Andrew Starr

A Review of data fusion models and architectures: towards engineering guidelines

This paper reviews the potential benefits that can be obtained by the implementation of data fusion in a multi-sensor environment. A thorough review of the commonly used data fusion frameworks is presented together with important factors that need to be considered during the development of an effective data fusion problem-solving strategy. A system-based approach is defined for the application of data fusion systems within engineering. Structured guidelines for users are proposed.

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

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 nontechnical 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.

Operational fault diagnosis of manufacturing systems

Abstract Among all kinds of possible faults in a manufacturing system, operational faults occur most often (about 70%). Efficient diagnosis of these faults is critical for improving the availability and productivity of the manufacturing system. This paper presents a hierarchical diagnosis model based on fault tree analysis (FTA) and two other diagnosis models, respectively, based on the logic and sequential control of manufacturing systems which are usually controlled by a programmable logical controller (PLC). With these models working together, the operational faults of a manufacturing system can be diagnosed completely. The models have been successfully applied to a PLC-controlled flexible manufacturing system (FMS) and have achieved good results.

A systematic approach to integrated fault diagnosis of flexible manufacturing systems

A flexible manufacturing system (FMS) is an application of modern manufacturing techniques. Like for other manufacturing equipment, the success of an FMS is very much dependent upon its trouble-free operation. It is crucial to monitor all the possible faults or abnormalities in real time and, when a fault is detected, react quickly in order to maintain the productivity of the FMS. Because of the complexity of FMSs, the functionally complete diagnosis of an FMS should be based on all the available information and various advanced diagnostic techniques so as to get a satisfactory result. This paper proposes a systematic approach to fault diagnosis of FMSs that integrates condition monitoring, fault diagnosis and maintenance planning. An intelligent integrated fault-diagnosis system is designed with a modular and reconfigurable structure. The implementation of the integrated diagnosis system is presented in detail. The system can monitor the major conditions and diagnose the major faults of an FMS, and give corresponding maintenance planning as well. The developed system has been applied to an existing FFS-1500-2 FMS in Zhengzhou Textile Machinery Plant and has achieved good results.

Evaluation of overall equipment effectiveness based on market

Purpose – Continuous manufacturing systems used within the steel industry involve different machines and processes that are arranged in a sequence of operations in order to manufacture the products. The steel industry is generally a capital‐intensive industry and, because of high capital investment, the utilisation of equipment as effectively as possible is of high priority. This paper seeks to illustrate a new method, overall equipment effectiveness market‐based (OEE‐MB) for the precise calculation of equipment effectiveness for full process cycle in order to respond to the steel market.Design/methodology/approach – A refinement of the existing concept of OEE is developed based on a new scheme for loss analysis within market time. The paper illustrates the concept with a case study based on compact strip manufacturing processes within the steel industry.Findings – While the results for OEE by ignoring a considerable amount of possible hidden losses might be satisfying, the OEE‐MB report shows potential r...

Two diagnostic models for PLC controlled flexible manufacturing systems

The control of flexible manufacturing systems (FMSs) is generally characterised by logical and sequential functions under the auspices of a programmable logic controller (PLC). Operational faults associated with control processes are often confusing to maintenance personnel at workshop level. This has resulted in the development of automatic diagnosis techniques. In this paper two generic diagnostic models based on the logical function chart and sequential control process of the PLC are developed. With the two complementary models, the major operational faults of PLC controlled FMSs can be diagnosed. Application of the models to a typical FMS is presented.

Maintenance Today and Future Trends

This chapter describes the state of the art in maintenance and its future trends. The key areas that have influenced maintenance in the last 40 years are management of people and assets, and technological capability. These areas are important because they aim to take the best advantage of expensive resources, whether that advantage be profit, or to provide the best possible service with limited resources. The chapter first sets out the current range of maintenance in industrial practice. It is recognised that many businesses do not undertake the full extent of the work reported here, but it is our purpose to survey the state of the art. The chapter then continues to survey the influences of nascent technologies and ideas, before making some predictions about the future. Indeed, some of the most advanced condition-based maintenance effectively aims to predict the future. However, here we do not offer a crystal ball calibrated to international standards; we will constrain ourselves to an informed, independent opinion.

Decisions in condition monitoring-an examplar for data fusion architecture

This paper aims to demonstrate the strategy and structures involved in making decisions based on condition data, and to draw parallels with data fusion models. A new df model is demonstrated, and examples are drawn from condition monitoring applications. In particular, the work introduces a new framework for the application of data fusion solutions to the analysis of engineering problems. A review of frameworks used in data fusion applications is presented, along with important factors to consider in the layout of a robust process model, to host a coherent and effective data fusion problem-solving strategy. The main theme of the work focuses on the development of an intelligent multi-sensored engine. The partners involved in this research effort aim to develop a robust methodology for sensing and analysis under harsh environments, stressing its application to the fields of combustion and fault diagnostics analysis. The proposed process model will be used to facilitate the implementation of a common strategy to tackle these problems.

A review of key planning and scheduling in the rail industry in Europe and UK

Planning and scheduling activities within the rail industry have benefited from developments in computer-based simulation and modelling techniques over the last 25 years. Increasingly, the use of computational intelligence in such tasks is featuring more heavily in research publications. This paper examines a number of common rail-based planning and scheduling activities and how they benefit from five broad technology approaches. Summary tables of papers are provided relating to rail planning and scheduling activities and to the use of expert and decision systems in the rail industry.

A methodology for online wear debris morphology and composition analysis

Online or inline detection of basic debris features, i.e. size, quantity, size distribution, shape, and compositions simultaneously with real time diagnostics is one of the possible ways to perform wear debris analysis with high reliability. At present many techniques and sensors are available that can perform near real time detection and diagnostics for debris quantitative features. But to perform real time detection and diagnostics for features like shape and composition still requires a reliable technical concept. In this article a new technique for online wear debris shape and composition analysis is described. The developed technique is a combination of hardware and software based on imaging technology for shape and composition detection. Rule-based algorithms are used to perform near real time debris analysis diagnostics. An experimental study is also presented that shows the possible potential of the developed technique on real applications.