Fault diagnosis of low speed bearing based on acoustic emission signal and multi-class relevance vector machine

This study presents an acoustic emission (AE) based fault diagnosis for low speed bearing using multi-class relevance vector machine (RVM). A low speed test rig was developed to simulate the various defects with shaft speeds as low as 10 rpm under several loading conditions. The data was acquired using anAEsensor with the test bearing operating at a constant loading (5 kN) andwith a speed range from20 to 80 rpm. This study is aimed at finding a reliable method/tool for low speed machines fault diagnosis based on AE signal. In the present study, component analysis was performed to extract the bearing feature and to reduce the dimensionality of original data feature. The result shows that multi-class RVM offers a promising approach for fault diagnosis of low speed machines.

A practical signal processing approach for condition monitoring of low speed machinery using Peak-Hold-Down-Sample algorithm

A simple and effective down-sample algorithm, Peak-Hold-Down-Sample (PHDS) algorithm is developed in this paper to enable a rapid and efficient data transfer in remote condition monitoring applications. The algorithm is particularly useful for high frequency Condition Monitoring (CM) techniques, and for low speed machine applications since the combination of the high sampling frequency and low rotating speed will generally lead to large unwieldy data size. The effectiveness of the algorithm was evaluated and tested on four sets of data in the study. One set of the data was extracted from the condition monitoring signal of a practical industry application. Another set of data was acquired from a low speed machine test rig in the laboratory. The other two sets of data were computer simulated bearing defect signals having either a single or multiple bearing defects. The results disclose that the PHDS algorithm can substantially reduce the size of data while preserving the critical bearing defect information for all the data sets used in this work even when a large down-sample ratio was used (i.e., 500 times down-sampled). In contrast, the down-sample process using existing normal down-sample technique in signal processing eliminates the useful and critical information such as bearing defect frequencies in a signal when the same down-sample ratio was employed. Noise and artificial frequency components were also induced by the normal down-sample technique, thus limits its usefulness for machine condition monitoring applications.

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.

QIPC low speed run-overs of young children research project final report

This final report outlines the research conducted by the Centre for Accident Research and Road Safety – Queensland (CARRS-Q) for the research project (title above). This report provides an outline of the project methodology, literature review, three stages of research results (including the focus group discussions, review of organisational records, documentation and initiatives, and analysis of previous CARRS-Q occupational road safety self-report surveys), and recommendations for intervention strategy and initiatives development and implementation.

Developing guidelines for intervention to reduce risk of low-speed vehicle run-overs of young children

In Australia, research suggests that up to one quarter of child pedestrian hospitalisations result from driveway run-over incidents (Pinkney et al., 2006). In Queensland, these numbers equate to an average of four child fatalities and 81 children presenting at hospital emergency departments every year (The Commission for Children, Young People and Child Guardian). National comparison shows that these numbers represent a slightly higher per capita rate (23.5% of all deaths). To address this issue, the current research was undertaken with the aim to develop an educative intervention based on data collected from parents and caregivers of young children. Thus, the current project did not seek to use available intervention or educational material, but to develop a new evidence-based intervention specifically targeting driveway run-overs involving young children. To this end, general behavioural and environmental changes that caregivers had undertaken in order to reduce the risk of injury to any child in their care were investigated. Broadly, the first part of this report sought to: • develop a conceptual model of established domestic safety behaviours, and to investigate whether this model could be successfully applied to the driveway setting; • explore and compare sources of knowledge regarding domestic and driveway child safety; and • examine the theoretical implications of current domestic and driveway related behaviour and knowledge among caregivers. The aim of the second part of this research was to develop and test the efficacy of an intervention based on the findings in the first part of the research project. Specifically, it sought to: • develop an educational driveway intervention that is based on current safety behaviours in the domestic setting and informed by existing knowledge of driveway safety and behaviour change theory; and • evaluate its efficacy in a sample of parents and caregivers.

Characteristics of low-speed vehicle run-over events in children : an 11-year review

Objectives: The purpose of this study was to investigate the characteristics associated with fatal and non-fatal low-speed vehicle run-over (LSVRO) events in relation to person, incident and injury characteristics, in order to identify appropriate points for intervention and injury prevention. Methods: Data on all known LSVRO events in Queensland, Australia, over 11 calendar years (1999–2009) were extracted from five different databases representing the continuum of care ( prehospital to fatality) and manually linked. Descriptive and multivariate analyses were used to analyse the sample characteristics in relation to demographics, health service usage, outcomes, incident characteristics, and injury characteristics. Results: Of the 1641 LSVRO incidents, 98.4% (n=1615) were non-fatal, and 1.6% were fatal (n=26). Over half the children required admission to hospital (56%, n=921); mean length of stay was 3.4 days. Younger children aged 0–4 years were more frequently injured, and experienced more serious injuries with worse outcomes. Patterns of injury (injury type and severity), injury characteristics (eg, time of injury, vehicle type, driver of vehicle, incident location), and demographic characteristics (such as socioeconomic status, indigenous status, remoteness), varied according to age group. Almost half (45.6%; n=737) the events occurred outside major cities, and approximately 10% of events involved indigenous children. Parents were most commonly the vehicle drivers in fatal incidents. While larger vehicles such as four-wheel drives (4WD) were most frequently involved in LSVRO events resulting in fatalities, cars were most frequently involved in non-fatal events. Conclusions: This is the first study, to the authors’ knowledge, to analyse the characteristics of fatal and non-fatal LSVRO events in children aged 0–15 years on a state-wide basis. Characteristics of LSVRO events varied with age, thus age-specific interventions are required. Children living outside major cities, and indigenous children, were over-represented in these data. Further research is required to identify the burden of injury in these groups.