AbstractCondition Monitoring (CM) of fluid machines plays a critical role in maintaining efficient productivity in many processing industries. Conventional vibration techniques generally
provide more localised information with the need for many sensors, associated data acquiring and processing efforts, which are difficult for system deployment and are reluctantly accepted by those industries, for example paper mills and food production lines making marginal profits.
To find adequate CM techniques for such industries this research investigates a new cost effective scheme of implementing CM, which combines the high diagnostic capability of using Surface Vibration (SV) with the global detection capability of using the Instantaneous Angular Speed (IAS) measurements and Airborne Sound (AS). To address specific techniques involved in the scheme, this research is arranged in three consecutive Phases: Phase I is the technical evaluation; Phase II is the field implementation practices and Phase III is the application of AS through Convolution Neural Networks (CNN).
In Phase I, widely used reciprocating compressor is investigated numerically and experimentally, which clarifies the performances of SV, IAS, AS, pressure and motor current
in a quantitative way for differentiating common faults such as leakages happening in valves and intercoolers, faulty motor drives and mechanical transmission systems. It paves the foundations for the field implementation in Phase II.
In Phase II, this novel scheme is realised on three sets of vacuum pumps in a paper mill. Based on an analytic study of dynamic responses to common faults on these pumps, a field test was conducted to verify the feasibility of the scheme and the preliminary study shows that airborne sound can show the relative spectral components for each machine to a good degree of accuracy.
Knowledge gained from the preceding phases of study is now applied to Phase III. New techniques based on airborne signal differences through CNN have been demonstrated to give a good indication of the sound propagation and location of noise sources under all operating discharge pressure conditions at 100% validation accuracy, proving that the state of the art deep leaning approaches can be used to deal with complicated acoustic data.
|Date of Award||2023|
|Supervisor||Andrew Ball (Co-Supervisor) & Fengshou Gu (Co-Supervisor)|