibres. Changes in the collagen fibres were then correlated with the grade of OA in tissue. This study is the first to successfully utilize the IHC staining technique in conjunction with laser scanning confocal microscopy. This is a valuable tool for assessing changes to articular cartilage in OA.
In-situ Monitoring of Active Crack in Steel Structure Using Acoustic Emission Technique
Publisher: IEEE
Date: 2009
A complete solution for neutral knowledge representation within collaborative product development environment
Publisher: Springer Science and Business Media LLC
Date: 12-01-2005
The Development of a New Image Acquisition & Analysis System for 3D Surface Measurements Using Confocal Laser Scanning Microscopy
Publisher: Trans Tech Publications Ltd.
Date: 09-02-2008
Detrended Fluctuation Analysis in Machine Condition Diagnosis
Publisher: American Scientific Publishers
Date: 15-03-2012
On the bi-dimensional variational decomposition applied to nonstationary vibration signals for rolling bearing crack detection in coal cutters
Publisher: IOP Publishing
Date: 12-05-2016
Automatic wear-particle classification using neural networks
Publisher: Springer Science and Business Media LLC
Date: 1998
Fault detection and diagnosis of a gearbox in marine propulsion systems using bispectrum analysis and artificial neural networks
Publisher: Springer Science and Business Media LLC
Date: 03-2011
A new approach to numerical characterisation of wear particle surfaces in three-dimensions for wear study
Publisher: Elsevier BV
Date: 04-2012
3D Surface Characterizations of Wear Particles Generated from Lubricated Regular Concave Cylinder Liners
Publisher: Springer Science and Business Media LLC
Date: 07-05-2014
Expert system development for vibration analysis in machine condition monitoring
Publisher: Elsevier BV
Date: 2008
The surface roughness evolutions of wear particles and wear components under lubricated rolling wear condition
Publisher: Elsevier BV
Date: 07-2005
Condition Monitoring and Fault Diagnosis for Marine Diesel Engines using Information Fusion Techniques
Publisher: Kaunas University of Technology (KTU)
Date: 07-09-2012
In vitro wear study of PEEK and CFRPEEK against UHMWPE for artificial cervical disc application
Publisher: Elsevier BV
Date: 06-2018
Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis
Publisher: Elsevier BV
Date: 2013
Effects of temperature on sliding wear process under contaminated lubricant test conditions
Publisher: Elsevier BV
Date: 10-2004
Study on biomimetic preparation of shell surface microstructure for ship antifouling
Publisher: Elsevier BV
Date: 08-2013
A New Approach to Automated Bearing Fault Severity Assessment Using Deep-Learning
Publisher: Springer International Publishing
Date: 2020
Vibration and wear prediction analysis of IC engine bearings by numerical simulation
Publisher: Elsevier BV
Date: 08-2017
3-D Particle Surface Reconstruction From Multiview 2-D Images With Structure From Motion and Shape From Shading (January 2020)
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Study on Influence of Cylinder Liner Surface Texture on Lubrication Performance for Cylinder Liner–Piston Ring Components
Publisher: Springer Science and Business Media LLC
Date: 20-04-2013
Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed
Publisher: Springer Science and Business Media LLC
Date: 08-2012
Computer image analysis of wear particles in three-dimensions for machine condition monitoring
Publisher: Elsevier BV
Date: 12-1998
Extraction and use of frequency-domain relationships between time-varying gear meshing properties and diagnostic measurements
Publisher: Elsevier BV
Date: 05-2023
A novel prediction model for aircraft spare part intermittent demand in aviation transportation logistics using multi-components accumulation and high resolution analysis
Publisher: SAGE Publications
Date: 22-04-2014
Abstract: High effective management of civil aircraft spare parts is of significant importance for the economical operation of aircrafts. However, the stochastic characteristics of the aircraft spare parts make it difficult to find a reliable rule to precisely predict the future demand. In order to address this issue, this work presents a novel multi-components accumulation and high resolution analysis (MCAHR) method to improve the forecasting performance of aircraft spare parts. The MCAHR takes the advantages of high resolution of the wavelet transform to analyze the time series of spare part intermittent demand. The original time series were decomposed into several sub-bands along with the time axis of the wavelet. Then particle swarm optimized fuzzy neural networks were established for each sub-band to intelligently mine their intrinsic features. Accurate prediction result was hence obtained by the accumulation of the outputs of all fuzzy neural networks. Experimental tests using the historical data of A320 civil aircrafts were carried out in this work to evaluate the proposed MCAHR method. The analysis results have demonstrated a high efficiency of the MCAHR method and that its prediction performance is superior to existing methods. Hence, the proposed MCAHR method has practical importance in the civil aircraft spare part intermittent demand prediction and will provide a significant economic benefit to the industry through reasonable management of aircraft spare parts.
A microfluidic device for three-dimensional wear debris imaging in online condition monitoring
Publisher: SAGE Publications
Date: 15-12-2016
Abstract: Three-dimensional morphologies of wear particles are important information sources for machine condition assessment and fault diagnosis. However, existing three-dimensional image acquisition systems, such as laser scanning confocal microscopy and atomic force microscopy, cannot be directly applied in condition-based maintenance of machines. In order to automatically acquire three-dimensional information of wear debris for online condition monitoring, a microfluidic device consisting of an oil flow channel and a video imaging system is developed. This paper focuses on the control of particle motions. A microchannel is designed to ensure the continuous rotation of particles such that their three-dimensional features can be captured. The relationships between running torque and channel height and particle size are analysed to determine the channel height. An infinite fluid field is considered to make sure that the particles rotate around the same axis to capture 360 degree views. Based on this, the cross section of the microchannel is determined at 5 mm × 0.2 mm (height × width) to capture the wear debris under 200 µm. A CMOS sensor is used to image the particles in multiple views and then three-dimensional features of wear debris (e.g. thickness, height aspect ratio and sphericity) are obtained. Two experiments were carried out to evaluate the performances of the designed system. The results demonstrate that (1) the microfluidic device is effective in capturing multiple view images of wear particles various in sizes and shapes (2) spatial morphological characteristics of wear particles can be constructed using a sequence of multi-view images.
Restoration of low-informative image for robust debris shape measurement in on-line wear debris monitoring
Publisher: Elsevier BV
Date: 2019
Development of a gear vibration indicator and its application in gear wear monitoring
Publisher: Elsevier BV
Date: 08-2016
Gear faults diagnosis based on wavelet-AR model and PCA
Publisher: SPIE
Date: 07-08-2010
DOI: 10.1117/12.866387
A study of the effect of contaminant particles in lubricants using wear debris and vibration condition monitoring techniques
Publisher: Elsevier BV
Date: 06-2005
Ship electric propulsion with a sensorless permanent magnet synchronous motor: A simulation study
Publisher: SAGE Publications
Date: 18-05-2012
Abstract: New general ships powered by electric propulsion always operate at low motor speeds. Popular sensorless vector control methods, including open loop and closed loop methods, are not reliable in this application. This is because the magnitude of back-electromotive force voltage is very small in the low speed region, and the performance of these methods is therefore significantly degraded. To overcome this problem, a high-frequency injection method is introduced in the control of the ship electric propulsion system. The injected periodic signal creates a high-frequency revolving field to act as a carrier. Then the desired information about the position angle of the motor can be retrieved. A simulation model of a ship electric propulsion system is established in Matlab/Simulink. The test results show that the newly proposed control system works stably with various ship operating conditions and robustly against speed variations. Its estimated position error is less than 0.3 rad. Moreover, the proposed sensorless vector control approach has been compared with the closed-loop-based model reference adaptive system. The comparison has demonstrated that the dynamic characteristics of the proposed control system are superior to the latter.
Multi-mode separation and nonlinear feature extraction of hybrid gear failures in coal cutters using adaptive nonstationary vibration analysis
Publisher: Springer Science and Business Media LLC
Date: 20-11-2015
Watershed-Based Morphological Separation of Wear Debris Chains for On-Line Ferrograph Analysis
Publisher: Springer Science and Business Media LLC
Date: 13-12-2013
Recent progress on decoupling diagnosis of hybrid failures in gear transmission systems using vibration sensor signal: A review
Publisher: Elsevier BV
Date: 08-2016
Utilization of two-dimensional fast fourier transform and power spectral analysis for assessment of early degeneration of articular cartilage
Publisher: World Scientific Pub Co Pte Lt
Date: 09-2005
DOI: 10.1142/S0218957705001564
Abstract: Degeneration of articular cartilage begins from deterioration of the collagen fibres in the superficial zone. Standard histology using 2D imaging technique is often used to determine the microstructure of collagen fibres and the physiological functions of articular cartilage. However, information of the 3D collageneous structure in the cartilage could be lost and misinterpreted in 2D observations. In contrast, confocal microscopy permits studying the 3D internal structure of bulk articular cartilage with minimal physical disturbing. Using fibre optic laser scanning confocal microscopy, a 3D histology has been previously developed to visualize the collagen matrix in the superficial zone by means of identifying the early arthritic changes in articular cartilage. In this study, we characterized the collagen orientation in the superficial zone of normal cartilage, the cartilage with surface disruption and fibrillated cartilage using Fast Fourier transforms and power spectral analysis techniques. Thus, we have established an objective method for assessing the early pathology changes in the articular cartilage.
Investigation of the nano-mechanical properties and surface topographies of wear particles and human knee cartilages
Publisher: Elsevier BV
Date: 02-2015
Nonlinear dynamic response of a multi-degree of freedom gear system dynamic model coupled with tooth surface characters: a case study on coal cutters
Publisher: Springer Science and Business Media LLC
Date: 02-11-2015
Large margin-learning methodology from time-frequency maps and its physically interpretable weights for simultaneous machine health monitoring and fault diagnosis
Publisher: Elsevier BV
Date: 10-2023
Multi-dimensional variational mode decomposition for bearing-crack detection in wind turbines with large driving-speed variations
Publisher: Elsevier BV
Date: 02-2018
Marine CM: Condition identification of the cylinder liner-piston ring in a marine diesel engine using bispectrum analysis and artificial neural networks
Publisher: British Institute of Non-Destructive Testing (BINDT)
Date: 11-2013
A New Intelligent Fusion Method of Multi-Dimensional Sensors and Its Application to Tribo-System Fault Diagnosis of Marine Diesel Engines
Publisher: Springer Science and Business Media LLC
Date: 06-05-2012
Imaged wear debris separation for on-line monitoring using gray level and integrated morphological features
Publisher: Elsevier BV
Date: 08-2014
The development of three-dimensional imaging techniques of wear particle analysis
Publisher: Elsevier BV
Date: 03-1997
Prediction of wear trend of engines via on-line wear debris monitoring
Publisher: Elsevier BV
Date: 04-2018
Prediction of surface features of wear components based on surface characteristics of wear debris
Publisher: Elsevier BV
Date: 09-2007
Restoration of defocused ferrograph images using a large kernel convolutional neural network
Publisher: Elsevier BV
Date: 04-2019
Integrated model of BP neural network and CNN algorithm for automatic wear debris classification
Publisher: Elsevier BV
Date: 04-2019
A New Approach of Failure Warning and Monitoring Control System of Auxiliary Power Unit
Publisher: SAE International
Date: 10-11-2009
DOI: 10.4271/2009-01-3227
Two-dimensional fast Fourier transform and power spectrum for wear particle analysis
Publisher: Elsevier BV
Date: 07-1997
The investigation of nanotribology of UHMWPE in fluid using atomic force microscopy
Publisher: Wiley
Date: 23-07-2014
DOI: 10.1002/JBM.B.33249
Abstract: The fundamental understanding of the nanowear behavior of ultrahigh molecular weight polyethylene (UHMWPE) at a nanometer scale needs to be achieved to provide a better understanding of the initiating wear process and the potential causes of the wear particles generation of joint replacement. A nanotribology study was performed using atomic force microscope (AFM) tips sliding against UHMWPE surfaces in both water and bovine serum lubricants. Frictional properties of the nanocontact, and the geometry and mechanical features of the resulting scratches have been quantitatively characterized using AFM lateral force and PeakForce QNM modes. The results in this work indicated that the friction force and friction coefficient were smaller in serum lubricant than that in water. A normal load of 120 nN was the transition point for the plastic deformation of the material. The plastic deformation and material accumulation evolute with the increase of applied normal loads. Material pileup formed at the edges of the scratch, but they were not symmetrical due to the asymmetrical geometry of the silicon AFM tip. The height of the material pileup on the right side was approximately 40-70% of the pileup on the left side. The information may be useful for developing strategies for surface finishing techniques, which can control and minimize the production of asymmetric asperity and the resulting pileup with particular features. Furthermore, the moduli of the pileups were much larger than that of the fresh UHMWPE, which had the moduli greater than those of the inner scratch area. This suggested that stress concentration at these points could cause the pileup to be more susceptible to further wear processes, and eventually result in detaching from the bulk material.
A gearbox fault diagnosis method based on frequency-modulated empirical mode decomposition and support vector machine
Publisher: SAGE Publications
Date: 03-11-2016
Abstract: During the operation process of a gearbox, the vibration signals can reflect the dynamic states of the gearbox. The feature extraction of the vibration signal will directly influence the accuracy and effectiveness of fault diagnosis. One major challenge associated with the extraction process is the mode mixing, especially under such circumstance of intensive frequency. A novel fault diagnosis method based on frequency-modulated empirical mode decomposition is proposed in this paper. Firstly, several stationary intrinsic mode functions can be obtained after the initial vibration signal is processed using frequency-modulated empirical mode decomposition method. Using the method, the vibration signal feature can be extracted in unworkable region of the empirical mode decomposition. The method has the ability to separate such close frequency components, which overcomes the major drawback of the conventional methods. Numerical simulation results showed the validity of the developed signal processing method. Secondly, energy entropy was calculated to reflect the changes in vibration signals in relation to faults. At last, the energy distribution could serve as eigenvector of support vector machine to recognize the dynamic state and fault type of the gearbox. The analysis results from the gearbox signals demonstrate the effectiveness and veracity of the diagnosis approach.
Development of Mixing Methods of UHMWPE/Carbon Nanotubes (CNT) Composites for Use in Artificial Joints
Publisher: Trans Tech Publications, Ltd.
Date: 06-2017
DOI: 10.4028/WWW.SCIENTIFIC.NET/KEM.739.81
Abstract: This study focused on the mixing methods used in the manufacture of ultra-high molecular weight polyethylene (UHMWPE) reinforced by multi-walled carbon nanotubes (MWCNTs) and how these methods enhance the wear performance of the composite. Two mixing methods, ball milling and ultra-sonication, were used in this study, and the most effective forms of these mixing methods were analysed to allow an effective comparison. The performance of the mixing methods was evaluated through a comparison of wear volume loss, coefficient of friction, and hardness. The optimal power and mixing time for sonication in this study was determined and composites with 0.5% CNTs w/w concentration were conformed. It was evidenced that the mixing process influence the mechanical and wear performance of UHMWPE / MWCNTs composites and ultrasonication was identified as an effective mixing method that improves significantly the wear performance of the composite.
Detection of gear cracks in a complex gearbox of wind turbines using supervised bounded component analysis of vibration signals collected from multi-channel sensors
Publisher: Elsevier BV
Date: 06-2016
Osteoarthritis diagnosis using wear particle analysis technique: Investigation of correlation between particle and cartilage surface in walking process
Publisher: Elsevier BV
Date: 02-2007
Three dimensional surface characterization of human cartilages at a micron and nanometre scale
Publisher: Elsevier BV
Date: 04-2013
Properties of Census Dual System Population Size Estimators
Publisher: Wiley
Date: 21-11-2011
An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis
Publisher: Elsevier BV
Date: 08-2003
PDMS/MWCNT nanocomposite films for underwater sound absorption applications
Publisher: Springer Science and Business Media LLC
Date: 13-01-2020
Investigation of the geometries and surface topographies of UHMWPE wear particles
Publisher: Elsevier BV
Date: 10-2013
Investigations of wear particles and selected cytokines in human osteoarthritic knee joints
Publisher: SAGE Publications
Date: 11-2014
Abstract: Inflammation of the synovial membrane (synovitis) is considered to drive the process that leads to osteoarthritis. However, the relationships between the mediators of inflammation and the properties of wear particles are not fully understood. In this study, the levels of IL-6 and IL-8 were assessed in different grades of osteoarthritis to determine whether their concentrations in the synovial fluid correlate with specific characteristics of wear particles. This study has found that the size, adhesion and nano-surface roughness of wear particles have medium strong to strong correlations with IL-6 and IL-8. This study provided evidence that the characteristics of wear particles contain valuable information for grading the disease process and the need for further evaluation of the association of properties of wear particles and the inflammatory process.
Wear Performance of UHMWPE and Reinforced UHMWPE Composites in Arthroplasty Applications: A Review
Publisher: MDPI AG
Date: 18-05-2015
Tribological properties and tribochemical analysis of nano-cerium oxide and sulfurized isobutene in titanium complex grease
Publisher: Elsevier BV
Date: 2016
Study of the nano-mechanical properties of human knee cartilage in different wear conditions
Publisher: Elsevier BV
Date: 04-2013
A new swarm intelligence optimized multiclass multi-kernel relevant vector machine: An experimental analysis in failure diagnostics of diesel engines
Publisher: SAGE Publications
Date: 26-12-2017
Abstract: 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.
Surface Characterization Using Wavelet Theory and Confocal Laser Scanning Microscopy
Publisher: ASME International
Date: 04-2005
DOI: 10.1115/1.1866161
Abstract: Surface characterization, particularly roughness analysis, is very important for a wide range of applications including wear assessment. This paper proposes a set of methods and techniques to acquire appropriate images using confocal laser scanning microscopy, to separate roughness, waviness, and form using wavelet theory, and to characterize surface roughness for engineering surfaces and surfaces of small particles. Two application ex les on engineering surfaces and wear particles have been presented in the paper to demonstrate that the method developed in this study can be used to measure surface roughness reliably and precisely. A guide on how to determine the iris size, step size, and objective lens has been scientifically provided according to theoretical analysis and experimental results.
A Fault Diagnosis Approach for Gears Using Multidimensional Features and Intelligent Classifier
Publisher: SAGE Publications
Date: 11-2010
DOI: 10.1260/0957-4565.41.10.76
Abstract: Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults
An integrated intelligence system for wear debris analysis
Publisher: Elsevier BV
Date: 05-2002
The gearbox wears state monitoring and evaluation based on on-line wear debris features
Publisher: Elsevier BV
Date: 04-2019
Motion-Blurred Particle Image Restoration for On-Line Wear Monitoring
Publisher: MDPI AG
Date: 08-04-2015
DOI: 10.3390/S150408173
Wear-debris analysis in expert systems
Publisher: Springer Science and Business Media LLC
Date: 2001
Quantitative wear particle analysis for osteoarthritis assessment
Publisher: SAGE Publications
Date: 05-10-2017
Abstract: Osteoarthritis is a degenerative joint disease that affects millions of people worldwide. The aims of this study were (1) to quantitatively characterise the boundary and surface features of wear particles present in the synovial fluid of patients, (2) to select key numerical parameters that describe distinctive particle features and enable osteoarthritis assessment and (3) to develop a model to assess osteoarthritis conditions using comprehensive wear debris information. Discriminant analysis was used to statistically group particles based on differences in their numerical parameters. The analysis methods agreed with the clinical osteoarthritis grades in 63%, 50% and 61% of particles for no osteoarthritis, mild osteoarthritis and severe osteoarthritis, respectively. This study has revealed particle features specific to different osteoarthritis grades and provided further understanding of the cartilage degradation process through wear particle analysis – the technique that has the potential to be developed as an objective and minimally invasive method for osteoarthritis diagnosis.
Bridging the trust gap: Evaluating feature relevance in neural network-based gear wear mechanism analysis with explainable AI
Publisher: Elsevier BV
Date: 09-2023
Development of a new protocol for testing bath sponge quality
Publisher: Elsevier BV
Date: 10-2007
The use of the fractal description to characterize engineering surfaces and wear particles
Publisher: Elsevier BV
Date: 08-2003
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