Stochastic evidence aggregation system of failure modes utilizing a modified dempster-shafer theory
First Claim
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1. An information fusion system comprising;
- at least two diagnostic algorithms for providing belief values about a failure mode of a mechanism;
a filter for receiving the belief values from the diagnostic algorithms and providing smoothed belief values; and
an aggregator for aggregating the smoothed belief values into at least one diagnostic state; and
wherein;
one diagnostic algorithm is coupled to a diagnostic module comprising a demultiplexer having an output connected to an input of the filter;
another diagnostic algorithm is coupled to an aggregation module having an input connected to an output of the filter;
the filter is a Kalman filter; and
the filter is for smoothing noise in the belief values provided by the diagnostic algorithms;
the filter uses a diagnostic state at time t−
1 to smooth out noise in the belief value provided by a diagnostic algorithm at time t;
the aggregating the smoothed belief values from the filter takes into account a conflict among evidence provided by the diagnostic algorithms; and
multiple failure mode hypotheses are generated by taking a pair-wise union of elements in the conflict and iteratively taking pair-wise unions of multiple failure hypotheses generated during a previous iteration.
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Abstract
A system for obtaining diagnostic information, such as evidence about a mechanism, within an algorithmic framework, including filtering and aggregating the information through, for instance, a stochastic process. The output may be an overall belief value relative to a presence of an item such as, for example, a fault in the mechanism.
7 Citations
13 Claims
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1. An information fusion system comprising;
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at least two diagnostic algorithms for providing belief values about a failure mode of a mechanism; a filter for receiving the belief values from the diagnostic algorithms and providing smoothed belief values; and an aggregator for aggregating the smoothed belief values into at least one diagnostic state; and wherein; one diagnostic algorithm is coupled to a diagnostic module comprising a demultiplexer having an output connected to an input of the filter; another diagnostic algorithm is coupled to an aggregation module having an input connected to an output of the filter; the filter is a Kalman filter; and the filter is for smoothing noise in the belief values provided by the diagnostic algorithms; the filter uses a diagnostic state at time t−
1 to smooth out noise in the belief value provided by a diagnostic algorithm at time t;the aggregating the smoothed belief values from the filter takes into account a conflict among evidence provided by the diagnostic algorithms; and multiple failure mode hypotheses are generated by taking a pair-wise union of elements in the conflict and iteratively taking pair-wise unions of multiple failure hypotheses generated during a previous iteration. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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12. An evidence aggregation system comprising:
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a diagnostic module; a filter connected to an output of the diagnostic module; and an aggregation module connected to an output of the filter; and wherein; the diagnostic module comprises; a delay operator; and a demultiplexer connected to the delay operator and the filter; the delay operator has an input for receiving an initial diagnostic state; the delay operator has an output for providing a time delayed diagnostic state to the demultiplexer; the demultiplexer has an output for providing selected diagnostic state information to the filter; the filter is for providing belief values to the aggregation module; the aggregation module is for providing diagnostic states; and a diagnostic state is related to a failure mode of an observed mechanism.
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13. A method for fusing information comprising:
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obtaining belief values via a diagnostic module comprising a demultiplexer about one or more failure modes of a stochastic system; filtering the belief values into smoothed belief values; and aggregating the smoothed belief values into at least one diagnostic state about the system; and
whereinthe filtering for filling a gap in belief values resulting from occasional drop-outs in the belief values provided by the diagnostic algorithms; the filter is for accounting for a propagation of faults within the mechanism; and a diagnostic state for single and/or multiple failure mode hypotheses set is calculated by a weighted averaging of smoothed belief values.
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Specification