{
  "$type": "site.standard.document",
  "description": "Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a…",
  "path": "/patents/1158485",
  "publishedAt": "2016-03-31T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "G07C5/006",
    "International Business Machines Corporation"
  ],
  "textContent": "Methods, systems, and computer program products for generating estimates of failure risk for a vehicular component in situations of high-dimensional and low sample size data are provided herein. A method includes splitting a first input time series comprising multiple data points derived from a vehicular component across a fleet of multiple vehicles into multiple sub-time series; generating a first failure status predicting function of a first selected sub-time series; deleting, from the first input time series, the portion of the data points that corresponds to the first selected sub-time series; repeating the preceding two steps for a second selected sub-time series; generating a second failure status predicting function of each selected sub-time series; applying each second failure status predicting function to a second input time series to calculate prediction of failure values; and identifying the largest prediction of failure value as an estimate of failure risk for the vehicular component.",
  "title": "Generating Estimates of Failure Risk for a Vehicular Component in Situations of High-Dimensional and Low Sample Size Data"
}