Condition monitoring and diagnosis of injector faults in a diesel engine using in-cylinder pressure and acoustic emission techniques

Failing injectors are one of the most common faults in diesel engines. The severity of these faults could have serious effects on diesel engine operations such as engine misfire, knocking, insufficient power output or even cause a complete engine breakdown. It is thus essential to prevent such faults from occurring by monitoring the condition of these injectors. In this paper, the authors present the results of an experimental investigation on identifying the signal characteristics of a simulated incipient injector fault in a diesel engine using both in-cylinder pressure and acoustic emission (AE) techniques. A time waveform event driven synchronous averaging technique was used to minimize or eliminate the effect of engine speed variation and amplitude fluctuation. It was found that AE is an effective method to detect the simulated injector fault in both time (crank angle) and frequency (order) domains. It was also shown that the time domain in-cylinder pressure signal is a poor indicator for condition monitoring and diagnosis of the simulated injector fault due to the small effect of the simulated fault on the engine combustion process. Nevertheless, good correlations between the simulated injector fault and the lower order components of the enveloped in-cylinder pressure spectrum were found at various engine loading conditions.

Economic fluctuations and productivity in the Malaysian construction sector

Growth in productivity is the key determinant of the long-term health and prosperity of an economy. The construction industry being one of major strategic importance, its productivity performance has a significant effect on national economic growth. The relationship between construction output and economy has received intensive studies, but there is lack of empirical study on the relationship between construction productivity and economic fluctuations. Fluctuations in construction output are endemic in the industry. In part they are caused by the boom and slump of the economy as a whole and in part by the nature of the construction product. This research aims to uncover how the productivity of construction sector is influenced in the course of economic fluctuations in Malaysia. Malaysia has adopted three economic policies – New Economic Policy (1971-1990), National Development Policy (1991-2000) and the National Vision Policy (2001-2010) since gaining independence in 1959. The Privatisation Master Plan was introduced in 1991. Operating within this historical context, the Malaysian construction sector has experienced four business cycles since 1960. A mixed-method design approach is adopted in this study. Quantitative analysis was conducted on the published official statistics of the construction industry and the overall economy in Malaysia between 1970 and 2009. Qualitative study involved interviews with a purposive sample of 21 industrial participants. This study identified a 32-year long building cycle appears in 1975-2006. It is superimposed with three shorter construction business cycles in 1975-1987, 1987-1999 and 1999-2006. The correlations of Construction labour productivity (CLP) and GDP per capita are statistically significant for the 1975-2006 building cycle, 1987-1999 and 1999-2006 construction business cycles. It was not significant in 1975-1987 construction business cycles. The Construction Industry Surveys/Census over the period from 1996 to 2007 show that the average growth rate of total output per employee expanded but the added value per employee contracted which imply high cost of bought-in materials and services and inefficient usage of purchases. The construction labour productivity is peaked at 2004 although there is contraction of construction sector in 2004. The residential subsector performed relatively better than the other sub-sectors in most of the productivity indicators. Improvements are found in output per employee, value added per employee, labour competitiveness and capital investment but declines are recorded in value added content and capital productivity. The civil engineering construction is most productive in the labour productivity nevertheless relatively poorer in the capital productivity. The labour cost is more competitive in the larger size establishment. The added value per labour cost is higher in larger sized establishment attributed to efficient in utilization of capital. The interview with the industrial participant reveals that the productivity of the construction sector is influenced by the economic environment, the construction methods, contract arrangement, payment chain and regulatory policies. The fluctuations of construction demand have caused companies switched to defensive strategy during the economic downturn and to ensure short-term survival than to make a profit for the long-term survival and growth. It leads the company to take drastic measures to curb expenses, downsizing, employ contract employment, diversification and venture overseas market. There is no empirical evidence supports downsizing as a necessary step in a process of reviving productivity. The productivity does not correlate with size of firm. A relatively smaller and focused firm is more productive than the larger and diversified organisation. However diversified company experienced less fluctuation in both labour and capital productivity. In order to improve the productivity of the construction sector, it is necessary to remove the negatives and flaws from past practices. The recommended measures include long-term strategic planning and coordinated approaches of government agencies in planning of infrastructure development and to provide regulatory environments which encourage competition and facilitate productivity improvement.

Turbulence and suspended sediment measurements in an urban environment during the Brisbane River flood of January 2011

In urbanised areas, the flood flows constitute a hazard to populations and infrastructure as illustrated during major floods in 2011. During the 2011 Brisbane River flood, some turbulent velocity data were collected using acoustic Doppler velocimetry in an inundated street. The field deployment showed some unusual features of flood flow in the urban environment. That is, the water elevations and velocities fluctuated with distinctive periods between 50 and 100 s linked with some local topographic effects. The instantaneous velocity data were analysed using a triple decomposition. The velocity fluctuations included a large energy component in the slow fluctuation range, while the turbulent motion components were much smaller. The suspended sediment data showed some significant longitudinal flux. Altogether the results highlighted that the triple decomposition approach originally developed for period flows is well suited to complicated flows in an inundated urban environment.

Transmission system expansion planning considering combined operation of wind farms and energy storage systems

An energy storage system (ESS) can provide ancillary services such as frequency regulation and reserves, as well as smooth the fluctuations of wind power outputs, and hence improve the security and economics of the power system concerned. The combined operation of a wind farm and an ESS has become a widely accepted operating mode. Hence, it appears necessary to consider this operating mode in transmission system expansion planning, and this is an issue to be systematically addressed in this work. Firstly, the relationship between the cost of the NaS based ESS and its discharging cycle life is analyzed. A strategy for the combined operation of a wind farm and an ESS is next presented, so as to have a good compromise between the operating cost of the ESS and the smoothing effect of the fluctuation of wind power outputs. Then, a transmission system expansion planning model is developed with the sum of the transmission investment costs, the investment and operating costs of ESSs and the punishment cost of lost wind energy as the objective function to be minimized. An improved particle swarm optimization algorithm is employed to solve the developed planning model. Finally, the essential features of the developed model and adopted algorithm are demonstrated by 18-bus and 46-bus test systems.

Towards proximity-based passenger sensing on public transport buses

While substantial research on intelligent transportation systems has focused on the development of novel wireless communication technologies and protocols, relatively little work has sought to fully exploit proximity-based wireless technologies that passengers actually carry with them today. This paper presents the real-world deployment of a system that exploits public transit bus passengers’ Bluetooth-capable devices to capture and reconstruct micro- and macro-passenger behavior. We present supporting evidence that approximately 12% of passengers already carry Bluetooth-enabled devices and that the data collected on these passengers captures with almost 80 % accuracy the daily fluctuation of actual passengers flows. The paper makes three contributions in terms of understanding passenger behavior: We verify that the length of passenger trips is exponentially bounded, the frequency of passenger trips follows a power law distribution, and the microstructure of the network of passenger movements is polycentric.

Intelligent reliability analysis with incomplete covariates

The reliability analysis is crucial to reducing unexpected down time, severe failures and ever tightened maintenance budget of engineering assets. Hazard based reliability methods are of particular interest as hazard reflects the current health status of engineering assets and their imminent failure risks. Most existing hazard models were constructed using the statistical methods. However, these methods were established largely based on two assumptions: one is the assumption of baseline failure distributions being accurate to the population concerned and the other is the assumption of effects of covariates on hazards. These two assumptions may be difficult to achieve and therefore compromise the effectiveness of hazard models in the application. To address this issue, a non-linear hazard modelling approach is developed in this research using neural networks (NNs), resulting in neural network hazard models (NNHMs), to deal with limitations due to the two assumptions for statistical models. With the success of failure prevention effort, less failure history becomes available for reliability analysis. Involving condition data or covariates is a natural solution to this challenge. A critical issue for involving covariates in reliability analysis is that complete and consistent covariate data are often unavailable in reality due to inconsistent measuring frequencies of multiple covariates, sensor failure, and sparse intrusive measurements. This problem has not been studied adequately in current reliability applications. This research thus investigates such incomplete covariates problem in reliability analysis. Typical approaches to handling incomplete covariates have been studied to investigate their performance and effects on the reliability analysis results. Since these existing approaches could underestimate the variance in regressions and introduce extra uncertainties to reliability analysis, the developed NNHMs are extended to include handling incomplete covariates as an integral part. The extended versions of NNHMs have been validated using simulated bearing data and real data from a liquefied natural gas pump. The results demonstrate the new approach outperforms the typical incomplete covariates handling approaches. Another problem in reliability analysis is that future covariates of engineering assets are generally unavailable. In existing practices for multi-step reliability analysis, historical covariates were used to estimate the future covariates. Covariates of engineering assets, however, are often subject to substantial fluctuation due to the influence of both engineering degradation and changes in environmental settings. The commonly used covariate extrapolation methods thus would not be suitable because of the error accumulation and uncertainty propagation. To overcome this difficulty, instead of directly extrapolating covariate values, projection of covariate states is conducted in this research. The estimated covariate states and unknown covariate values in future running steps of assets constitute an incomplete covariate set which is then analysed by the extended NNHMs. A new assessment function is also proposed to evaluate risks of underestimated and overestimated reliability analysis results. A case study using field data from a paper and pulp mill has been conducted and it demonstrates that this new multi-step reliability analysis procedure is able to generate more accurate analysis results.

Green skills for the construction industry

The skill shortage issues have long existed in the construction industry in countries like Australia. Couple this with the lead and lag time between market demand and resultant supply has traditionally seen cyclical fluctuation of skills demand within the construction industry. Skills demand and shortages are generally well documented and can even have a level of predictability in Australia given the tendency to have a delayed reaction to global economic downturns. Sustainability issues in the construction industry have attracted growing public awareness. Traditionally driven by ever increasing, if only gradual, mandated minimum requirements, drive towards sustainable developments is now increasingly being created by the client. As this demand increases, accordingly a demand for people with the skills to provide these services should be felt. This research examines the green skill shortage issues within the context of construction industry. Stakeholders from across relevant sectors of the built environment were engaged to ascertain the industry’s utilisation and demand for ‘green skilled’ personnel. These findings will assist stakeholders within the construction industry in negating the effects of a skills shortage in the event of accelerated demand for sustainable construction.

Determination of battery storage capacity in energy buffer for wind farm

Design of a battery energy storage system (BESS) in a buffer scheme is examined for the purpose of attenuating the effects of unsteady input power from wind farms. The design problem is formulated as maximization of an objective function that measures the economic benefit obtainable from the dispatched power from the wind farm against the cost of the BESS. Solution to the problem results in the determination of the capacity of the BESS to ensure constant dispatched power to the connected grid, while the voltage level across the dc-link of the buffer is kept within preset limits. A computational procedure to determine the BESS capacity and the evaluation of the dc voltage is shown. Illustrative examples using the proposed design method are included.

Seasonal amplitude of hemorrhagic fever with renal syndrome in China : a call for attention to neglected regions

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne viral disease characterized by fever, hemorrhagic, kidney damage and hypotension, is caused by different species of hantaviruses [1]. Every year, HFRS affects thousands of people in Asia, and more than 90% of these cases are reported in China [2, 3]. Due to its high fatality, HFRS has attracted considerable research attention, and prior studies have predominantly focused on quantifying HFRS morbidity [4], identifying high risk areas [5] and populations [6], or exploring peak time of HFRS occurrence [3]. To date, no study has assessed the seasonal amplitude of HFRS in China, even though it reveals the seasonal fluctuation and thus may provide pivotal information on the possibility of HFRS outbreaks.

A statistical approach to quantify the impact on voltage quality caused by PV generators

A probabilistic method is proposed to evaluate voltage quality of grid-connected photovoltaic (PV) power systems. The random behavior of solar irradiation is described in statistical terms and the resulting voltage fluctuation probability distribution is then derived. Reactive power capabilities of the PV generators are then analyzed and their operation under constant power factor mode is examined. By utilizing the reactive power capability of the PV-generators to the full, it is shown that network voltage quality can be greatly enhanced.

Economic development and construction productivity in Malaysia

The productivity of the construction industry has a significant effect on national economic growth. Gains from higher construction productivity flow through the economy, as all industries rely on construction to some extent as part of their business investment. Contractions and expansions of economic activity are common phenomena in an economy. Three construction cycles occurred between the years 1970 and 2011 in Malaysia. The relationships between construction productivity and economic development are examined by the partial correlation method to establish the underlying factors driving the change in construction productivity. Construction productivity is statistically significantly correlated with gross domestic product (GDP) per capita in a positive direction for the 1985–98 and 1998–2009 cycles, but not the 1970–85 cycle. Fluctuations in construction activities and the influx of foreign workers underlie the changes of construction productivity in the 1985–98 cycle. There was less fluctuation in construction activities in the 1998–2009 cycle, with changes being mainly due to the fiscal stimulation policies of the government in attempting to stabilize the economy. The intensive construction of mega-projects resulted in resource constraints and cost pressures during the 1980s and 1990s. A better management of the ‘boom-bust’ nature of the construction business cycle is required to maintain the capability and capacity of the industry.

Gold nanoparticles modified electrodes for biosensors

Biomolecules are chemical compounds found in living organisms which are the building blocks of life and perform important functions. Fluctuation from the normal concentration of these biomolecules in living system leads to several disorders. Thus the exact determination of them in human fluids is essential in the clinical point of view. High performance liquid chromatography, flow injection analysis, capillary electrophoresis, fluorimetry, spectrophotometry, electrochemical and chemiluminescence techniques were usually used for the determination of biologically important molecules. Among these techniques, electrochemical determination of biomolecules has several advantages over other methods viz., simplicity, selectivity and sensitivity. In the past two decades, electrodes modified with polymer films, self-assembled monolayers containing different functional groups and carbon paste have been used as electrochemical sensors. But in recent years, nanomaterials based electrochemical sensors play an important role in the improvement of public health because of its rapid detection, high sensitivity and specificity in clinical diagnostics. To date gold nanoparticles (AuNPs) have received arousing attention mainly due to their fascinating electronic and optical properties as a consequence of their reduced dimensions. These unique properties of AuNPs make them as an ideal candidate for the immobilization of enzymes for biosensing. Further, the electrochemical properties of AuNPs reveal that they exhibit interesting properties by enhancing the electrode conductivity, facilitating electron transfer and improving the detection limit of biomolecules. In this chapter, we summarized the different strategies used for the attachment of AuNPs on electrode surfaces and highlighted the electrochemical determination of glucose, ascorbic acid (AA), uric acid (UA) and dopamine derivatives using the AuNPs modified electrodes.

Probabilistic power system contingency analysis considering wind

This thesis was a step forward in developing probabilistic assessment of power system response to faults subject to intermittent generation by renewable energy. It has investigated the wind power fluctuation effect on power system stability, and the developed fast estimation process has demonstrated the feasibility for real-time implementation. A better balance between power network security and efficiency can be achieved based on this research outcome.

Turbulence measurements in a shallow tidal estuary : analysis based on triple decomposition

In an estuary, mixing and dispersion are the result of the combination of large scale advection and small scale turbulence which are both complex to estimate. A field study was conducted in a small sub-tropical estuary in which high frequency (50 Hz) turbulent data were recorded continuously for about 48 hours. A triple decomposition technique was introduced to isolate the contributions of tides, resonance and turbulence in the flow field. A striking feature of the data set was the slow fluctuations which exhibited large amplitudes up to 50% the tidal amplitude under neap tide conditions. The triple decomposition technique allowed a characterisation of broader temporal scales of high frequency fluctuation data sampled during a number of full tidal cycles.

Understanding the effects of complex seasonality on suburban daily transit ridership

Fluctuations in transit ridership pattern over the year have always concerned transport planners, operators and researchers. Predominantly, metrological elements have been specified to explain variability in ridership volume. However, the outcome of this research points to new direction to explain ridership fluctuation in Brisbane. It explored the relationship between daily bus ridership, seasonality and weather variables for a one-year period, 2012. Rather than segregating the entire year’s ridership into the four calendar seasons (summer, autumn, spring, and winter), this analysis distributed the yearly ridership into nine complex seasonality blocks. These represent calendar season, school/university (academic) period and their corresponding holidays, as well as other observant holidays such as Christmas. The dominance of complex seasonality over typical calendar season was established through analysis and using Multiple Linear Regression (MLR). This research identified a very strong association between complex seasonality and bus ridership. Furthermore, an expectation that Brisbane’s subtropical summer is unfavourable to transit usage was not supported by the findings of this study. A nil association of precipitation and temperature was observed in this region. Finally, this research developed a ridership estimation model, capable of predicting daily ridership within very limited error range. Following the application of this developed model, the estimated annual time series data of each suburb was analysed using Fourier Transformation to appreciate whether any cyclical effects remained, compared with the original data.