{
  "$type": "site.standard.document",
  "description": "Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive…",
  "path": "/patents/1308532",
  "publishedAt": "2022-01-13T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "F02D41/1405",
    "Tula Technology, Inc."
  ],
  "textContent": "Using machine learning for cylinder misfire detection in a dynamic firing level modulation controlled internal combustion engine is described. In a classification embodiment, cylinder misfires are differentiated from intentional skips based on a measured exhaust manifold pressure. In a regressive model embodiment, the measured exhaust manifold pressure is compared to a predicted exhaust manifold pressure generated by neural network in response to one or more inputs indicative of the operation of the vehicle. Based on the comparison, a prediction is made if a misfire has occurred or not. In yet other alternative embodiment, angular crank acceleration is used as well for misfire detection.",
  "title": "MACHINE LEARNING FOR MISFIRE DETECTION  IN A DYNAMIC FIRING LEVEL MODULATION CONTROLLED ENGINE OF A VEHICLE"
}