Methods and systems for data smoothing stress data
First Claim
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1. A data smoothing method for reducing raw stress data, comprising:
- receiving a data set at a processor,wherein said data set comprises raw stress data from at least one sensor;
transforming the data set responsive to a transform function using said processor; and
smoothing the raw stress data to reduce the quantity of the data set while retaining significant stress events for yielding a reduced data set,wherein said smoothing comprises dynamic binning is applied to the raw stress data,wherein the dynamic binning is controlled by a minimum and maximum allowable bin size, andwherein the dynamic binning comprises fewer bins in a low stress region relative to a high stress region.
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Abstract
Methods and systems for data smoothing raw data such as stress data. The present invention may utilize various techniques to smooth data such as dropping data using a stress threshold, dropping data by resample (or decimation), and smoothing by dynamic binning. Additionally, the present invention may utilize a transform function to estimate stress from a stress sensor to areas of peak stress. In an exemplary application, the present invention can be utilized to reduce stress data collected by a plurality of embedded stress sensors in a rocket motor for purposes of computing service life of the motor.
11 Citations
15 Claims
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1. A data smoothing method for reducing raw stress data, comprising:
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receiving a data set at a processor, wherein said data set comprises raw stress data from at least one sensor; transforming the data set responsive to a transform function using said processor; and smoothing the raw stress data to reduce the quantity of the data set while retaining significant stress events for yielding a reduced data set, wherein said smoothing comprises dynamic binning is applied to the raw stress data, wherein the dynamic binning is controlled by a minimum and maximum allowable bin size, and wherein the dynamic binning comprises fewer bins in a low stress region relative to a high stress region. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A method of computing a service life prediction of a device being based on measured stress, comprising:
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receiving stress measurements from at least one embedded stress sensors yielding a data set comprising raw stress data; smoothing the raw stress data for reducing a quantity of the data set while retaining significant stress events using a processor for producing a reduced data set; calculating a service life prediction from the reduced data set; and removing the device from service responsive to the service life prediction, wherein smoothing comprises dynamic binning is applied to the raw stress data wherein the dynamic binning is controlled by a dynamic stress bin trigger comprising a change in stress that prompts a new bin and a minimum and maximum allowable bin size, wherein the dynamic binning is controlled by a minimum and maximum allowable bin size, and wherein the dynamic binning comprises fewer bins in a low stress region relative to a high stress region. - View Dependent Claims (11, 12, 13)
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14. A data smoothing system, comprising:
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a structure for data input and output; at least one processor being coupled to a memory and said structure for said data input and output, wherein said at least one processor is configured to receive a data set, which comprises raw stress data, transform the data set responsive to a transform function, smooth the raw stress data to reduce the quantity of the data set while retaining significant stress events, and calculate a service life prediction from the reduced data set, and a plurality of embedded sensors in a rocket motor, wherein the plurality of embedded sensors are configured to measure the raw stress data wherein said smooth comprises dynamic binning is applied to the raw stress data wherein the dynamic binning is controlled by a dynamic stress bin trigger where a change in stress prompts a new bin, wherein the dynamic binning is controlled by a minimum and maximum allowable bin size, wherein the dynamic binning comprises fewer bins in a low stress region relative to a high stress region, wherein the raw stress data is re-sampled responsive to a pre-determined frequency in the low stress region; and wherein the raw stress data is dropped responsive to a stress threshold in the low stress region. - View Dependent Claims (15)
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Specification