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.

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.

Enabling organizational cultural change using systemic strategic human resource management – a longitudinal case study

Researchers report that successful cultural change in an organization is difficult to achieve. This research contends that it is more likely to be successful when a systemic approach to strategic human resource management (SHRM) is used to facilitate the change. The contention was tested in an action research case study and longitudinal assessment of change in a large Australian public sector agency. A clear finding from this research is that the cultural change had been sustained through the systemic application of SHRM.

Internal organizational factors influencing sustainable implementation of information systems : experiences from a local government in Indonesia

Many information systems (IS) studies have found that information systems implementation sustainability is determined by internal organizational factors. In general these studies have been conducted in private organizations and these factors may not be applicable to IS implementations sustainability within public organizations. This study examines what internal organizational factors play a role in the sustainable implementation of e-government initiatives using a case study of local e-government in Indonesia. It also considers how these factors contribute to sustainable systems by strengthening stakeholders’ commitment through invoking feelings of involvement as responsibilities are assigned to them. The study concludes that the internal factors contribute to collective action that influences sustainable implementation of information systems. Limitations and future research are briefly discussed.