Recursive actuator fault detection and diagnosis for emergency landing of UASs

This paper presents a practical recursive fault detection and diagnosis (FDD) scheme for online identification of actuator faults for unmanned aerial systems (UASs) based on the unscented Kalman filtering (UKF) method. The proposed FDD algorithm aims to monitor health status of actuators and provide indication of actuator faults with reliability, offering necessary information for the design of fault-tolerant flight control systems to compensate for side-effects and improve fail-safe capability when actuator faults occur. The fault detection is conducted by designing separate UKFs to detect aileron and elevator faults using a nonlinear six degree-of-freedom (DOF) UAS model. The fault diagnosis is achieved by isolating true faults by using the Bayesian Classifier (BC) method together with a decision criterion to avoid false alarms. High-fidelity simulations with and without measurement noise are conducted with practical constraints considered for typical actuator fault scenarios, and the proposed FDD exhibits consistent effectiveness in identifying occurrence of actuator faults, verifying its suitability for integration into the design of fault-tolerant flight control systems for emergency landing of UASs.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Condition monitoring and fault diagnosis of diesel engines using instantaneous angular speed analysis

Continuous monitoring of diesel engine performance is critical for early detection of fault developments in an engine before they materialize into a functional failure. Instantaneous crank angular speed (IAS) analysis is one of a few nonintrusive condition monitoring techniques that can be utilized for such a task. Furthermore, the technique is more suitable for mass industry deployments than other non-intrusive methods such as vibration and acoustic emission techniques due to the low instrumentation cost, smaller data size and robust signal clarity since IAS is not affected by the engine operation noise and noise from the surrounding environment. A combination of IAS and order analysis was employed in this experimental study and the major order component of the IAS spectrum was used for engine loading estimation and fault diagnosis of a four-stroke four-cylinder diesel engine. It was shown that IAS analysis can provide useful information about engine speed variation caused by changing piston momentum and crankshaft acceleration during the engine combustion process. It was also found that the major order component of the IAS spectra directly associated with the engine firing frequency (at twice the mean shaft rotating speed) can be utilized to estimate engine loading condition regardless of whether the engine is operating at healthy condition or with faults. The amplitude of this order component follows a distinctive exponential curve as the loading condition changes. A mathematical relationship was then established in the paper to estimate the engine power output based on the amplitude of this order component of the IAS spectrum. It was further illustrated that IAS technique can be employed for the detection of a simulated exhaust valve fault in this study.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Amino group positions dependent morphology and coverage of electropolymerized metallophthalocyanine (M = Ni and Co) films on electrode surfaces

Electropolymerized films of teraaminometallophthalocyanines (MTAPc; M = Ni and Co) with amino groups at α- (4α-MTAPc) and β- (4β-MTAPc) positions were prepared on glassy carbon (GC) and indium tin oxide (ITO) electrodes. It was found that the electropolymerization growth rate of 4α-MTAPc was less than that of 4β-MTAPc prepared under identical conditions. Further, the surface coverage of the polymerized 4β-MTAPc film was greater than that of 4α-MTAPc polymerized film. Atomic force microscopy (AFM), X-ray diffraction (XRD) and UV–visible spectroscopic studies were carried out for the polymerized films of 4α-NiIITAPc (p-4α-NiIITAPc) and 4β-NiIITAPc (p-4β-NiIITAPc) alone because both Ni(II) and Co(II) polymerized films show similar trend in electropolymerization and surface coverage values. AFM images show that p-4α-NiIITAPc film contains islands and the thickness of this film was nearly three times less than that of p-4β-NiIITAPc. XRD patterns for the two polymerized films reveal that p-4β-NiIITAPc film was relatively more crystalline than p-4α-NiIITAPc film. Further, the compactness of these films was scrutinized from their barrier properties toward [Fe(CN)6]3−/4− redox couple. The differences in the polymerization growth rate of 4α-MTAPc and 4β-MTAPc, and the thicknesses of the resultant polymerized films suggest that unlike 4β-MTAPc one or two amino groups might have not involved in electropolymerization in the case of 4α-MTAPc. Further, the influence of surface coverage on the electrocatalytic properties of the polymerized films was studied by taking p-4β-CoIITAPc and p-4α-CoIITAPc films as examples. The electrocatalytic oxygen reduction current was almost same at both the electrodes suggesting that only the surface species were involved in the electrocatalytic reduction of oxygen.

Acute injuries in track and field athletes: A 3-year observational study at the Penn Relays Carnival with epidemiology and medical coverage implications

Background Few studies have examined acute injuries in track and field in both elite and sub-elite athletes. Purpose To observe the absolute and relative rates of injury in track and field athletes across a wide range of competition levels and ages during three years of the Penn Relays Carnival to assist with future medical coverage planning and injury prevention strategies. Study design: Descriptive epidemiology study. Methods Over a 3-year period all injuries treated by the medical staff were recorded on a standardised injury report form. Absolute injury rates (absolute number of injuries) and relative injury rates (number of injuries per 1000 participants) were determined and odds ratios (OR) of injury rates were calculated between sexes, competition levels and events. Injuries were also broken down into major or minor medical or orthopedic injuries. Results Throughout the study period 48,473 competing athletes participated in the Penn Relays Carnival, and 436 injuries were sustained. For medical coverage purposes, the relative rate of injury subtypes was greatest for minor orthopedic injuries (5.71 injuries per 1000 participants), followed by minor medical injuries (3.42 injuries per 1000 participants), major medical injuries (0.69 injuries per 1000 participants) and major orthopedic injuries (0.18 injuries per 1000 participants). College/elite level athletes displayed the lowest relative injury rate (7.99 injuries per 1000 participants), which was significantly less than high school (9.87 injuries per 1000 participants) and masters level athletes (16.33 injuries per 1000 participants). Males displayed a greater likelihood of suffering a minor orthopedic injury compared to females (OR = 1.36, 95% CI = 1.06 to 1.75; χ2 = 5.73, p = 0.017) but were less likely to sustain a major medical injury (OR = 0.33, 95% CI = 0.15 to 0.75; χ2 = 7.75, p = 0.005). Of the three most heavily participated in events, the 4 x 400m relay displayed the greatest relative injury rate (13.6 injuries per 1000 participants) compared to the 4 x 100 and 4 x 200m relay. Conclusions Medical coverage teams for future large scale track and field events need to plan for at least two major orthopedic and seven major medical injuries per 1000 participants. Male track and field athletes, particularly masters level male athletes, are at greater risk of injury compared to other genders and competition levels.

It wasn't my fault : does crash culpability improve predictive outcomes in a self-report study?

Introduction: Research that has focused on the ability of self-report assessment tools to predict crash outcomes has proven to be mixed. As a result, researchers are now beginning to explore whether examining culpability of crash involvement can subsequently improve this predictive efficacy. This study reports on the application of the Manchester Driver Behaviour Questionnaire (DBQ) to predict crash involvement among a sample of general Queensland motorists, and in particular, whether including a crash culpability variable improves predictive outcomes. Surveys were completed by 249 general motorists on-line or via a pen-and-paper format. Results: Consistent with previous research, a factor analysis revealed a three factor solution for the DBQ accounting for 40.5% of the overall variance. However, multivariate analysis using the DBQ revealed little predictive ability of the tool to predict crash involvement. Rather, exposure to the road was found to be predictive of crashes. An analysis into culpability revealed 88 participants reported being “at fault” for their most recent crash. Corresponding between and multi-variate analyses that included the culpability variable did not result in an improvement in identifying those involved in crashes. Conclusions: While preliminary, the results suggest that including crash culpability may not necessarily improve predictive outcomes in self-report methodologies, although it is noted the current small sample size may also have had a deleterious effect on this endeavour. This paper also outlines the need for future research (which also includes official crash and offence outcomes) to better understand the actual contribution of self-report assessment tools, and culpability variables, to understanding and improving road safety.

Reliable Fault Diagnosis for Low-Speed Bearings Using Individually Trained Support Vector Machines With Kernel Discriminative Feature Analysis

This paper proposes a highly reliable fault diagnosis approach for low-speed bearings. The proposed approach first extracts wavelet-based fault features that represent diverse symptoms of multiple low-speed bearing defects. The most useful fault features for diagnosis are then selected by utilizing a genetic algorithm (GA)-based kernel discriminative feature analysis cooperating with one-against-all multicategory support vector machines (OAA MCSVMs). Finally, each support vector machine is individually trained with its own feature vector that includes the most discriminative fault features, offering the highest classification performance. In this study, the effectiveness of the proposed GA-based kernel discriminative feature analysis and the classification ability of individually trained OAA MCSVMs are addressed in terms of average classification accuracy. In addition, the proposedGA- based kernel discriminative feature analysis is compared with four other state-of-the-art feature analysis approaches. Experimental results indicate that the proposed approach is superior to other feature analysis methodologies, yielding an average classification accuracy of 98.06% and 94.49% under rotational speeds of 50 revolutions-per-minute (RPM) and 80 RPM, respectively. Furthermore, the individually trained MCSVMs with their own optimal fault features based on the proposed GA-based kernel discriminative feature analysis outperform the standard OAA MCSVMs, showing an average accuracy of 98.66% and 95.01% for bearings under rotational speeds of 50 RPM and 80 RPM, respectively.

Feature article coverage of Australian out-of-home care : portrayals and policy reform

Mandatory reporting is a key aspect of Australia’s approach to protecting children and is incorporated into all jurisdictions’ legislation, albeit in a variety of forms. In this article we examine all major newspaper’s coverage of mandatory reporting during an 18-month period in 2008-2009, when high-profile tragedies and inquiries occurred and significant policy and reform agendas were being debated. Mass media utilise a variety of lenses to inform and shape public responses and attitudes to reported events. We use frame analysis to identify the ways in which stories were composed and presented, and how language portrayed this contested area of policy. The results indicate that within an overall portrayal of system failure and the need for reform, the coverage placed major responsibility on child protection agencies for the over-reporting, under-reporting, and overburdened system identified, along with the failure of mandatory reporting to reduce risk. The implications for ongoing reform are explored along with the need for robust research to inform debate about the merits of mandatory reporting.

Coverage algorithms for an under-actuated car-like vehicle in an uncertain environment

A coverage algorithm is an algorithm that deploys a strategy as to how to cover all points in terms of a given area using some set of sensors. In the past decades a lot of research has gone into development of coverage algorithms. Initially, the focus was coverage of structured and semi-structured indoor areas, but with time and development of better sensors and introduction of GPS, the focus has turned to outdoor coverage. Due to the unstructured nature of an outdoor environment, covering an outdoor area with all its obstacles and simultaneously performing reliable localization is a difficult task. In this paper, two path planning algorithms suitable for solving outdoor coverage tasks are introduced. The algorithms take into account the kinematic constraints of an under-actuated car-like vehicle, minimize trajectory curvatures, and dynamically avoid detected obstacles in the vicinity, all in real-time. We demonstrate the performance of the coverage algorithm in the field by achieving 95% coverage using an autonomous tractor mower without the aid of any absolute localization system or constraints on the physical boundaries of the area.

Application of anomaly technique in wind turbine bearing fault detection

Bearing faults are the most common cause of wind turbine failures. Unavailability and maintenance cost of wind turbines are becoming critically important, with their fast growing in electric networks. Early fault detection can reduce outage time and costs. This paper proposes Anomaly Detection (AD) machine learning algorithms for fault diagnosis of wind turbine bearings. The application of this method on a real data set was conducted and is presented in this paper. For validation and comparison purposes, a set of baseline results are produced using the popular one-class SVM methods to examine the ability of the proposed technique in detecting incipient faults.

Coverage of international and Pacific news in the Fiji Times and The Australian

The debate over the nature and flow of international news has dominated intellectual debate about journalism practice for some time. Developing countries argued there was an imbalance in the nature and amount of international news concerning them. They argued that the Western media rarely reported on developing countries and when they did, reported predominantly negative news about developing countries. The debate led to calls for a New World Information and Communication Order (NWICO). A number of studies examined its arguments, many finding developing countries were indeed disadvantaged by the Western media. This study compared foreign news coverage in The Australian and The Fiji Times, with special attention on news from the Pacific Islands region. It found the coverage of the Pacific Islands was still grossly inadequate in both newspapers. The coverage consisted of only a small number of stories, which were predominantly negative, surprising especially in the case of The Fiji Times.