If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.Abstract Competitiveness has forced companies to improve the overall performance of the business. In the area of maintenance, much has been written about strategies, such as total productive maintenance or reliability centred maintenance, in order to increase the reliability and therefore capacity of the industrial plants in their quest for world-class maintenance. However, if a strategy is to be effective, it must be supported with an invaluable resource, information. In the present work, the role of computerised maintenance management systems (CMMSs) is discussed as a powerful tool necessary for obtaining information from raw data and support the decision-making process. Furthermore, a CMMS has been designed, developed, customised and implemented for a disc brake pad manufacturing company based in England. In addition, a maintenance maturity grid has been proposed to support the CMMS implementation. The grid shows that the complexity of the CMMS will increase as the maintenance function moves from a reactive to a proactive culture. The implemented CMMS aims to reduce total downtime and frequency of failures of the machines by improving the efficiency and effectiveness of the maintenance force. The computer program simplifies and reduces the time of data capture compared to the currently used paper-based reporting system. It also provides the maintenance planners with a platform for decision analysis and support often ignored in the commercial CMMSs available in the market.

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Abstract. Radial Basis Neural Networks (RBNN) can approximate any regular function and have a faster training phase than other similar neural networks. However, the activation of each neuron depends on the euclidean distance between a pattern and the neuron center. Therefore, the activation function is symmetrical and all attributes are considered equally relevant. This could be solved by altering the metric used in the activation function (i.e. using non-symmetrical metrics). The Mahalanobis distance is such a metric, that takes into account the variability of the attributes and their correlations. However, this distance is computed directly from the variance-covariance matrix and does not consider the accuracy of the learning algorithm. In this paper, we propose to use a generalized euclidean metric, following the Mahalanobis structure, but evolved by a Genetic Algorithm (GA). This GA searches for the distance matrix that minimizes the error produced by a fixed RBNN. Our approach has been tested on two domains and positive results have been observed in both cases.

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