NEURAL NETWORK AS AN EFFICIENT DIAGNOSTICS TOOL: A CASE STUDY IN A TEXTILE COMPANY

Abstract

Abstract Due to the increasing pressure on efficiency gains and quick response to customers’ requests, manufacturing industries are striving to reduce and eliminate costly, unscheduled downtimes and breakdowns. To this end, Condition-Based Maintenance (CBM) approach aims at reducing the uncertainty of maintenance according to the needs indicated by the equipment condition. However, one of the main problems of the CBM approach is to identify and measure the conditions of the system under control, in order to define the need for a preventive maintenance intervention. This paper describes a diagnostics system based on the Artificial Neural Network (ANN) technique, applied to a textile finishing machinery. This policy, very cost-effective since it uses a signal often available without the embedment of a sensor, aims at avoiding the need for costly, direct measurement of system conditions. The obtained results lead to the conclusion that neural networks represent an effective tool in supporting CBM approaches.