Neural network system for estimation of helicopter gross weight and center of gravity location

• US 5,987,397 A
• Filed: 03/13/1998
• Issued: 11/16/1999
• Est. Priority Date: 03/13/1998
• Status: Expired due to Fees

• Current Assignee: the united states of america as represented by the secretary of the navy
• Original Assignee: the united states of america as represented by the secretary of the navy
• Inventors: Haas, David, McCool, Kelly
• Primary Examiner(s): Hoff, Marc S.
• Assistant Examiner(s): VO, HIEN XUAN
• Application Number:
  US09/042,045
• Time in Patent Office: 613 Days
• Field of Search: 702/173, 702/169, 702/144, 364/158, 73/65.06, 73/65.05, 73/178 H, 706/25
• US Class Current: 702/173
• CPC Class Codes: G01G 19/07 for weighing aircraft

Abstract

The invention is directed to a helicopter health and usage monitoring system utilizing a neural network for estimating gross weight and center of gravity location from measured flight condition parameter inputs; and includes means for measuring a plurality of variable flight condition parameters during flight of the helicopter; memory means for successively receiving and storing parameter input signals as well as estimates of gross weight and center of gravity location; and processing means responsive to the signals received from the measurement means for generating the gross weight and center of gravity location estimates.

Claims

1. A system for estimating information on gross weight and center of gravity location onboard a helicopter by using variable input parameters measured during airflight in a reference frame fixed relative to the helicopter, including:

means for generating successive signals representing said variable input parameters measured; and

means for determining from said signals the estimated information in terms of real time values of the gross weight and center of gravity location in accordance with a learned relationship.

2. The system as defined in claim 1 wherein said learned relationship is developed by use of at least one neural network algorithm which interrelates said input parameters with coinciding reference data obtained from previously recorded flight data.

3. The system as defined in claim 2 wherein said coinciding reference data relates to the gross weight and center of gravity location of the helicopter.

4. The system as defined in claim 3 wherein said means for determining the real time values includes:

a memory device for storing the measured variable input parameters and the neural network algorithm; and

means for signal processing helicopter fuel information and initial estimates of the gross weight and center of gravity location during hover conditions to obtain predictions on the gross weight and center of gravity location subsequently calculated during airflight.

5. The system as defined in claim 4 wherein the initial estimates are updated by signal processing of data generated during airflight subsequent to the hover conditions.

6. The system as defined in claim 1 wherein said means for determining the real time values includes:

a memory device for storing the measured variable input parameters and an algorithm; and

means for signal processing measured variable input parameters and helicopter fuel information in accordance with said algorithm to obtain the estimated information on the gross weight and center of gravity location subsequently calculated during airflight.

8. The system as defined in claim 1 wherein said learned relationship is developed by use of at least one neural network algorithm which interrelates said input parameters with coinciding reference data obtained from previously recorded flight data.

9. The system as defined in claim 2 wherein said coinciding reference data relates to the gross weight and center of gravity location of the helicopter.

10. The system as defined in claim 3 wherein said step of determining the real time values includes the steps of:

storing the measured variable input parameters and the neural network algorithm in a memory device; and

signal processing helicopter fuel information and initial estimates of the gross weight and center of gravity location during hover conditions to obtain predictions on the gross weight and center of gravity location subsequently calculated during airflight.

11. The system as defined in claim 10 wherein the initial estimates are updated by signal processing of data generated during airflight subsequent to the hover conditions.

7. A system for estimating information on gross weight and center of gravity location onboard a helicopter by using variable input parameters measured during airflight in a reference frame fixed relative to the helicopter, including the steps of generating successive signals representing said measured variable input parameters;

and determining from said signals the estimated information in terms of real time values of the gross weight and center of gravity location in accordance with a learned relationship.

12. The system as defined in claim 7 wherein said step of determining the real time values includes the steps of:

storing the measured variable input parameters and an algorithm in a memory device; and

signal processing helicopter fuel information in accordance with said algorithm to obtain the estimated information on the gross weight and center of gravity location.