Bayesian approach to identifying sub-module failure
First Claim
1. A method of identifying sub-module failure within a system having a plurality of sub-modules, the method comprising:
- calculating a prior probability of failure associated with each sub-module based on a lambda-smoothing equation
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Accused Products
Abstract
A diagnostic device identifies failed sub-modules within a larger system based on error codes received from the system. The device stores a likelihood matrix that correlates each sub-module with each possible error code and maintains a likelihood value corresponding to the probability of a failed sub-module generating a corresponding error code and stores a prior probability of failure associated with each sub-module based on prior observational data. In response to received error codes, the device calculates a posterior probability of failure for each of the plurality of sub-modules based on a product of the likelihood values corresponding to the received error codes and the prior probability of failure associated with each sub-module. Based on the calculated posterior probability, the device identifies the sub-module with the highest posterior probability of failure as the failed sub-module.
12 Citations
13 Claims
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1. A method of identifying sub-module failure within a system having a plurality of sub-modules, the method comprising:
calculating a prior probability of failure associated with each sub-module based on a lambda-smoothing equation - View Dependent Claims (2, 3)
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4. A diagnostic device for identifying failed sub-modules within a larger system, the diagnostic device comprising:
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an input for receiving error codes from the system; a memory for storing a likelihood matrix that correlates each sub-module with each possible error code and maintains a likelihood value corresponding to the probability of a failed sub-module generating a corresponding error code and stores a prior probability of failure associated with each sub-module based on prior observational data, wherein the prior probabilities included in the likelihood matrix are calculated with a lambda-smoothing algorithm that is based on a number of observed failures associated with a particular sub-module, a number of sub-modules included within the system, and a number of total observed failures associated with the system; and a processor for executing an algorithm that calculates a posterior probability of failure for each of the plurality of sub-modules based on a product of the likelihood values corresponding to the received error codes and the prior probability of failure associated with each sub-module, wherein the processor generates an output identifying the sub-module with the highest posterior probability of failure as the failed sub-module. - View Dependent Claims (5, 6, 7, 8, 9)
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10. A diagnostic device for identifying failed sub-modules within a larger system, the diagnostic device comprising:
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memory means for storing a likelihood matrix that includes likelihood values corresponding to a probability of a failed sub-module generating error codes and a prior probability of failure associated with each sub-module, wherein the likelihood values are calculated with a lambda-smoothing algorithm that is based on a number of observed failures associated with a particular sub-module, a number of sub-modules included within the system, and a number of total observed failures associated with the system; processor means for calculating a posterior probability of failure for each of the plurality of sub-modules based on likelihood values selected that correspond to the error codes and the prior probability of failure associated with each sub-module; and output means for identifying based on the calculated posterior probability of failure associated with each sub-module the sub-module most likely to have failed. - View Dependent Claims (11, 12, 13)
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Specification