Fault diagnosis and prognosis using diagnostic trouble code markov chains

A method for fault diagnosis, comprising:

• 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 set of Markov chains using the diagnostic trouble code data for each of the plurality of failure modes;
• training the set of Markov chains 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 set of Markov chains.

A computer-readable non-transitory medium tangibly embodying computer-executable instructions for:

• 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 set of Markov chains 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 a Markov chain from the set of Markov chains;
• training the set of Markov chains 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 from the set of Markov chains;
• computing a likelihood of a diagnostic trouble code sequence for each of the plurality of failure modes using the trained set of 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.