Application of an adaptive neural network with symbolic rule extraction to fault detection and diagnosis in a power generation plant

Artificial neural networks have a good potential to be employed for fault diagnosis and condition monitoring problems in complex processes. In this paper, the applicability of the fuzzy ARTMAP (FAM) neural network as an intelligent learning system for fault detection and diagnosis in a power generation plant is described. The process under scrutiny is the circulating water (CW) system, with specific attention to the conditions of heat transfer and tube blockage in the CW system. A series of experiments has been conducted systematically to investigate the effectiveness of FAM in fault detection and diagnosis tasks. In addition, a set of domain rules has been extracted from the trained FAM network so that its predictions can be explained and justified. The outcomes demonstrate the benefits of employing FAM as an intelligent fault detection and diagnosis tool with an explanatory capability for monitoring and diagnosing complex processes in power generation plants.

Condition monitoring and fault prediction via an adaptive neural network

This paper describes the application of an adaptive neural network, called Fuzzy ARTMAP (FAM), to handle fault prediction and condition monitoring problems in a power generation station. The FAM network, which is supplemented with a pruning algorithm, is used as a classifier to predict different machine conditions, in an off-line learning mode. The process under scrutiny in the power plant is the Circulating Water (CW) system, with prime attention to monitoring the heat transfer efficiency of the condensers. Several phases of experiments were conducted to investigate the `optimum' setting of a set of parameters of the FAM classifier for monitoring heat transfer conditions in the power plant.

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.

Retail brand orientation, positional advantage and organisational performance

This study adopts a resource-based view of branding referred to as brand orientation. Despite the importance of branding in retail, relatively little empirical research has been conducted to understand the degree to which retailers can be considered brand oriented. The purpose of the present research is to establish a conceptualisation of brand orientation that is applicable in a retail context across countries. Moreover, we seek to empirically validate a model of the retail brand orientation–positional advantage–organisational performance relationship and to contribute to a more comprehensive understanding of the factors driving retailer performance. A mail survey was used to collect data from retail firms in Australia, USA and UK. The unit of analysis is the retail firm. Confirmatory factor analysis was employed to assess the measurement properties of the study constructs and structural equation modelling was performed to test the research model. The findings suggest that four elements of retail brand orientation (functionality, distinctiveness, augmentation and symbolism) play different roles in relation to certain aspects of positional advantage, which highlights the importance of developing strength in all four areas. Similarly, a position of superiority in only one aspect of a retailer's offer is insufficient to assure both financial and strategic returns.

Biomarkers in bipolar disorder : a positional paper from the International Society for Bipolar Disorders Biomarkers Task Force

Although the etiology of bipolar disorder remains uncertain, multiple studies examining neuroimaging, peripheral markers and genetics have provided important insights into the pathophysiologic processes underlying bipolar disorder. Neuroimaging studies have consistently demonstrated loss of gray matter, as well as altered activation of subcortical, anterior temporal and ventral prefrontal regions in response to emotional stimuli in bipolar disorder. Genetics studies have identified several potential candidate genes associated with increased risk for developing bipolar disorder that involve circadian rhythm, neuronal development and calcium metabolism. Notably, several groups have found decreased levels of neurotrophic factors and increased pro-inflammatory cytokines and oxidative stress markers. Together these findings provide the background for the identification of potential biomarkers for vulnerability, disease expression and to help understand the course of illness and treatment response. In other areas of medicine, validated biomarkers now inform clinical decision-making. Although the findings reviewed herein hold promise, further research involving large collaborative studies is needed to validate these potential biomarkers prior to employing them for clinical purposes. Therefore, in this positional paper from the ISBD-BIONET (biomarkers network from the International Society for Bipolar Disorders), we will discuss our view of biomarkers for these three areas: neuroimaging, peripheral measurements and genetics; and conclude the paper with our position for the next steps in the search for biomarkers for bipolar disorder.