Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Rule learning and extraction using a hybrid neural network : a case study on fault detection and diagnosis

A hybrid network, based on the integration of Fuzzy ARTMAP (FAM) and the Rectangular Basis Function Network (RecBFN), is proposed for rule learning and extraction problems. The underlying idea for such integration is that FAM operates as a classifier to cluster data samples based on similarity, while the RecBFN acts as a “compressor” to extract and refine knowledge learned by the trained FAM network. The hybrid network is capable of classifying data samples incrementally as well as of acquiring rules directly from data samples for explaining its predictions. To evaluate the effectiveness of the hybrid network, it is applied to a fault detection and diagnosis task by using a set of real sensor data collected from a Circulating Water (CW) system in a power generation plant. The rules extracted from the network are analyzed and discussed, and are found to be in agreement with experts’ opinions used in maintaining the CW system.

Application of the fuzzy min-max neural network to fault detection and diagnosis of induction motors

In this paper, an application of the motor current signature analysis (MCSA) method and the fuzzy min–max (FMM) neural network to detection and classification of induction motor faults is described. The finite element method is employed to generate simulated data pertaining to changes in the stator current signatures under different motor conditions. The MCSA method is then used to process the stator current signatures. Specifically, the power spectral density is employed to extract harmonics features for fault detection and classification with the FMM network. Various types of induction motor faults, which include stator winding faults and eccentricity problems, under different load conditions are experimented. The results are analyzed and compared with those from other methods. The outcomes indicate that the proposed technique is effective for fault detection and diagnosis of induction motors under different conditions.

Robust fault-tolerant leader-follower control of four-wheel-steering mobile robots using terminal sliding mode

This paper addresses the actuator failure compensation problem of non-linear fourwheel-steering mobile robots based on vehicle kinematics, undergoing both known and unknown failures causing degenerated steering performance or wheels stuck at some observable angles. Terminal sliding mode control technique is used to guarantee the tracking stability infinite time with the presence of actuator fault. Simulation results are given to illustrate the effectiveness of the proposed control scheme. © Institution of Engineers Australia 2012.

A journey through places: politics of spatial location in the stories of South Asian diaspora in Australia

In this paper, I analyse the post-colonial and post-modern experiences in the light of questions and issues concerning the spatial identities or locations that the diasporans carry. By politics of spatial location, I mean a migrant's positions within power hierarchies created through geographic, historical, political, economic and other socially stratifying factors in the new homeland. This paper is mainly concerned with the theoretical ways in which a shift is accelerated in Australia's literary landscape by the South Asian diasporic writers who produce and cover the dynamics of politics of location in different contexts. It also focuses on South Asian diaspora's widely agreed ‘ability to recreate their cultures in diverse locations’. I conclude that these stories are not just of spaces but also of a promising future for the South Asian diaspora in Australia.

Feasibility study to objectively assess activity and location of Hispanic preschoolers: a short communication

Abstract. Both physical and social environmental factors influence young children’s physical activity, yet little is known about where Hispanic children are more likely to be active. We assessed the feasibility of simultaneously measuring, then processing objective measures of location and physical activity among Hispanic preschool children. Preschool-aged Hispanic children (n = 15) simultaneously wore QStarz BT100X global positioning system (GPS) data loggers and Actigraph GT3X accelerometers for a 24- to 36-hour period, during which time their parents completed a location and travel diary. Data were aggregated to the minute and processed using the personal activity location measurement system (PALMS). Children successfully wore the GPS data loggers and accelerometers simultaneously, 12 of which yielded data that met quality standards. The average percent correspondence between GPS- and diary-based estimates of types of location was high and Kappa statistics were moderate to excellent, ranging from 0.49-0.99. The between method (GPS monitor, parent-reported diary) correlations of estimated participant-aggregated minutes spent on vehicle-based trips were strong. The simultaneous use of GPS and accelerometers to assess Hispanic preschool children’s location and physical activity is feasible. This methodology has the potential to provide more precise findings to inform environmental interventions and policy changes to promote physical activity among Hispanic preschool children.