MISFIRE DETECTION DEVICE FOR INTERNAL COMBUSTION ENGINE, MISFIRE DETECTION SYSTEM FOR INTERNAL COMBUSTION ENGINE, DATA ANALYSIS DEVICE, AND CONTROLLER FOR INTERNAL COMBUSTION ENGINE

A misfire detection device (70) for an internal combustion engine (10) is provided. A mapping takes time series data of instantaneous speed parameters (T30) as inputs. Each instantaneous speed parameter corresponds to one of a plurality of successive second intervals in a first interval. The instant...

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Bibliographische Detailangaben
Hauptverfasser:	KATAYAMA, Akihiro, MUTO, Harufumi, MURAI, Tatsunobu, TAKAMOTO, Satoshi
Format:	Patent
Sprache:	eng ; fre ; ger
Schlagworte:	BLASTING CALCULATING COMBUSTION ENGINES COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONTROLLING COMBUSTION ENGINES COUNTING HEATING HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS IGNITION, OTHER THAN COMPRESSION IGNITION, FORINTERNAL-COMBUSTION ENGINES LIGHTING MEASURING MECHANICAL ENGINEERING PHYSICS TESTING TESTING OF IGNITION TIMING IN COMPRESSION-IGNITION ENGINES TESTING STATIC OR DYNAMIC BALANCE OF MACHINES ORSTRUCTURES TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR WEAPONS
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Beschreibung
Zusammenfassung:	A misfire detection device (70) for an internal combustion engine (10) is provided. A mapping takes time series data of instantaneous speed parameters (T30) as inputs. Each instantaneous speed parameter corresponds to one of a plurality of successive second intervals in a first interval. The instantaneous speed parameters (T30) correspond to the rotational speed (NE) of the crankshaft (24). The first interval is a rotational angular interval of the crankshaft (24) in which compression top dead center occurs. The second interval is smaller than an interval between compression top dead center positions. The mapping outputs a probability (y(i), z(i)) that a misfire has occurred in at least one cylinder (#1 to #4) that reaches compression top dead center in the first interval. The mapping data (76a) defining the mapping has been learned by machine learning (S50-S60).