FAULT DIAGNOSIS AND PROGNOSIS USING DIAGNOSTIC TROUBLE CODE MARKOV CHAINS
First Claim
1. A fault diagnosis and prognosis system, comprising:
- a plurality of electronic control units, wherein at least one of the plurality of electronic control units is configured to;
receive information defining a relationship between failure modes and diagnostic trouble codes;
extract diagnostic trouble code data for a plurality of diagnostic trouble codes relating to a particular failure mode;
construct a Markov chain using the diagnostic trouble code data for the particular failure mode;
train the Markov chain to learn a set of state parameters using the diagnostic trouble code data;
compute a likelihood of a diagnostic trouble code sequence for the particular failure mode using the trained Markov chain; and
predict a trend using the trained Markov chain.
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Accused Products
Abstract
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.
46 Citations
20 Claims
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1. A fault diagnosis and prognosis system, comprising:
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a plurality of electronic control units, wherein at least one of the plurality of electronic control units is configured to; receive information defining a relationship between failure modes and diagnostic trouble codes; extract diagnostic trouble code data for a plurality of diagnostic trouble codes relating to a particular failure mode; construct a Markov chain using the diagnostic trouble code data for the particular failure mode; train the Markov chain to learn a set of state parameters using the diagnostic trouble code data; compute a likelihood of a diagnostic trouble code sequence for the particular failure mode using the trained Markov chain; and predict a trend using the trained Markov chain. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
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14. A method for fault diagnosis, comprising:
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receiving information defining a relationship between failure modes and diagnostic trouble codes; 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; 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; computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains. - View Dependent Claims (15, 16, 17, 18)
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19. A computer-readable medium tangibly embodying computer-executable instructions for:
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receiving a diagnostic matrix defining a relationship between failure modes and diagnostic trouble codes; 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; constructing a Markov chain using the diagnostic trouble code data for each of the plurality of failure modes, wherein each of the diagnostic trouble codes represent a state of the Markov chain; training the Markov chain to learn a set of state parameters using the diagnostic trouble code data, wherein the set of state parameters includes initial and transition probabilities for each state of the Markov chain; computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained Markov chains; determining a fault diagnosis by ranking a likelihood for each of the plurality of failure modes in order of most likely to fail; and predicting a remaining time to next failure state using the trained Markov chain. - View Dependent Claims (20)
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Specification