Original Date: 03/08/1999
Revision Date: 01/18/2007
Information : Advanced Modeling as a Knowledge Base
Modeling is an integral part of the Applied Research Laboratory at the Pennsylvania State University’s (ARL Penn State’s) six-layer hierarchy for integrated predictive diagnostics. This process translates the sensing observables from a physical model with machinery phenomena effects. The development of model-based prognostic capability for condition based maintenance (CBM) requires a proven methodology to create and validate physical models which capture the system’s dynamic (vibratory) response under normal and faulted conditions.
In heavy duty and high performance power transmission systems, the rotary elements can be driven to catastrophic failure through various mechanisms. Subsystem component defective material and/or normal wear can lead to fatigue stress cracks. Damage initiated by transient load swings, due to larger magnitudes and higher-than-expected amounts of intermittent loading cycles, can also occur in a system when operational performance limits are chronically commanded. For most systems, operational demands prescribe a slow evolution (compared to operational speed or length of a given machine service event) in material property and/or component configuration changes. Therefore, the potential exists to track the fault through the filter of the system’s behavior via its vibratory response.
The uniqueness of a fault’s system-perturbing force, caused by the nature of a fault’s physical mechanism, is of primary importance in identifying a signature in the vibratory response of a system. Faults occur at the weakest links in the physical load path that transmit the largest amounts of power and experience the largest, local stress variations due to dynamic loading. The load path in power transmission equipment can be complex; however, it may be identified as traveling through a few common components (e.g., gears, rotary shafts, rotary bearings, machine housing/frames). In terms of life-cycle fatigue behavior; faults/failures in gear pairs; rotary shafting; and bearings, the outcome is understood, but dynamic response and tribological information are lacking for machines operated to failure. This shortcoming motivated the ARL Penn State to develop the mechanical diagnostics test bed (MDTB). The laboratory uses this test bed facility to provide transitional failure data on gearboxes.
The ARL Penn State’s effort consists of computational and experimental work to correctly characterize the dynamic response. Currently under consideration by the laboratory is preservation of salient-relative dynamic features to capture and aid in a comprehensive understanding of deficiencies in current, macro-fault characterization models. This gearbox system model will also serve as a numerical study test bed to aid in optimal (or development of a best) sensor location strategy for CBM. Drive, misalignment, rotating unbalance loads, and some operating constraints will be assessed and estimated from experimental MDTB operating and transitional test data. Computational modeling efforts include finite element modeling and experimental identification/characterization of system modal and transfer characteristics. Analytical dynamic models of gear mesh, rotating shaft, and bearing faults will be adapted for integration and inclusion into an overall system model as nonlinear system perturbation forces.
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