This paper describes how acoustic emission (AE) measurements can be used to supplement the mechanical information available from an indentation test. It examines the extent to which AE data can be used to replace time-consuming surface crack measurement data for the assessment of fracture toughness of brittle materials. AE is known to be sensitive to fracture events and so it was expected that features derived from the AE data may provide information on the processes (microscale and macroscale fracture events and densification) occurring during indentation. AE data were acquired during indentation tests on samples of a WC-12%Co coating of nominal thickness 300 lm at a variety of indentation loads. The raw AE signals were reduced to three stages and three features per stage, giving nine possible indicators per indentation. Each indicator was compared with the crack profile, measured both conventionally and using a profiling method which gives the total surface crack length around the indent. A selection of the indents was also sectioned in order to make some observations on the subsurface damage. It has been found that reproducible AE signals are generated during indentation involving three distinct stages, associated, respectively, with nonradial cracking, commencement of radial cracking, and continued descent of the indenter. It has been shown that AE can give at least as good a measure of cracking processes during indentation as is possible using crack measurement after indentation.

This work concerns the detection of incipient cavitation in pumps using acoustic emission (AE). Three activities have been pursued in this context: (a) the construction of a small-scale rig for the investigation of cavitation detection using AE sensors; (b) the acquisition of data on a 75 kW singlestage centrifugal pump in an industrial test loop under normal running and cavitation conditions; (c) the determination of parameters that could be used for the early diagnosis of cavitation within pumps.In the laboratory-scale apparatus water was pumped around a short loop by a 3 kW centrifugal pump. The flow loop contained a section specifically designed to induce cavitation by means of reducing the pressure level to that of the vapour pressure of the fluid. This apparatus was used to produce a variety of well-controlled cavitation conditions which were useful in determining the suitability of AE for the detection of cavitation.The industrial-scale tests consisted of progressively reducing the net positive suction head in a 75 kW pump while recording the AE signals at various points on the test loop and pump.Results are presented from both laboratory and full-scale tests which demonstrate the feasibility of detecting incipient cavitation using AE in the face of background noise from normal running of the pump. The features of AE which are indicative of cavitation are also seen to change continuously as NPSH is decreased. Thus early detection of cavitation is possible, certainly before any indication is seen on the dynamic head.this end, a small laboratory flow loop has been instru-

Citation for the version of the work held in 'OpenAIR@RGU. This work puts emphasis on using failure analysis as a basis for designing a condition based prognostic maintenance plan in order to control cost of power and make maintenance more efficient. An essential aspect of such failure analysis is to identify wind turbine components, ascertain their failures and find root causes of the failures. However as a first step, identification of prominent failures in the critical assemblies of a wind turbine using available inspection methods and making provisions to control their occurrence would make significant contribution in improving wind turbine reliability. This work introduces Failure Modes Effects and Criticality Analysis (FMECA) as an important failure analysis tool that has in the past successfully benefitted the airlines, marine, nuclear and spacecraft industries. FMECA is a structured failure analysis technique that can also evaluate the risk and priority number of a failure and hence assist in prioritising maintenance works. The work shows, how with a slight modification of the existing FMECA method, a very useful failure analysis method can be developed for offshore wind turbines including its operational uniqueness. This work further proposes modifying the format for calculating the Risk Priority Number (RPN) for wind turbine failure. By using wind turbine gearbox as a case study, this work illustrates the usefulness of RPN number in identifying failures which can assist in designing cost effective maintenance plan. Some preliminary results of a FMECA tool that has been developed to automatically evaluate the effects and criticality of a failure in a wind turbine at the component level is included.

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