Diesel engine problems, acoustic emission signals and simulated misfire faults

This paper discusses commonly encountered diesel engine problems and the underlying combustion related faults. Also discussed are the methods used in previous studies to simulate diesel engine faults and the initial results of an experimental simulation of a common combustion related diesel engine fault, namely diesel engine misfire. This experimental fault simulation represents the first step towards a comprehensive investigation and analysis into the characteristics of acoustic emission signals arising from combustion related diesel engine faults. Data corresponding to different engine running conditions was captured using in-cylinder pressure, vibration and acoustic emission transducers along with both crank-angle encoder and top-dead centre signals. Using these signals, it was possible to characterise the diesel engine in-cylinder pressure profiles and the effect of different combustion conditions on both vibration and acoustic emission signals.

Examining Exposure of Motorcycles at Signalized Intersections

Crash statistics in Singapore from 2001 to 2005 have shown that motorcycles are involved in about 54% of intersection crashes. The overall involvement of motorcycles in crashes as the not-at-fault party is about 43% but at intersections, the corresponding percentage is increased to 57%. Quasi-induced exposure estimates show that the motorcycle exposure rate at signalized intersections is 41.7% even though motorcycles account for only 19% of the vehicle population. This study seeks to examine in greater details, the problem of motorcycle exposure at signalized intersections. In particular, the exposure arising from potential crashes with red light running vehicles from the conflicting stream at four signalized intersections is investigated. The results show that motorcycles are more exposed because they tend to accumulate near the stop-line during the red phase to facilitate an earlier discharge during the initial period of the green which is the more vulnerable period. At sites where there are more weaving opportunities because the lanes are wider or where there are exclusive right-turn lanes, the accumulation is higher and hence an increased exposure is observed. The analysis also shows that the presence of heavy vehicles tends to decrease motorcycle exposure as their weaving opportunities become restricted as well as there is a greater reluctance for them to weave past or queue alongside the heavy vehicles and their effects intensify for narrower lane width.

Right-angle crash vulnerability of motorcycles at signalized intersections : mixed logit analysis

Motorcycles are particularly vulnerable in right-angle crashes at signalized intersections. The objective of this study is to explore how variations in roadway characteristics, environmental factors, traffic factors, maneuver types, human factors as well as driver demographics influence the right-angle crash vulnerability of motorcycles at intersections. The problem is modeled using a mixed logit model with a binary choice category formulation to differentiate how an at-fault vehicle collides with a not-at-fault motorcycle in comparison to other collision types. The mixed logit formulation allows randomness in the parameters and hence takes into account the underlying heterogeneities potentially inherent in driver behavior, and other unobserved variables. A likelihood ratio test reveals that the mixed logit model is indeed better than the standard logit model. Night time riding shows a positive association with the vulnerability of motorcyclists. Moreover, motorcyclists are particularly vulnerable on single lane roads, on the curb and median lanes of multi-lane roads, and on one-way and two-way road type relative to divided-highway. Drivers who deliberately run red light as well as those who are careless towards motorcyclists especially when making turns at intersections increase the vulnerability of motorcyclists. Drivers appear more restrained when there is a passenger onboard and this has decreased the crash potential with motorcyclists. The presence of red light cameras also significantly decreases right-angle crash vulnerabilities of motorcyclists. The findings of this study would be helpful in developing more targeted countermeasures for traffic enforcement, driver/rider training and/or education, safety awareness programs to reduce the vulnerability of motorcyclists.

Effectiveness of red light cameras on the right-angle crash involvement of motorcycles

Red light cameras (RLC) have been used to reduce right-angle collisions at signalized intersections. However, the effect of RLCs on motorcycle crashes has not been well investigated. The objective of this study is to evaluate the effectiveness of RLCs on motorcycle safety in Singapore. This is done by comparing their exposure, proneness of at-fault right-angle crashes as well as the resulting right-angle collisions at RLC with those at non-RLC sites. Estimating the crash vulnerability from not-at-fault crash involvements, the study shows that with a RLC, the relative crash vulnerability or crash-involved exposure of motorcycles at right-angle crashes is reduced. Furthermore, field investigation of motorcycle maneuvers reveal that at non-RLC arms, motorcyclists usually queue beyond the stop-line, facilitating an earlier discharge and hence become more exposed to the conflicting stream. However at arms with a RLC, motorcyclists are more restrained to avoid activating the RLC and hence become less exposed to conflicting traffic during the initial period of the green. The study also shows that in right-angle collisions, the proneness of at-fault crashes of motorcycles is lowest among all vehicle types. Hence motorcycles are more likely to be victims than the responsible parties in right-angle crashes. RLCs have also been found to be very effective in reducing at-fault crash involvements of other vehicle types which may implicate exposed motorcycles in the conflicting stream. Taking all these into account, the presence of RLCs should significantly reduce the vulnerability of motorcycles at signalized intersections.

Developing targeted countermeasures to improve motorcycle safety

The fatality and injury rate of motorcyclists per registered vehicle are higher than those of other motor vehicles by 13 and 7 times respectively. The crash involvement rate of motorcyclists as a victim party is 58% at intersections and as an offending party is 67% at expressways. Previous research efforts showed that the motorcycle safety programs are not very effective in improving motorcycle safety. This is perhaps due to inefficient design of safety program as specific causal factors may not be well explored. The objective of this study is to propose more sophisticated countermeasures and awareness programs for improving motorcycle safety after analyzing specific causal factors for motorcycle crashes at intersections and expressways. Methodologically this study applies the binary logistic model to explore the at-fault or not-at-fault crash involvement of motorcyclists at those locations. A number of explanatory variables representing roadway characteristics, environmental factors, motorcycle descriptions, and rider demographics have been evaluated. Results shows that the night time crash occurrence, presence of red light camera, lane position, rider age, licence class, and multivehicle collision significantly affect the fault of motorcyclists involved in crashes at intersections. On the other hand, the night time crash occurrence, lane position, speed limit, rider age, licence class, engine capacity, riding with pillion passenger, foreign registered motorcycles, and multivehicle collision has been found to be significant at expressways. Legislate to wear reflective clothes and using reflective markings on the motorcycles and helmets are suggested as an effective countermeasure for reducing their vulnerability. The red light cameras at intersections reduce the vulnerability of motorcycles and hence motorcycle flow and motorcycle crashes should be considered during installation of red light cameras. At signalized intersections, motorcyclists may be taught to follow correct movement and queuing rather than weaving through the traffic as it leads them to become victims of other motorists. The riding simulators in the training centers can be useful to demonstrate the proper movement and queuing at junctions. Riding with pillion passenger and excess speed at expressways are found to significantly influence the at at-fault crash involvement of the motorcyclists. Hence the motorcyclists should be advised to concentrate more on riding while riding with pillion passenger and encouraged to avoid excess speed at expressways. Very young and very older group of riders are found to be at-fault than middle aged groups. Hence this group of riders should be targeted for safety improvement. This can be done by arranging safety talks and programs in motorcycling clubs in colleges and universities as well as community riding clubs with high proportion of elderly riders. It is recommended that the driving centers may use the findings of this study to include in licensure program to make motorcyclists more aware of the different factors which expose the motorcyclists to crash risks so that more defensive riding may be needed.

They Say You are a Danger But You Are Not: Representations and Construction of the Moral Self in Narratives of 'Dangerous Individuals'

This article is based on an analysis of narratives of 26 offenders with mental health problems living in the United Kingdom. It explores the impact of an ascribed dangerous status and the construction of the self as moral and responsible in response to this label with reference to the literature on denial, deviance disavowal and other “techniques of neutralization” and Goffman's presentation of self. Two dominant strands are identified in relation to the construction of moral self-hood: “Not my fault” and “Good at heart” narratives. “Techniques of neutralization” are widely drawn on, particularly denial of responsibility in the “Not my fault” narratives that seek to explain anti-social behavior with reference to external forces such as a hostile environment inhibiting their ability to control their lives. In contrast, “Good at heart” narratives draw on the essentially good and moral nature of the inner-self. Both are used as evidence of sharing and adhering to moral norms in order to present an acceptable and credible self.

A review of current statistical methodologies for in storage sampling and surveillance in the grains industry

Effective, statistically robust sampling and surveillance strategies form an integral component of large agricultural industries such as the grains industry. Intensive in-storage sampling is essential for pest detection, Integrated Pest Management (IPM), to determine grain quality and to satisfy importing nation’s biosecurity concerns, while surveillance over broad geographic regions ensures that biosecurity risks can be excluded, monitored, eradicated or contained within an area. In the grains industry, a number of qualitative and quantitative methodologies for surveillance and in-storage sampling have been considered. Primarily, research has focussed on developing statistical methodologies for in storage sampling strategies concentrating on detection of pest insects within a grain bulk, however, the need for effective and statistically defensible surveillance strategies has also been recognised. Interestingly, although surveillance and in storage sampling have typically been considered independently, many techniques and concepts are common between the two fields of research. This review aims to consider the development of statistically based in storage sampling and surveillance strategies and to identify methods that may be useful for both surveillance and in storage sampling. We discuss the utility of new quantitative and qualitative approaches, such as Bayesian statistics, fault trees and more traditional probabilistic methods and show how these methods may be used in both surveillance and in storage sampling systems.

Integrated health prediction of bridge systems using dynamic object oriented Bayesian networks (DOOBNS)

The serviceability and safety of bridges are crucial to people’s daily lives and to the national economy. Every effort should be taken to make sure that bridges function safely and properly as any damage or fault during the service life can lead to transport paralysis, catastrophic loss of property or even casualties. Nonetheless, aggressive environmental conditions, ever-increasing and changing traffic loads and aging can all contribute to bridge deterioration. With often constrained budget, it is of significance to identify bridges and bridge elements that should be given higher priority for maintenance, rehabilitation or replacement, and to select optimal strategy. Bridge health prediction is an essential underpinning science to bridge maintenance optimization, since the effectiveness of optimal maintenance decision is largely dependent on the forecasting accuracy of bridge health performance. The current approaches for bridge health prediction can be categorised into two groups: condition ratings based and structural reliability based. A comprehensive literature review has revealed the following limitations of the current modelling approaches: (1) it is not evident in literature to date that any integrated approaches exist for modelling both serviceability and safety aspects so that both performance criteria can be evaluated coherently; (2) complex system modelling approaches have not been successfully applied to bridge deterioration modelling though a bridge is a complex system composed of many inter-related bridge elements; (3) multiple bridge deterioration factors, such as deterioration dependencies among different bridge elements, observed information, maintenance actions and environmental effects have not been considered jointly; (4) the existing approaches are lacking in Bayesian updating ability to incorporate a variety of event information; (5) the assumption of series and/or parallel relationship for bridge level reliability is always held in all structural reliability estimation of bridge systems. To address the deficiencies listed above, this research proposes three novel models based on the Dynamic Object Oriented Bayesian Networks (DOOBNs) approach. Model I aims to address bridge deterioration in serviceability using condition ratings as the health index. The bridge deterioration is represented in a hierarchical relationship, in accordance with the physical structure, so that the contribution of each bridge element to bridge deterioration can be tracked. A discrete-time Markov process is employed to model deterioration of bridge elements over time. In Model II, bridge deterioration in terms of safety is addressed. The structural reliability of bridge systems is estimated from bridge elements to the entire bridge. By means of conditional probability tables (CPTs), not only series-parallel relationship but also complex probabilistic relationship in bridge systems can be effectively modelled. The structural reliability of each bridge element is evaluated from its limit state functions, considering the probability distributions of resistance and applied load. Both Models I and II are designed in three steps: modelling consideration, DOOBN development and parameters estimation. Model III integrates Models I and II to address bridge health performance in both serviceability and safety aspects jointly. The modelling of bridge ratings is modified so that every basic modelling unit denotes one physical bridge element. According to the specific materials used, the integration of condition ratings and structural reliability is implemented through critical failure modes. Three case studies have been conducted to validate the proposed models, respectively. Carefully selected data and knowledge from bridge experts, the National Bridge Inventory (NBI) and existing literature were utilised for model validation. In addition, event information was generated using simulation to demonstrate the Bayesian updating ability of the proposed models. The prediction results of condition ratings and structural reliability were presented and interpreted for basic bridge elements and the whole bridge system. The results obtained from Model II were compared with the ones obtained from traditional structural reliability methods. Overall, the prediction results demonstrate the feasibility of the proposed modelling approach for bridge health prediction and underpin the assertion that the three models can be used separately or integrated and are more effective than the current bridge deterioration modelling approaches. The primary contribution of this work is to enhance the knowledge in the field of bridge health prediction, where more comprehensive health performance in both serviceability and safety aspects are addressed jointly. The proposed models, characterised by probabilistic representation of bridge deterioration in hierarchical ways, demonstrated the effectiveness and pledge of DOOBNs approach to bridge health management. Additionally, the proposed models have significant potential for bridge maintenance optimization. Working together with advanced monitoring and inspection techniques, and a comprehensive bridge inventory, the proposed models can be used by bridge practitioners to achieve increased serviceability and safety as well as maintenance cost effectiveness.

Improving stability of a DFIG-based wind power system with tuned damping controller

This paper focuses on the implementation of a damping controller for the doubly fed induction generator (DFIG) system. Coordinated tuning of the damping controller to enhance the damping of the oscillatory modes is presented using bacterial foraging technique. The effect of the tuned damping controller on converter ratings of the DFIG system is also investigated. The results of both eigenvalue analysis and the time-domain simulation studies are presented to elucidate the effectiveness of the tuned damping controller in the DFIG system. The improvement of the fault ride-through capability of the system is also demonstrated.

Tourist use of mopeds in Queensland

Tourist use of mopeds in Queensland is encouraged by licensing regulations permitting moped riding for car licence holders, who may lack prior knowledge or experience of moped or motorcycle use. Using official crash and registration data, this research examines moped use by tourists, identified as crash-involved riders holding an interstate or overseas licence. Tourists were more likely to be younger, female, in single vehicle crashes, and deemed at fault than Queensland licence holders. Potential safety issues include poor riding skills, inexperience, inattention and lack of protective clothing. Moped rental companies play an important role in managing client crash and injury risks. These risks could also be reduced through introduction of more stringent licensing requirements, though this may be detrimental to moped rental companies as well as to tourist mobility and enjoyment. The discussion considers the relevance of adventure tourism perspectives and theory to the use of mopeds by tourists.

Accuracy of FEM and BEM for electromagnetic flux calculations in high power applications

The development and design of electric high power devices with electromagnetic computer-aided engineering (EM-CAE) software such as the Finite Element Method (FEM) and Boundary Element Method (BEM) has been widely adopted. This paper presents the analysis of a Fault Current Limiter (FCL), which acts as a high-voltage surge protector for power grids. A prototype FCL was built. The magnetic flux in the core and the resulting electromagnetic forces in the winding of the FCL were analyzed using both FEM and BEM. An experiment on the prototype was conducted in a laboratory. The data obtained from the experiment is compared to the numerical solutions to determine the suitability and accuracy of the two methods.

Supporting risk-informed decisions during business process execution

This paper proposes a technique that supports process participants in making risk-informed decisions, with the aim to reduce the process risks. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we prompt the participant with the expected risk that a given fault will occur given the particular input. These risks are predicted by traversing decision trees generated from the logs of past process executions and considering process data, involved resources, task durations and contextual information like task frequencies. The approach has been implemented in the YAWL system and its effectiveness evaluated. The results show that the process instances executed in the tests complete with substantially fewer faults and with lower fault severities, when taking into account the recommendations provided by our technique.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

TS-fuzzy controlled DFIG based wind energy conversion system

This paper focuses on the implementation of the TS (Tagaki-Sugino) fuzzy controller for the active power and the DC capacitor voltage control of the Doubly Fed Induction Generator (DFIG) based wind generator. DFIG system is represented by a third-order model where electromagnetic transients of the stator are neglected. The effectiveness of the TS-fuzzy controller on the rotor speed oscillations and the DC capacitor voltage variations of the DFIG damping controller on converter ratings of the DFIG system is also investigated. The results of the time domain simulation studies are presented to elucidate the effectiveness of the TS-fuzzy controller compared with conventional PI controller in the DFIG system. The proposed TS-fuzzy controller can improve the fault ride through capability of DFIG compared to the conventional PI controller

Early-mid Cretaceous tectonic evolution of eastern Gondwana : from silicic LIP magmatism to continental rupture

The Early–mid Cretaceous marks the confluence of three major continental-scale events in eastern Gondwana: (1) the emplacement of a Silicic Large Igneous Province (LIP) near the continental margin; (2) the volcaniclastic fill, transgression and regression of a major epicontinental seaway developed over at least a quarter of the Australian continent; and (3) epeirogenic uplift, exhumation and continental rupturing culminating in the opening of the Tasman Basin c. 84 Ma. The Whitsunday Silicic LIP event had widespread impact, producing both substantial extrusive volumes of dominantly silicic pyroclastic material and coeval first-cycle volcanogenic sediment that accumulated within many eastern Australian sedimentary basins, and principally in the Great Australian Basin system (>2 Mkm3 combined volume). The final pulse of volcanism and volcanogenic sedimentation at c. 105–95 Ma coincided with epicontinental seaway regression, which shows a lack of correspondence with the global sea-level curve, and alternatively records a wider, continental-scale effect of volcanism and rift tectonism. Widespread igneous underplating related to this LIP event is evident from high paleogeothermal gradients and regional hydrothermal fluid flow detectable in the shallow crust and over a broad region. Enhanced CO2 fluxing through sedimentary basins also records indirectly, large-scale, LIP-related mafic underplating. A discrete episode of rapid crustal cooling and exhumation began c. 100–90 Ma along the length of the eastern Australian margin, related to an enhanced phase of continental rifting that was largely amagmatic, and probably a switch from wide–more narrow rift modes. Along-margin variations in detachment fault architecture produced narrow (SE Australia) and wide continental margins with marginal, submerged continental plateaux (NE Australia). Long-lived NE-trending cross-orogen lineaments controlled the switch from narrow to wide continental margin geometries.