Emanuele Dovere

RFId systems for moveable asset management: an assessment model

Management of moveable assets is a key issue in most industrial manufacturing companies. With the increasing complexity of production systems characterised by numerous and heterogeneous machining centres, it becomes quite hard to carry out an effective and cost-efficient management model of the tools and fixtures needed for ensuring a correct and timely execution of the planned production cycles. A prerequisite for a proper management of moveable assets is the adoption of identification systems to support the traceability and data collection of the most relevant pieces of information along the life cycle of an asset. The aim of this paper was to provide an assessment model for evaluating the benefits and costs related to the adoption of RFId tags as identifications systems for moveable assets, and in particular in managing machinery tools in a production premise. In order to present a practical example of applicability of the model, an industrial application is reported with an in-depth analysis of the potential benefits and issues derived from the implementation of RFId tags.

Agile diagnostic tool based on electrical signature analysis

Abstract Today, time based maintenance is the most common strategy, to assure a certain level of productivity and reliability in the industrial plants, with a fixed interval length between two maintenance actions. However, given the stochastic nature of the faults, plant equipments and machines usually fail unexpectedly, before the scheduled maintenance action. Therefore, condition based maintenance (CBM) has been introduced, without any fixed interval length between the maintenance actions, as a more efficient maintenance policy. A technique developed for condition based maintenance of electro-mechanical systems is electrical signature analysis. The basic idea behind electrical signature analysis is that, time-dependent load and speed variations in an electro-mechanical system generally induce correlated small variations in the systems electrical response. Electrical signature analysis analyzes these small variations providing diagnostic information. A tool based on this technique is agile in its adoption since no specific sensors must be installed on the machine, but only power signal must be measured and thus the implementation of such tool can be considered simple and flexible. This paper presents a tool that has such kind of characteristics. The tool processes power signals acquired by transducers installed on the electrical lines of electro-mechanical systems, for machine fault diagnosis. The data acquired are processed through some steps, using some signal indicators developed to understand the machine behaviour.