The authors would like to thank the Rockwell Company, Milwaukee, USA, for the sponsorship of the project.Abstract-Faults in pumping systems can be caused through the changes in flow regimes, such as cavitation, that lead to impeller degradation or corruption of pumped material, and ultimately to the deterioration and breakdown of pumps themselves. Results are presented of experiments using spectral analysis of motor current to diagnose and predict specific failures in pumps. Fault signatures were established by relating spectral features to individual faults, and by analysing their behaviour in the presence of faults. Faults investigated were cavitation, blockage, and impeller damage. A fuzzy logic system was built as a final decision making module.

Maintenance of process equipment is one of the inescapable tasks associated with the operation of process plants and, until relatively recently, it was implemented either on a routine basis or after the failure of equipment. Attitudes are changing and now many organizations are adopting methods for identifying incipient faults, so that maintenance can be scheduled before there has been a failure which would lead to loss of production and spoilage of raw material. These methods include schemes in which all the measurement signals in a plant are gathered and, in addition to providing the input to the various control systems, they are compared with model-based values for individual sections throughout the entire process, so that a warning or an alarm can be raised, according to the seriousness of the observed discrepancy. Although these methods represent a considerable advance on ‘routine maintenance’ and ‘repair after failure’, their effectiveness is limited by the quality and detail of the information that is gathered. Each process measurement should be regarded as a ‘window’ through which the operation of the process can be observed. However, the majority of existing process measurement systems have been designed to provide the input signal to a process control system, for which the primary requirement is a good average value. Typically, this is obtained by filtering out all the components of the sensor signal having frequencies greater than about 5 Hz, with the result that the ‘window’ is only translucent, rather than transparent, and much of the detailed information which is available at the interface between the process and the sensor is lost. If, on the other hand, the measurement system has a wide frequency response, then much of the information that is available at the interface between the process and the sensor can be gathered and analysed to provide a clear view of the operation and status of both the process and the measurement system. This concept involves two significant departures from the conventional approach to process measurements and to the utilization of measurement information. In the first place, the measurement systems should be designed to have the widest possible frequency response, so that the maximum information regarding the measured process parameter that is present at the interface between the process and the sensor is gathered. Secondly, instead of conditioning the primary sensor signal to provide a good average value for process control, it is analysed using well established techniques to provide not only the signal for process control but also information on which predictive maintenance procedures can be based.

No abstract

The signal conditioning and processing circuits of conventional electromagnetic flowmeters have been designed to provide an accurate average flowrate measurement signal, principally for the purpose of process control. One consequence of this is that the ‘noise’ and other low frequency components of the electrode signal are suppressed. Hitherto, the possibility that they may carry potentially useful information has been overlooked, but there are studies which show that information regarding the flow regime can be identified by spectral analysis of the higher frequency or ‘noise’ components of the sensor signals from some other types of flowmeters. If the entire electrode signal is analysed, using well-established signal analysis methods, diagnostic information regarding the flow regime in which the flowmeter is operating can be recovered and several distinctly different flow regimes can be identified, such as increased turbulence, swirling flow, flow pulsations and two-phase flow, all of which adversely affect the performance of the flowmeter. This paper presents the results of laboratory simulations of these flow regimes and describes a fuzzy logic method for identifying them. It also suggests a change to the conventional mode of operation and signal processing which would enable the additional functions to be implemented without involving any modification to the design or construction of the conventional flowtube. The potential benefits which arise from its application include the identification of flow regimes which adversely affect the performance of the flowmeter in its installed position and the ability to verify, on line, that the flowmeter is functioning correctly.