Methods and systems for component wear prediction

• US 20050114088A1
• Filed: 11/24/2004
• Published: 05/26/2005
• Est. Priority Date: 11/24/2003
• Status: Abandoned Application

• Current Assignee: Ellis E. Gorden, William R. Rader
• Original Assignee: Ellis E. Gorden, William R. Rader
• Inventors: Rader, William R., Gorden, Ellis E.
• Application Number:
  US10/997,132
• Publication Number:
  US 20050114088A1
• Time in Patent Office: Days
• US Class Current: 702/185
• CPC Class Codes: F02D 41/22 Safety or indicating devices for abnormal conditions in air/fuel ratio feedback systems F02D41/1495, in electric control linkage F02D11/107, in purge control systems F02M25/0809
• CPC Class Codes: G01M 15/05 by combined monitoring of two or more different engine parameters
• CPC Class Codes: Y02T 10/40 Engine management systems

Abstract

Methods and systems are disclosed for detecting imminent engine failure. The methods and systems involve receiving, in real-time, on-board machine parameters associated with a piece of heavy equipment, the on-board machine parameters comprising fuel consumption and fluid temperature; receiving time correlated external machine parameters associated with the piece of heavy equipment, the external machine parameters comprising outside ambient temperature, engine oil sample analysis, and operator observations; recording failure event information associated with actual engine failures; and calculating an estimated failure time for the engine based on the on-board machine parameters, the external machine parameters, and the failure event information.

Claims

1. A method for detecting imminent engine failure comprising:

receiving, in real-time, on-board machine parameters associated with a piece of heavy equipment, the on-board machine parameters comprising fuel consumption and fluid temperature;

receiving time correlated external machine parameters associated with the piece of heavy equipment, the external machine parameters comprising outside ambient temperature, engine oil sample analysis, and operator observations;

recording failure event information associated with actual engine failures; and

calculating an estimated failure time for the engine based on the on-board machine parameters, the external machine parameters, and the failure event information.