CORDIS provides links to public deliverables and publications of HORIZON projects.
Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .
Exploitable results
The overall industrial objective of the research was to develop a quasi-on-line system for the diagnosis of faults in reciprocating machines. The industrial areas identified in the work programme were; large marine engines, power generating diesels and refinery compressors.
One of the main claimed innovations of the project is in the application of acoustic emission (AE) monitoring of large reciprocating machines. In this context, a major oil refining company (HEL.P) has found AE to be a powerful adjunct to its traditional acceleration monitoring techniques and, when combined, these techniques have given higher confidence diagnosis of faults in process-critical reciprocating compressors. The two diesel engine manufacturers (MAN B&W and Perkins) have found AE monitoring to show considerable promise for on-line condition monitoring and/or as an input to engine management systems.
In such machines, the higher signal:noise ratio offered by AE makes it superior to acceleration monitoring and its non-intrusive nature makes it preferable to in-cylinder pressure monitoring. It was concluded early in the project that near and far field sound measurements were insufficiently precise and noise-free to supply any additional diagnostic information.
Eleven engines or test rigs have been used to study 14 faults on test beds at five locations. The results show that AE signals are most sensitive to the faults considered for both mechanical and fluid related signals. Acceleration signals are much less sensitive and then only to mechanical events. However the combination of both sensors offers the best solution in identifying (discriminating) fault and condition.
The signals acquired on all of the machines considered so far have a number of generic features. Sharp spikes and rapid fluctuations over parts of the signals can be used to distinguish between mechanical and fluid events in engines of varying size and function. Identification of these events enables signal mapping and extraction of basic parameters such as timing, engine speed, combustion, load and valve operation. AE sensors are more sensitive to position and offer capabilities for source location using arrays of sensors.
Algorithms developed during the project demonstrate the capabilities of AE monitoring since those of acceleration monitoring are fairly well established. In each case, AE was the only non-intrusive technique, which can offer the level of diagnostic specificity required to identify faults at an early stage and to give a measure of fault severity. The modular approach chosen has allowed a high level of flexibility and ultimately partners will be able to exploit one or more of the modules whilst taking advantage of the common elements of the system, such as in data acquisition, signal processing and data handling. Refinements to the algorithms carried out since the MTA include; a generic means of treating reciprocating machinery signals, the reconstruction of in-cylinder pressure curves from non-invasive measurements, and engine running characteristics (e.g. combustion behaviour). All of the above measurements can be achieved on-line and there is considerable commercial potential to develop embedded systems.
Searching for OpenAIRE data...
There was an error trying to search data from OpenAIRE
No results available