Systems integration in maintenance engineering

Starr, Andrew; Ball, Andrew

doi:10.1243/0954408001530128

Cited by 4 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8 Data fusion techniques have been widely applied in many areas such as robotic, 9 image processing, 10 condition monitoring and fault diagnosis, 7,11,12 pattern recognition 13 and maintenance engineering. 14 Data fusion techniques combine data from multiple sensors, and related information from associated databases, to achieve improved accuracies and more specific inferences than achieve using a single sensor. 15,16 In data fusion methods, data combination may be performed at three levels: 17 Data level: At this level, information of each sensor is directly combined.…”

Section: Introductionmentioning

confidence: 99%

Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory

Khazaee

Ahmadi

Omid

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

Nowadays, the ever increasing need for higher accuracy, reliability and security in modern industries has given rise intensively to the use of multi-sensor data fusion method in fault diagnosis of industrial equipment. In this article, an effective and powerful method for precise fault diagnosis of planetary gearbox based on fusion of vibration and acoustic data using the Dempster–Shafer theory is presented. For this purpose, the vibration and acoustic signals in different modes of the gears were first received simultaneously by two separate sensors and then were transmitted from time domain to time–frequency domain using wavelet analysis. After signal processing, each sensor's data were transferred to a local classifier for primary fault diagnosis. Local classification was performed by artificial neural network classifier. The outputs of the local classification were used as the inputs into Dempster–Shafer rules for fusion of classifiers and achieving the final accuracy of the classification. In primary fault diagnosis, the accuracy of fault classification based on vibration and acoustic signals was obtained as 86% and 88%, respectively. After incorporating the outcomes of two sensors, the final accuracy of the classification was calculated as 98% which indicates a 10% jump compared to single-sensor mode. These results indicate the effectiveness of the data fusion method in condition monitoring and fault diagnosis of the equipment. Moreover, in this article, the capability of Dempster–Shafer theory in the fusion of uncertain data and the increase of accuracy in the classification was demonstrated to a quiet acceptable level.

show abstract

Section: Introductionmentioning

confidence: 99%

Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory

Khazaee

Ahmadi

Omid

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part E:

View full text Add to dashboard Cite

show abstract

“…Integrated systems should automate the retrieval of information that decision makers require to make sound judgements. Essentially it should be a means of establishing links between data sources and close the loop from the minutiae of data to collection to strategic decision making [4].…”

Section: Integration Of Criticalitymentioning

confidence: 99%

“…A limiting factor is the existing level of systems integration. Starr and Ball [4] suggest that this can be addressed by the holistic approach and the adoption of new technology, including:…”

Section: Integrating Information Systemsmentioning

confidence: 99%

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

Moore

Starr

2006

Computers in Industry

114

View full text Add to dashboard Cite

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.

show abstract

“…Wei et al [10] presented a maintenance strategy which is a combination of both routine maintenance as well as daily predictive maintenance and repairs. Furthermore, the need for a systemic approach to maintenance has been favoured by researchers in the recent past [11,12] . The use of computer networks and distributed intelligence agents technologies are increasingly being applied to the industrial scenario [13] .…”

Section: Introductionmentioning

confidence: 99%

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

Verma

Srividya

Ramesh

2010

Int. J. Autom. Comput.

View full text Add to dashboard Cite

Large engineering plants (LEPs) have certain unique features that necessitate a maintenance strategy that is a combination of both time and condition based maintenance. Although this requirement is appreciated to varying degrees by asset owners, applied research leading to a systematic development of such a maintenance strategy is the need of the day. Such a strategy should also adopt a wholesome "systemic" approach so that the realization of the overall objectives of maintenance is maximized. E-maintenance has several potential benefits for large engineering plants. In this paper, a three pronged strategy is suggested for the successful implementation of e-maintenance for LEPs. Firstly, an integrated condition and time based maintenance framework is proposed for LEPs. Secondly, reference is drawn to models for condition and time based maintenance at systemic levels. As a part of the ab initio development of a condition monitoring system for a LEP, one of the characteristics of the condition monitoring system, namely, predictability, is discussed in detail as a sample for a systemic study. Thirdly, emphasis is laid on the information and expertise available in the domain of plant design, operation and maintenance and the same is tapped for incorporation in maintenance decision making.

show abstract

Systems integration in maintenance engineering

Cited by 4 publications

References 15 publications

Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory

Classifier fusion of vibration and acoustic signals for fault diagnosis and classification of planetary gears based on Dempster–Shafer evidence theory

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

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

Contact Info

Product

Resources

About