Determining vibration signature of a defective planet bearing in a wind turbine epicyclic gearbox

Planet bearings of wind turbine epicyclic gearboxes are considered as one of the most critical components due to their high failure rate. In order to develop effective vibration based detection algorithms for these bearings, a thorough understanding of their vibration signature is required. In this paper, we investigate the vibration behaviour of an epicyclic gearbox in the presence of a defective planet bearing both theoretically and experimentally. We also identify different sources of modulation sidebands using an analytical model which includes ring gear flexibility and planet bearing defects. The findings from this work will help engineers to develop more effective fault detection algorithms.

The sensitivity of vibration characteristics of reinforced concrete beams under incremental static and cyclic loading

The use of changes in vibration properties for global damage detection and monitoring of existing concrete structures has received great research attention in the last three decades. To track changes in vibration properties experimentally, structures have been artificially damaged by a variety of scenarios. However, this procedure does not represent realistically the whole design-life degradation of concrete structures. This paper presents experimental work on a set of damaged reinforced concrete beams due to different loading regimes to assess the sensitivity of vibration characteristics. Of the total set, three beams were subject to incremental static loading up to failure to simulate overloading, and two beams subject to 15 million loading cycles with varying amplitudes to produce an accelerated whole-life degradation scenario. To assess the vibration behaviour in both cases, swept sine and harmonic excitations were conducted at every damage level. The results show that resonant frequencies are not sensitive enough to damage due to cyclic loading, whereas cosh spectral and root mean square distances are more sensitive, yet more scattered. In addition, changes in non-linearity follow a softening trend for beams under incremental static loading, whilst they are significantly inconsistent for beams under cyclic loading. Amongst all examined characteristics, changes in modal stiffness are found to be most sensitive to damage and least scattered, but modal stiffness is tedious to compute due mainly to the difficulty of constructing restoring force surfaces from field measurements. © (2013) Trans Tech Publications.

Achievements and challenges in machine vision-based inspection of large concrete structures

Large concrete structures need to be inspected in order to assess their current physical and functional state, to predict future conditions, to support investment planning and decision making, and to allocate limited maintenance and rehabilitation resources. Current procedures in condition and safety assessment of large concrete structures are performed manually leading to subjective and unreliable results, costly and time-consuming data collection, and safety issues. To address these limitations, automated machine vision-based inspection procedures have increasingly been proposed by the research community. This paper presents current achievements and open challenges in vision-based inspection of large concrete structures. First, the general concept of Building Information Modeling is introduced. Then, vision-based 3D reconstruction and as-built spatial modeling of concrete civil infrastructure are presented. Following that, the focus is set on structural member recognition as well as on concrete damage detection and assessment exemplified for concrete columns. Although some challenges are still under investigation, it can be concluded that vision-based inspection methods have significantly improved over the last 10 years, and now, as-built spatial modeling as well as damage detection and assessment of large concrete structures have the potential to be fully automated.

Relationship between load/unload response ratio and damage variable and its application

The physics-based parameter: load/unload response ratio (LURR) was proposed to measure the proximity of a strong earthquake, which achieved good results in earthquake prediction. As LURR can be used to describe the damage degree of the focal media qualitatively, there must be a relationship between LURR and damage variable (D) which describes damaged materials quantitatively in damage mechanics. Hence, based on damage mechanics and LURR theory, taking Weibull distribution as the probability distribution function, the relationship between LURR and D is set up and analyzed. This relationship directs LURR applied in damage analysis of materials quantitatively from being qualitative earlier, which not only provides the LURR method with a more solid basis in physics, but may also give a new approach to the damage evaluation of big scale structures and prediction of engineering catastrophic failure. Copyright (c) 2009 John Wiley & Sons, Ltd.

Relationship between load/unload response ratio and damage variable and its application

The physics-based parameter: load/unload response ratio (LURR) was proposed to measure the proximity of a strong earthquake, which achieved good results in earthquake prediction. As LURR can be used to describe the damage degree of the focal media qualitatively, there must be a relationship between LURR and damage variable (D) which describes damaged materials quantitatively in damage mechanics. Hence, based on damage mechanics and LURR theory, taking Weibull distribution as the probability distribution function, the relationship between LURR and D is set up and analyzed. This relationship directs LURR applied in damage analysis of materials quantitatively from being qualitative earlier, which not only provides the LURR method with a more solid basis in physics, but may also give a new approach to the damage evaluation of big scale structures and prediction of engineering catastrophic failure. Copyright (c) 2009 John Wiley & Sons, Ltd.

Equivalent-barotropic definition of tropospheric mean temperature

An equivalent-barotropic (EB) description of the tropospheric temperature field is derived from the geostrophic empirical mode (GEM) in the form of a scalar function Gamma(p, phi), where p is pressure and phi is 300-850-mb thickness. Baroclinic parameter phi plays the role of latitude at each longitudinal section. Compared with traditional Eulerian-mean methods, GEM defines a mean field in baroclinic streamfunction space with a time scale much longer than synoptic variability. It prompts an EB concept that is only based on a baroclinic field. Monthly GEM fields are diagnosed from NCEP-NCAR reanalysis data and account for more than 90% of the tropospheric thermal variance. The circumglobal composite of GEM fields exhibits seasonal, zonal, and hemispheric asymmetries, with larger rms errors occurring in winter and in the Northern Hemisphere (NH). Zonally asymmetric features and planetary deviation from EB are seen in the NH winter GEM. Reconstruction of synoptic sections and correlation analysis reveal that the tropospheric temperature field is EB at the leading order and has a 1-day phase lag behind barotropic variations in extratropical regions.

Strategies that students use to trace code: an analysis based in grounded theory

Fitzgerald, S., Simon, B., and Thomas, L. 2005. Strategies that students use to trace code: an analysis based in grounded theory. In Proceedings of the First international Workshop on Computing Education Research (Seattle, WA, USA, October 01 - 02, 2005). ICER '05. ACM, New York, NY, 69-80

Bridge-vehicle interaction for structural health monitoring: potentials, applications, and limitations

Structural Health Monitoring (SHM) is an integral part of infrastructure maintenance and management systems due to socio-economic, safety and security reasons. The behaviour of a structure under vibration depends on structure characteristics. The change of structure characteristics may suggest the change in system behaviour due to the presence of damage(s) within. Therefore the consistent, output signal guided, and system dependable markers would be convenient tool for the online monitoring, the maintenance, rehabilitation strategies, and optimized decision making policies as required by the engineers, owners, managers, and the users from both safety and serviceability aspects. SHM has a very significant advantage over traditional investigations where tangible and intangible costs of a very high degree are often incurred due to the disruption of service. Additionally, SHM through bridge-vehicle interaction opens up opportunities for continuous tracking of the condition of the structure. Research in this area is still in initial stage and is extremely promising. This PhD focuses on using bridge-vehicle interaction response for SHM of damaged or deteriorating bridges to monitor or assess them under operating conditions. In the present study, a number of damage detection markers have been investigated and proposed in order to identify the existence, location, and the extent of an open crack in the structure. The theoretical and experimental investigation has been conducted on Single Degree of Freedom linear system, simply supported beams. The novel Delay Vector Variance (DVV) methodology has been employed for characterization of structural behaviour by time-domain response analysis. Also, the analysis of responses of actual bridges using DVV method has been for the first time employed for this kind of investigation.

Power system parameters forecasting using Hilbert-Huang transform and machine learning

A novel hybrid data-driven approach is developed for forecasting power system parameters with the goal of increasing the efficiency of short-term forecasting studies for non-stationary time-series. The proposed approach is based on mode decomposition and a feature analysis of initial retrospective data using the Hilbert-Huang transform and machine learning algorithms. The random forests and gradient boosting trees learning techniques were examined. The decision tree techniques were used to rank the importance of variables employed in the forecasting models. The Mean Decrease Gini index is employed as an impurity function. The resulting hybrid forecasting models employ the radial basis function neural network and support vector regression. A part from introduction and references the paper is organized as follows. The second section presents the background and the review of several approaches for short-term forecasting of power system parameters. In the third section a hybrid machine learningbased algorithm using Hilbert-Huang transform is developed for short-term forecasting of power system parameters. Fourth section describes the decision tree learning algorithms used for the issue of variables importance. Finally in section six the experimental results in the following electric power problems are presented: active power flow forecasting, electricity price forecasting and for the wind speed and direction forecasting.

Network analysis of 2-mode data

Network analysis is distinguished from traditional social science by the dyadic nature of the standard data set. Whereas in traditional social science we study monadic attributes of individuals, in network analysis we study dyadic attributes of pairs of individuals. These dyadic attributes (e.g. social relations) may be represented in matrix form by a square 1-mode matrix. In contrast, the data in traditional social science are represented as 2-mode matrices. However, network analysis is not completely divorced from traditional social science, and often has occasion to collect and analyze 2-mode matrices. Furthermore, some of the methods developed in network analysis have uses in analysing non-network data. This paper presents and discusses ways of applying and interpreting traditional network analytic techniques to 2-mode data, as well as developing new techniques. Three areas are covered in detail: displaying 2-mode data as networks, detecting clusters and measuring centrality.

Development of ELISAs for detecting domoic acid, okadaic acid, and saxitoxin and their applicability for the detection of marine toxins in samples collected in Belgium

Okadaic acid, a diarrhetic shellfish poison, domoic acid, an amnesic shellfish poison, and saxitoxin, a paralytic shellfish poison, are three of the best-known marine biotoxins. The mouse bioassay is the method most widely used to detect many of these toxins in shellfish samples, but animal welfare concerns have prompted researchers to seek alternative methods of detection. In this study, three direct competitive enzyme-linked immunosorbent assays (ELISAs), each based on antibodies raised in rabbits against a conjugate of the analyte of interest, were developed for marine biotoxin detection in mussel, oyster, and scallop. One assay was for okadaic acid, one for saxitoxin, and one for domoic acid usually detected and quantified by high-performance liquid chromatography-ultraviolet light (HPLC-UV). All three compounds and a number of related toxins were extracted quickly and simply from the shellfish matrices with a 9 : 1 mixture of ethanol and water before analysis. The detection capabilities (CC values) of the developed ELISAs were 150 mu g kg-1 for okadaic acid, 50 mu g kg-1 for domoic acid, and 5 mu g kg-1 or less for saxitoxin. The assays proved satisfactory when used over a 4-month period for the analysis of 110 real samples collected in Belgium.

Detection of aberrant behaviour in home environments from video sequence

We introduce an application for the detection of aberrant behaviour within home based environments, with a focus on repetitive actions, which may be present in instance of persons suffering from dementia. Video based analysis has been used to detect the motion of a person within a given scene in addition to tracking them over the time. Detection of repetitive actions has been based on the analysis of a person's trajectory using the principles of signal correlation. Along with the ability to detect repetitive motion the developed approach also has the ability to measure the amount of activity/inactivity within the scene during a given period of time. Our results showed that the developed approach had the ability to detect all patterns in the data set examined with an average accuracy of 96.67%. This work has therefore validated the proposed concept of video based analysis for the detection of repetitive activities.

Production of a monoclonal antibody and its application in an optical biosensor based assay for the quantitative measurement of Pantothenic Acid (Vitamin B5) in foodstuffs

Pantothenicacid (PA), vitamin B5, is an essential B vitamin that may be fortified in food and as such requires robust and accurate methods of detection to meet compliance legislation. This study reports the production and characterisation of the first monoclonalantibody (MAb) specific for PA and the subsequent development of a surface plasmon resonance (SPR) biosensorassay for the quantification of PA. The developed assay was compared with an SPR based commercial kit which utilised a polyclonal antibody (PAb). Foodstuffs, including cereals (n = 43), infant formulas and baby food (n = 10) and fruit juices (n = 48) were analysed by both the MAb and PAb biosensorassays and comparison plots showed good correlation (R2 0.77–0.99). The results indicate that the MAb basedbiosensorassay is suitable for the measurement of PA in foodstuffs and has the added advantage of facilitating a constant, long term supply of identical antibody. Preliminary matrix studies suggest the MAb basedassay is an excellent candidate for further validation studies and routine quality assurance based analysis.

An ESPRIT-SAA-Based Detection Method for Broken Rotor Bar Fault in Induction Motors

This paper presents a novel detection method for broken rotor bar fault (BRB) in induction motors based on Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT) and Simulated Annealing Algorithm (SAA). The performance of ESPRIT is tested with simulated stator current signal of an induction motor with BRB. It shows that even with a short-time measurement data, the technique is capable of correctly identifying the frequencies of the BRB characteristic components but with a low accuracy on the amplitudes and initial phases of those components. SAA is then used to determine their amplitudes and initial phases and shows satisfactory results. Finally, experiments on a 3kW, 380V, 50Hz induction motor are conducted to demonstrate the effectiveness of the ESPRIT-SAA-based method in detecting BRB with short-time measurement data. It proves that the proposed method is a promising choice for BRB detection in induction motors operating with small slip and fluctuant load.

The effect of human-body shadowing on indoor UWB TOA-based ranging systems

Because of its superior time resolution, ultra-wide bandwidth (UWB) transmission can be a highly accurate technique for ranging in indoor localization systems. Nevertheless, the presence of obstructions may deteriorate the ranging performance of the system. Indoor environments are often densely populated with people. However, t h e effect of the human body presence has been scarcely investigated so far within the UWB ranging context. In this work, we investigate this effect by conducting UWB measurements and analyzing the ranging performance of the system. Two measurement campaigns were performed in the 3-5.5 GHz band, in an anechoic chamber and a laboratory environment. The range estimates were obtained by employing the threshold-based first peak detection technique. Analysis results revealed that the human body significantly attenuates the direct-path signal component. On the other hand, in this study it does not introduce a significant range error since the length difference between the diffracted paths around the body and the direct-path is less than the spatial resolution of the measurement setup. © 2012 IEEE.