Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Regulation of motor vehicle advertising: toward a framework for compliance research

There is concern that certain content within some motor vehicle television advertising may negatively influence the driving attitudes and behaviours of viewers, particularly young people, and hence have a negative impact on road safety. In recognition of this concern, many developed countries have adopted a self-regulatory approach to motor vehicle advertising. The basic elements of self-regulation are a code of practice or guiding principles governing advertising content and the establishment of a process for hearing and adjudicating complaints about alleged breaches of that code. However, as in other areas, the effectiveness of self-regulation is being questioned in that many motor vehicle advertisements in Australia and elsewhere appear non-compliant with self-regulatory codes. Applying lessons from studies of alcohol advertising, this paper first reviews the research assessing the content of motor vehicle advertising. A suggested research framework is then proposed to inform the development of motor vehicle advertising regulatory codes where they do not exist, and to better monitor compliance with codes where they do exist. The research framework suggested includes expert content analysis of ads, the impact of advertising on risk-taking cognitions and decisions in computer-simulated traffic situations, and assessing audience perceptions of, and reactions to, messages in advertisements mapped against regulatory code content. An example of audience reaction research is also presented.

Voltage oscillations in power distribution networks in the presence of DFIGs and induction motor loads

This paper investigates the oscillatory behavior of power distribution systems in the presence of distributed generation. The analysis is carried out over a distribution test system with two doubly fed induction type wind generators and different types of induction motor loads. The system is linearized by the perturbation method. Eigenvalues are calculated to see the modal interaction within the system. The study indicates that interactions between closely placed converter controllers and induction motor loads significantly influence the damping of the oscillatory modes of the system. The critical modes have a frequency of oscillation between the electromechanical and subsynchronous oscillations of power systems. Time-domain simulations are carried out to verify the validity of the modal analysis and to provide a physical feel for the types of oscillations that occur in distribution systems. Finally, significant parameters of the system that affect the damping and frequency of the oscillation are identified.