A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

Abstract

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company's strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept "symptom of failure", its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM opensoftware application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.

A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources

“…For a wide comprehension of the ARAM, potential is needed to analyze its implementation cases for three components [55]: monitoring, diagnosis and prognosis. Guillen et al [56] describe these components in terms of failure mode control:…”

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

“…Thus, failure detection, diagnostics and prediction, in networks of assets which co-operate among them to produce a certain purpose, demand an integrated approach, but that distinguish individual asset degradation behaviours. The logic of failure control has to manage not only reliability data but also operation and real-time internal and locational variables [11].…”

“…In order to take rapid and optimal decisions, the challenge is to structure the information from different sources, synchronizing it properly in time, in a sustainable and easily assimilable way, reducing the errors (avoiding dependencies among variables, noise, and interferences) and valuing real risks. A clear conceptual framework allows the permanent development of current and new algorithms, corresponding to distinct data behaviour-anomalies with physical degradation patterns of assets according to their operation and operation environment conditions and their effects on the whole plant [11].…”

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“…Also, maintenance has taken the endeavour to face information and data within the wider scope of AM [14]: meaningful examples may derive from what developed in data management [15], or E-maintenance [16], [17]. In this very field, an interesting framework to guide information and data management has been developed in [18] but enclosed within the scope of maintenance without looking at the AM theory, which is the aim of this work.…”

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