Technology International Incorporated 0f Virginia
US Navy

Construction Of A Detectable Defect Drift, Diffusion (D4) Model For Prediction Of Helicopter Gearbox Life Expectancy

Topic: N88-248 Mathematical Model For Predicting Helicopter Gearbox Failure Modes; Performance Period: 9/30/1988 through 4/30/1989

Contract #: N00421-88-C-0335; Naval Air System (NAS); Command Issued by Supply Department, Naval Air Station, Patuxant River, MD

TPOC/Project Manager: Mr. Joe Whittington, Mr. Robert Jordan, Mr. Cy Seibert, NAS, Pensecola, FL and Mr. Gary wise and Mark Holline

Principal Investigator:    Dr. Enju Liang

Project team: Dr. Abdo A. Husseiny

Publications

Enju Liang and Abdo A. Husseiny (April 1989). Construction Of A Detectable Defect Drift, Diffusion (D4) Model For Prediction Of Helicopter Gearbox Life Expectancy. Final Technical Report; Contract #: N00421-88-C-0335 Naval Air System Command Issued by Supply Department, Naval Air Station, Patuxant River, MD

Patent

Husseiny; Abdo A. (May 11, 1993). System for prognosis and diagnostics of failure and wearout monitoring and for prediction of life expectancy of helicopter gearboxes and other rotating equipment. United States Patent # 5,210,704; Technology International Incorporated (LaPlace, LA)

Summary

A detectable defect, drift, diffusion (D4) model was developed for prediction of helicopter gearbox life expectancy given a known type and size of bearing or gear defect. The prediction model was designed for incorporation into a capability to provide a correlation between the size of detectable defects and the warning time (expected duration of acceptable operating time) that remains before the gearbox can no longer transmit the required operating power.

The basic data required for the model has to provide a clear indication of the size and type of the defect at the initial stage of detection.  The defect-related data has to be measured during operation.  A data bank on variety of defects may be developed by recording the results of engineering investigation in order to utilize the prediction capability off-line.  Also, output torque values would be useful in relationship with the applied power.

The model was developed on the basis that the dominant defect mechanism in the gear and bearing of the helicopter gearbox is likely to be initiated by the growth of microstructure deformation or microcracking.  The defect propagation mechanism is attributed to drift (defect growth rate) and diffusion processes which are basically Brownian in nature.

The prediction model is capable of providing quantitative prediction of the remaining life after the initiation of a defect in terms of defect size in a bearing or a gear.  However, it can be extended to the whole gearbox.  The warning time computed by the D4 model is explicitly related to the size and type of defect.  The model is independent of which type of element the defect occurs in (gear or bearing) as long as the defect size is detected for the element under consideration, however, the model is compatible with the failure mechanism of rolling equipment and elements.  The warning time can be displayed or indicated to the aircrew and can be evaluated accurately within statistical uncertainties and within an acceptable range, appropriate for safe abortion of a mission and would allow completion of a mission of reasonable duration.  The D4 model capability can be extended to consider the applied load (torque) and operating conditions (variability of load).

Some of the merits of the D4 model-based predictor are: simplicity, light weight, and small size of the required equipment for in-flight implementations; potential of reducing in-flight processing by developing a data bank of drift coefficients; minimal cost of implementation in-flight or off-flight; the model does not directly depend on material properties, plasticity or other complex phenomena; and the equipment needed for implementation are off-the-shelf items.

Based on the developed D4 prediction model, testing data can be used to simulate the defect propagation mechanism which is not yet well understood.  The results of the simulation can be utilized into methods to retard or arrest the defect growth.  The results can also be used am developing an optimal overhaul schedule.  The outcome of the model development can be adopted for other applications of rotary equipment which are subject to damage during operation.

Topic

N88-248

TITLE:  Mathematical Model For Predicting Helicopter Gearbox Failure Modes

CATEGORY: Advanced Development

DESCRIPTION:  A mathematical model is needed which will define the most likely failure to be encountered in modern helicopter gearboxes.  This will be used in developing a detection system utilizing various forms of vibration analysis for discovering defective bearings and gears within helicopter gearboxes.  Determining the most likely failure will require a statistical evaluation of gear and bearing failures from available engineering information on overhauled helicopter gearboxes.  The historical failure data should be partitioned into component  (e.g., gears, bearings, seals, free-wheeling units), failure mode (e.g., spalls, corrosion, cracks, lubrication depletion), gearbox (e.g., main transmission, intermediate gearbox, tail rotor gearbox), aircraft, component time (if possible) and any other classification that is appropriate.  Although Navy helicopters are of primary interest, suitable information on other helicopters would be of value.

Patent Summary

A wearout monitor for failure prognostics is a prognosis tool to predict incipient failure in rotating mechanical equipment. The wearout monitor provides maintenance management of a plant or process with information essential to planning preventive maintenance strategies. The monitor also assists in constructing a data base for development and implementation of policies for plant life extension, refurbishment, and modernization. The apparatus identifies systems of operation degradation of the whole system, as well as diagnosis of signs of commencing aging cycles of specific equipment, components or parts of equipment during operation. Data from the system is stored and also supplied to a central processing unit which includes an expert system, rule-based failure data bank, a predictor, a performance evaluator and a system identifier. The results of the predictions are supplied to management terminals or other indicators for subsequent use. Combination of prognostics and diagnostics of the symptoms of existing fault in mechanical equipment allows continuous on-line monitoring of systems to predict failures at early stages before leading to catastrophic breakdown and to assure safe and economic operation. By providing correlations between defect sizes and life expectancy of a rotating mechanical component, the monitor can provide the operator of the equipment with a warning time that indicates the time before loss of operation, thereby being critical to operation of transport systems wherein gearboxes can lead to loss of transmission power and subsequent loss of life particularly in helicopters.