4 research outputs found
A review of aircraft auxiliary power unit faults, diagnostics and acoustic measurem
The Auxiliary Power Unit (APU) is an integral part of an aircraft, providing electrical and pneumatic power to various on-board sub-systems. APU failure results in delay or cancellation of a flight, accompanied by the imposition of hefty fines from the regional authorities. Such inadvertent situations can be avoided by continuously monitoring the health of the system and reporting any incipient fault to the MRO (Maintenance Repair and Overhaul) organization. Generally, enablers for such health monitoring techniques are embedded during a product's design. However, a situation may arise where only the critical components are regularly monitored, and their status presented to the operator. In such cases, efforts can be made during service to incorporate additional health monitoring features using the already installed sensing mechanisms supplemented by maintenance data or by instrumenting the system with appropriate sensors. Due to the inherently critical nature of aircraft systems, it is necessary that instrumentation does not interfere with a system's performance and does not pose any safety concerns. One such method is to install non-intrusive vibroacoustic sensors such that the system integrity is maintained while maximizing system fault diagnostic knowledge. To start such an approach, an in-depth literature survey is necessary as this has not been previously reported in a consolidated manner. Therefore, this paper concentrates on auxiliary power units, their failure modes, maintenance strategies, fault diagnostic methodologies, and their acoustic signature. The recent trend in APU design and requirements, and the need for innovative fault diagnostics techniques and acoustic measurements for future aircraft, have also been summarized. Finally, the paper will highlight the shortcomings found during the survey, the challenges, and prospects, of utilizing sound as a source of diagnostics for aircraft auxiliary power units
Condition Monitoring and Fault Diagnosis of Fluid Machines in Process Industries
Condition 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