Professor Peter Cawley

 

Professor Peter Cawley

Department of Mechanical Engineering, Imperial College, London SW7 2AZ, UK.

Peter Cawley received BSc and PhD degrees in Mechanical Engineering from University of Bristol in 1975 and 1979 respectively.
He worked in industry from 1979-1981 and then joined the Mechanical Engineering Department at Imperial College, London initially as a lecturer and then successively senior lecturer, reader and professor. He is now head of the Imperial College Mechanical Engineering department and leads the NDE research group; he is also the principal investigator of the UK Research Centre for NDE (RCNDE) that has its head office at Imperial College. He has published over 180 refereed journal papers and a similar number of conference papers in this field and holds 4 current patents.
Peter Cawley is a fellow of the Royal Academy of Engineering and of the Royal Society. He is a director of two spin-out companies set up to exploit technology developed in his research group (Guided Ultrasonics Ltd and Permasense Ltd, both of which supply inspection and monitoring equipment to the petrochemical and other industries), and he is a consultant to a variety of industries.

Advances in In-Service NDT and Monitoring

Peter Cawley
UK Research Centre in NDE (RCNDE)
Department of Mechanical Engineering, Imperial College London

Abstract:
Plant availability is an increasingly important concern and it is highly desirable to extend service and inspection intervals. If conventional NDT is used, this means that it is necessary to detect smaller defects in order to ensure that they will not propagate to failure in the extended period before the next outage. An attractive alternative is replace traditional NDT with a permanently installed monitoring system that can either be interrogated on demand by connecting a test instrument or, if the electronics is sufficiently cheap and robust, can send data at regular intervals. The decreasing cost of electronics, coupled with low power wireless systems and long-life batteries mean that it is increasingly possible to provide regular data-to-desk for operators. However, this stream of data can overwhelm operators who are used to manual interpretation, so it must be analysed automatically to highlight areas of concern that the operator should investigate further. This paper will discuss the trend from periodic inspection to continuous monitoring and describe a data analysis framework that allows the performance of an installed monitoring system to be assessed, and for data collection frequencies for a given defect detection requirement to be specified. The framework will be illustrated with data from a guided wave pipe monitoring system, but the methodology is applicable to different sensor systems.