Compaction control of topography and fault network structure along strike-slip faults in sedimentary basins

Strike-slip faults commonly display structurally complex areas of positive or negative topography. Understanding the development of such areas has important implications for earthquake studies and hydrocarbon exploration. Previous workers identified the key factors controlling the occurrence of both topographic modes and the related structural styles. Kinematic and stress boundary conditions are of first-order relevance. Surface mass transport and material properties affect fault network structure. Experiments demonstrate that dilatancy can generate positive topography even under simple-shear boundary conditions. Here, we use physical models with sand to show that the degree of compaction of the deformed rocks alone can determine the type of topography and related surface fault network structure in simple-shear settings. In our experiments, volume changes of ∼5% are sufficient to generate localized uplift or subsidence. We discuss scalability of model volume changes and fault network structure and show that our model fault zones satisfy geometrical similarity with natural flower structures. Our results imply that compaction may be an important factor in the development of topography and fault network structure along strike-slip faults in sedimentary basins.

Bearing fault prognosis based on health state probability estimation

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

A UKF-based estimation strategy for actuator fault detection of UASs

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating correspond- ing velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Isolation and characterization of 21 polymorphic microsatellite loci in the iconic Australian lungfish, Neoceratodus forsteri, using the Ion Torrent next-generation sequencing platform

We isolated and characterized 21 microsatellite loci in the vulnerable and iconic Australian lungfish, Neoceratodus forsteri. Loci were screened across eight individuals from the Burnett River and 40 individuals from the Pine River. Genetic diversity was low with between one and six alleles per locus within populations and a maximum expected heterozygosity of 0.774. These loci will now be available to assess effective population sizes and genetic structure in N. forsteri across its natural range in South East Queensland, Australia.

Isolation of bioactive compounds that relate to the anti-platelet activity of Cymbopogon ambiguus

Infusions and decoctions of Cymbopogon ambiguus have been used traditionally in Australia for the treatment of headache, chest infections and muscle cramps. The aim of the present study was to screen and identify bioactive compounds from C. ambiguus that could explain this plant's anti-headache activity. A dichloromethane extract of C. ambiguus was identified as having activity in adenosine-diphosphate-induced human platelet aggregation and serotonin-release inhibition bioassays. Subsequent fractionation of this extract led to the isolation of four phenylpropenoids, eugenol, elemicin, Eugenol methylether and trans-isoelemicin. While both Eugenol and elemicin exhibited dose-dependent inhibition of ADP-induced human platelet serotonin release, only eugenol displayed potent inhibitory activity with an IC(50) value of 46.6 microM, in comparison to aspirin, with an IC(50) value of 46.1 microM. These findings provide evidence to support the therapeutic efficacy of C. ambiguus in the non-conventional treatment of Headache and Inflammatory conditions.

Isolation of two phenylethanoid glycosides from Eremophila gilesii

The leaves of Eremophila gilesii have been used traditionally to treat colds, headaches, sores, and chest pains. Our previous screening of Australian native plants showed that the methanol extract of the aerial parts of E. gilesii demonstrated notable inhibition of ADP-induced human platelet aggregation and serotonin release. Subsequent fractionation on the methanol extract led to the isolation of two phenylethanoid glycosides, verbascoside (1) and poliumoside (2). This is the first study reporting the presence of phenylethanoid glycosides in E. gilesii.

Nonlinear actuator fault detection for small-scale UAS

This paper presents a recursive strategy for online detection of actuator faults on a unmanned aerial system (UAS) subjected to accidental actuator faults. The proposed detection algorithm aims to provide a UAS with the capability of identifying and determining characteristics of actuator faults, offering necessary flight information for the design of fault-tolerant mechanism to compensate for the resultant side-effect when faults occur. The proposed fault detection strategy consists of a bank of unscented Kalman filters (UKFs) with each one detecting a specific type of actuator faults and estimating corresponding velocity and attitude information. Performance of the proposed method is evaluated using a typical nonlinear UAS model and it is demonstrated in simulations that our method is able to detect representative faults with a sufficient accuracy and acceptable time delay, and can be applied to the design of fault-tolerant flight control systems of UASs.

Isolation and use of chromosome 1 probes for linkage studies on Charcot-Marie-Tooth disease

Nine probes were isolated from a human chromosome 1 enriched library and mapped to regions of chromosome 1 using somatic cell hybrid lines. One clone, LR67, which mapped 1q12→q23 detected a BglI RFLP. This probe, as well as 4 other known chromosome 1 markers, α-spectrin, Factor XIIIB, DR10 and DR78, were used for linkage studies in 15 Charcot-Marie-Tooth disease (CMT1) families. Close linking of CMT1 to any of the 5 markers was not indicated. Total lod scores excluded linkage of CMT1 to LR67 and to DR10 at 5 cM or less, to DR78 and 10 cM or less, α-spectrin at 15 cM or less and Factor XIIIB at 20 cM or less. Possible linkage, however, was shown between LR67 and CMT1 at a distance of 30 cM. Also linkage at a distance of 5 cM was detected between this probe and α-spectrin.

Fault detection in wind turbine : a systematic literature review

Wind power has become one of the popular renewable resources all over the world and is anticipated to occupy 12% of the total global electricity generation capacity by 2020. For the harsh environment that the wind turbine operates, fault diagnostic and condition monitoring are important for wind turbine safety and reliability. This paper employs a systematic literature review to report the most recent promotions in the wind turbine fault diagnostic, from 2005 to 2012. The frequent faults and failures in wind turbines are considered and different techniques which have been used by researchers are introduced, classified and discussed.

Fault symptoms of rolling element bearings under variable operating conditions - a multi domain analysis

Diagnostics of rolling element bearings have been traditionally developed for constant operating conditions, and sophisticated techniques, like Spectral Kurtosis or Envelope Analysis, have proven their effectiveness by means of experimental tests, mainly conducted in small-scale laboratory test-rigs. Algorithms have been developed for the digital signal processing of data collected at constant speed and bearing load, with a few exceptions, allowing only small fluctuations of these quantities. Owing to the spreading of condition based maintenance in many industrial fields, in the last years a need for more flexible algorithms emerged, asking for compatibility with highly variable operating conditions, such as acceleration/deceleration transients. This paper analyzes the problems related with significant speed and load variability, discussing in detail the effect that they have on bearing damage symptoms, and propose solutions to adapt existing algorithms to cope with this new challenge. In particular, the paper will i) discuss the implication of variable speed on the applicability of diagnostic techniques, ii) address quantitatively the effects of load on the characteristic frequencies of damaged bearings and iii) finally present a new approach for bearing diagnostics in variable conditions, based on envelope analysis. The research is based on experimental data obtained by using artificially damaged bearings installed on a full scale test-rig, equipped with actual train traction system and reproducing the operation on a real track, including all the environmental noise, owing to track irregularity and electrical disturbances of such a harsh application.

Effectiveness of MED for Fault Diagnosis in Roller Bearings

Diagnostics of rolling element bearings is usually performed by means of vibration signals measured by accelerometers placed in the proximity of the bearing under investigation. The aim is to monitor the integrity of the bearing components, in order to avoid catastrophic failures, or to implement condition based maintenance strategies. In particular, the trend in this field is to combine in a single algorithm different signal-enhancement and signal-analysis techniques. Among the first ones, Minimum Entropy Deconvolution (MED) has been pointed out as a key tool able to highlight the effect of a possible damage in one of the bearing components within the vibration signal. This paper presents the application of this technique to signals collected on a simple test-rig, able to test damaged industrial roller bearings in different working conditions. The effectiveness of the technique has been tested, comparing the results of one undamaged bearing with three bearings artificially damaged in different locations, namely on the inner race, outer race and rollers. Since MED performances are dependent on the filter length, the most suitable value of this parameter is defined on the basis of both the application and measured signals. This represents an original contribution of the paper.

Bearing Fault Diagnostics Using the Spectral Pattern Recognition

In the field of diagnostics of rolling element bearings, the development of sophisticated techniques, such as Spectral Kurtosis and 2nd Order Cyclostationarity, extended the capability of expert users to identify not only the presence, but also the location of the damage in the bearing. Most of the signal-analysis methods, as the ones previously mentioned, result in a spectrum-like diagram that presents line frequencies or peaks in the neighbourhood of some theoretical characteristic frequencies, in case of damage. These frequencies depend only on damage position, bearing geometry and rotational speed. The major improvement in this field would be the development of algorithms with high degree of automation. This paper aims at this important objective, by discussing for the first time how these peaks can draw away from the theoretical expected frequencies as a function of different working conditions, i.e. speed, torque and lubrication. After providing a brief description of the peak-patterns associated with each type of damage, this paper shows the typical magnitudes of the deviations from the theoretical expected frequencies. The last part of the study presents some remarks about increasing the reliability of the automatic algorithm. The research is based on experimental data obtained by using artificially damaged bearings installed in a gearbox.

Predicting fault-prone software modules with rank sum classification

The detection and correction of defects remains among the most time consuming and expensive aspects of software development. Extensive automated testing and code inspections may mitigate their effect, but some code fragments are necessarily more likely to be faulty than others, and automated identification of fault prone modules helps to focus testing and inspections, thus limiting wasted effort and potentially improving detection rates. However, software metrics data is often extremely noisy, with enormous imbalances in the size of the positive and negative classes. In this work, we present a new approach to predictive modelling of fault proneness in software modules, introducing a new feature representation to overcome some of these issues. This rank sum representation offers improved or at worst comparable performance to earlier approaches for standard data sets, and readily allows the user to choose an appropriate trade-off between precision and recall to optimise inspection effort to suit different testing environments. The method is evaluated using the NASA Metrics Data Program (MDP) data sets, and performance is compared with existing studies based on the Support Vector Machine (SVM) and Naïve Bayes (NB) Classifiers, and with our own comprehensive evaluation of these methods.