| Patent application number | Description | Published |
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| 20090265364 | METHOD AND PROCESS FOR AUTOMATIC GENERATION OF SYMPTOM CODES FROM TEXTUAL PROBLEM DESCRIPTIONS TO ENABLE PROBLEM CLASSIFICATION, EARLY WARNING TREND PREDICTION, AND FAST RECALL OF PROGNOSTIC/DIAGNOSTIC SOLUTIONS - A system and method for converting text related to vehicle service to symptom codes. The method includes typing into work orders and service reports statements that describe the various symptoms and problems of a vehicle that is being serviced. The work orders and service reports are then transmitted to a database facility where they are analyzed. Prior to the reports being analyzed, the text in the work order and service reports is read by a machine reader that converts the text to symptom codes that describe particular vehicle conditions and symptoms. A processor analyzes the codes for patterns and other relationships, and can provide a display of such patterns. Further, the codes and reports are stored in a memory. | 10-22-2009 |
| 20090295559 | INTEGRATED HIERARCHICAL PROCESS FOR FAULT DETECTION AND ISOLATION - A system and method for determining the root cause of a fault in a vehicle system, sub-system or component using models and observations. In one embodiment, a hierarchical tree is employed to combine trouble or diagnostic codes from multiple sub-systems and components to get a confidence estimate of whether a certain diagnostic code is accurately giving an indication of problem with a particular sub-system or component. In another embodiment, a hierarchical diagnosis network is employed that relies on the theory of hierarchical information whereby at any level of the network only the required abstracted information is being used for decision making. In another embodiment, a graph-based diagnosis and prognosis system is employed that includes a plurality of nodes interconnected by information pathways. The nodes are fault diagnosis and fault prognosis nodes for components or sub-systems, and contain fault and state-of-health diagnosis and reasoning modules. | 12-03-2009 |
| 20100191414 | INTEGRATED DIAGNOSIS AND PROGNOSIS SYSTEM AS PART OF THE CORPORATE VALUE CHAIN - An integrated diagnosis and prognosis system that collects vehicle information over the life of a vehicle and its development. The system provides the collected vehicle information to supplier management, product development management, service/dealership management, customer relations departments and production facilities, which use the information to take certain action for existing vehicles, fleets of vehicles or future vehicles to improve vehicle reliability and quality. | 07-29-2010 |
| 20110118932 | FAULT DIAGNOSIS AND PROGNOSIS USING DIAGNOSTIC TROUBLE CODE MARKOV CHAINS - A system and method for fault diagnosis includes receiving information defining a relationship between failure modes and diagnostic trouble codes and extracting diagnostic trouble code data, including set times, frequency data and diagnostic trouble code sequence information for a plurality of diagnostic trouble codes relating to a plurality of failure modes. The system and method further include constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, and computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains. | 05-19-2011 |
| 20110238258 | EVENT-DRIVEN FAULT DIAGNOSIS FRAMEWORK FOR AUTOMOTIVE SYSTEMS - Systems and methods for capturing and analyzing significant parameter data from vehicle systems whenever a diagnostic trouble code (DTC) is triggered. A multi-dimensional matrix is constructed, with vehicles, DTCs, and parameter data comprising three dimensions of the matrix. The data matrix is populated with DTC and parameter data from many different vehicles, either when vehicles are taken to a dealer for service, or via wireless data download. Time can be added as a fourth dimension of the matrix, providing an indication of whether a particular system or component is temporally degrading. When sufficient data is accumulated, the data matrix is pre-processed, features are extracted from the data, and the features are classified, using a variety of mathematical techniques. Trained classifiers are then used to diagnose the root cause of any particular fault signal, and also to provide a prognosis of system health and remaining useful life. | 09-29-2011 |
