An intelligent maintenance system for continuous cost-based prioritisation of maintenance activities

Abstract

Abstract:A key aspect of competition in industrial maintenance is the trade-off between cost and risk. Decision making is dependent upon up-to-date information about distributed and disparate plant, coupled with knowledge of sensitive non-technical issues. Enabling technologies such as the internet are making strides in improving the quantity and quality of data, particularly by improving links with other information systems. In maintenance, the problem of disparate data sources is important. It is very difficult to make optimal decisions because the information is not easily obtained and merged. Information about technical state or machine health, cost of maintenance activities or loss of production, and non-technical risk factors such as customer information, is required. Even in the best information systems, these are not defined in the same units, and are not presented on a consistent time scale; typically, they are in different information systems. Some data is continuously updated, e.g. condition data, but the critical risk information is typically drawn from a historical survey, fixed in time. A particular problem for the users of condition based maintenance is the treatment of alarms. In principle, only genuine problems are reported, but the technical risk of failure is not the full story. The decision-maker will take into account cost, criticality and other factors, such as limited resources, to prioritise the work. The work reported here automatically prioritises jobs arising from condition based maintenance using a strategy called Cost-Based Criticality (CBC) which draws together three types of information. CBC weights each incident flagged by condition monitoring alarms with up-to-date cost information and risk factors, allowing an optimised prioritisation of maintenance activities. CBC does not attempt to change the strategic plan for maintenance activities: it only addresses prioritisation. The strategy uses a thin-client architecture rather than a central database, and is illustrated with examples from food manufacturing.

A systemic approach to integrated E-maintenance of large engineering plants

“…It can efficiently integrate geographically distributed sources of data, information and knowledge for optimal maintenance decisions and also enable use of technologically advanced sensors, signal processing tools and decision making techniques. E-maintenance has been defined as an asset information management network that integrates and synchronizes the various maintenance and reliability applications together and deliver asset information where it is needed and when it is needed [18] . The concept of e-maintenance has evolved and has developed into a workable information and decision network aimed at efficient and cost effective maintenance management strategy.…”

Managing knowledge within the manufacturing enterprise: an overview

“…Service is an increasingly important element of production, and the line between products and services is becoming less clear. In maintenance service, Moore and Starr propose an intelligent system that brings together equipment condition monitor alarms, cost information and risk factors to prioritise maintenance activities (Moore & Starr 2006). This research demonstrates an intelligent approach to maintenance, integrating a range of knowledge types to support decision making.…”

Identifying rail asset maintenance processes: a human-centric and sensemaking approach

“…Lost Customer Hours (LCH) within a rail service context]. Rail infrastructure appears to be moving from reactive models of asset maintenance to more 'predict-and-prevent' models where intelligent systems are embedded within the complex socio-technical transport network (Al-Douri et al 2016;Zoeteman 2001Zoeteman , 2006Bousdekis et al 2015;Moore and Starr 2006). Such intelligent infrastructure incorporates the use of remote sensors that provide condition-based maintenance approaches whereby the sensors attached to the assets track asset health data continuously (Bousdekis et al 2015;Campos 2009;Garcí et al 2003;Wang et al 2007).…”