Best basis-based intelligent machine fault diagnosis

The wavelet packet transform decomposes a signal into a set of bases for time–frequency analysis. This decomposition creates an opportunity for implementing distributed data mining where features are extracted from different wavelet packet bases and served as feature vectors for applications. This paper presents a novel approach for integrated machine fault diagnosis based on localised wavelet packet bases of vibration signals. The best basis is firstly determined according to its classification capability. Data mining is then applied to extract features and local decisions are drawn using Bayesian inference. A final conclusion is reached using a weighted average method in data fusion. A case study on rolling element bearing diagnosis shows that this approach can greatly improve the accuracy ofdiagno sis.

A multi-disciplinary approach for the design and management of airport terminals

Multi-disciplinary approaches to complex problems are becoming more common – they enable criteria manifested in distinct (and potentially conflicting) domains to be jointly balanced and satisfied. In this paper we present airport terminals as a case study which requires multi-disciplinary knowledge in order to balance conflicting security, economic and passenger-driven needs and correspondingly enhance the design, management and operation of airport terminals. The need for a truly multi-disciplinary scientific approach which integrates information, process, people, technology and space domains is highlighted through a brief discussion of two challenges currently faced by airport operators. The paper outlines the approach taken by this project, detailing the aims and objectives of each of seven diverse research programs.

Investigating the hydrodynamic performance of a gross pollutant trap

Field studies show that the internal screens in a gross pollutant trap (GPT) are often clogged with organic matter, due to infrequent cleaning. The hydrodynamic performance of a GPT with fully blocked screens was comprehensively investigated under a typical range of onsite operating conditions. Using an acoustic Doppler velocimeter (ADV), velocity profiles across three critical sections of the GPT were measured and integrated to examine the net fluid flow at each section. The data revealed that when the screens are fully blocked, the flow structure within the GPT radically changes. Consequently, the capture/retention performance of the device rapidly deteriorates. Good agreement was achieved between the experimental and the previous 2D computational fluid dynamics (CFD) velocity profiles for the lower GPT inlet flow conditions.

The capture/retention investigation of a gross pollutant trap

A technique was developed to investigate the capture/retention characteristic of a gross pollutant trap (GPT) with fully and partially blocked internal screens. Custom modified spheres of variable density filled with liquid were released into the GPT inlet and monitored at the outlet. The outlet data shows that the capture/retention performances of a GPT with fully blocked screens deteriorate rapidly. During higher flow rates, screen blockages below 68% approach maximum efficiency. At lower flow rates, the high performance trend is reversed and the variation in behaviour of pollutants with different densities becomes more noticeable. Additional experiments with a second upstream inlet configured GPT showed an improved capture/retention performance. It was also noted that the bypass allows the incoming pollutants to escape when the GPT is blocked. This useful feature prevents upstream blockages between cleaning intervals.

A comprehensive investigation into the hydrodynamic and capture/retention performance of a gross pollutant trap

A novel and comprehensive testing approach to examine the performance of gross pollutant traps (GPTs) was developed. A proprietary GPT with internal screens for capturing gross pollutants—organic matter and anthropogenic litter—was used as a case study. This work is the first investigation of its kind and provides valuable practical information for the design, selection and operation of GPTs and also the management of street waste in an urban environment. It used a combination of physical and theoretical models to examine in detail the hydrodynamic and capture/retention characteristics of the GPT. The results showed that the GPT operated efficiently until at least 68% of the screens were blocked, particularly at high flow rates. At lower flow rates, the high capture/retention performance trend was reversed. It was also found that a raised inlet GPT offered a better capture/retention performance. This finding indicates that cleaning operations could be more effectively planned in conjunction with the deterioration in GPT’s capture/retention performance.

Sustainable reuse of storm/waste-water resources : a review on solar photocatalytic purification processes

In recent years, there has been a significant amount of research and development in the area of solar photocatalysis. This paper reviews and summarizes the mechanism of photocatalytic oxidation process, types of photocatalyst, and the factors influencing the photoreactor efficiency and the most recent findings related to solar detoxification and disinfection of water contaminants. Various solar reactors for photocatlytic water purification are also briefly described. The future potential of solar photocatlysis for storm water treatment and reuse is also discussed to ensure sustainable use of solar energy and storm water resources.

Prognostic modelling options for remaining useful life estimation by industry

Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models.

Latent degradation indicators estimation and prediction : a Monte Carlo approach

Asset health inspections can produce two types of indicators: (1) direct indicators (e.g. the thickness of a brake pad, and the crack depth on a gear) which directly relate to a failure mechanism; and (2) indirect indicators (e.g. the indicators extracted from vibration signals and oil analysis data) which can only partially reveal a failure mechanism. While direct indicators enable more precise references to asset health condition, they are often more difficult to obtain than indirect indicators. The state space model provides an efficient approach to estimating direct indicators by using indirect indicators. However, existing state space models to estimate direct indicators largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires fixed inspection intervals. The discrete state assumption entails discretising continuous degradation indicators, which often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This paper proposes a state space model without these assumptions. Monte Carlo-based algorithms are developed to estimate the model parameters and the remaining useful life. These algorithms are evaluated for performance using numerical simulations through MATLAB. The result shows that both the parameters and the remaining useful life are estimated accurately. Finally, the new state space model is used to process vibration and crack depth data from an accelerated test of a gearbox. During this application, the new state space model shows a better fitness result than the state space model with linear and Gaussian assumption.

Association between indoor air pollution measurements and respiratory health in women and children in Lao PDR

This article presents the results of a study on the association between measured air pollutants and the respiratory health of resident women and children in Lao PDR, one of the least developed countries in Southeast Asia. The study, commissioned by the World Health Organisation, included PM10, CO and NO2 measurements made inside 181 dwellings in nine districts within two provinces in Lao PDR over a 5- month period (12/05–04/06), and respiratory health information (via questionnaires and peak expiratory flow rate (PEFR) measurements) for all residents in the same dwellings. Adjusted odds ratios were calculated separately for each health outcome using binary logistic regression. There was a strong and consistent positive association between NO2 and CO for almost all questionnaire-based health outcomes for both women and children. Women in dwellings with higher measured NO2 had more than triple of the odds of almost all of the health outcomes, and higher concentrations of NO2 and CO were significantly associated with lower PEFR. This study supports a growing literature confirming the role of indoor air pollution in the burden of respiratory disease in developing countries. The results will directly support changes in health and housing policy in Lao PDR.

Bond–slip models for double strap joints strengthened by CFRP

This paper presents the results of a series of tension tests on CFRP bonded steel plate double strap joints. The main aim of this research is to provide detailed understanding of bond characteristics using experimental and numerical analysis of strengthened double strap joints under tension. A parametric study has been performed by numerical modelling with the variables of CFRP bond lengths, adhesive maximum strain and adhesive layer thicknesses. Finally, bond-slip models are proposed for three different types of adhesives within the range of the parametric study.