A specific sound having a higher frequency than an operation sound of an engine is generated in synchronization with timing of a top dead center of a specific cylinder of the engine. Thus, timing of a noise of the engine is estimated by using, for example, a deviation in time between timing of generation of a specific sound and timing at which a noise occurs in the engine, based on a sound collected from around the engine without directly using a signal of a crank angle of the engine.

A specific sound having a higher frequency than an operation sound of an engine is generated in synchronization with timing of a top dead center of a specific cylinder of the engine. Thus, timing of a noise of the engine is estimated by using, for example, a deviation in time between timing of generation of a specific sound and timing at which a noise occurs in the engine, based on a sound collected from around the engine without directly using a signal of a crank angle of the engine.

A load cell for determining a radial force acting on a crankshaft having a receiving sleeve for receiving a bearing ring and a fastening ring for attaching the load cell in a transmission housing. Axial support areas are provided on the fastening ring for axially supporting the outer ring of the first bearing. Moreover, measuring regions for receiving radial forces of the receiving sleeve are provided which connect the receiving sleeve with the fastening ring. Strain sensors are attached to at least two of the measuring regions.

A load cell for determining a radial force acting on a crankshaft having a receiving sleeve for receiving a bearing ring and a fastening ring for attaching the load cell in a transmission housing. Axial support areas are provided on the fastening ring for axially supporting the outer ring of the first bearing. Moreover, measuring regions for receiving radial forces of the receiving sleeve are provided which connect the receiving sleeve with the fastening ring. Strain sensors are attached to at least two of the measuring regions.

A method for controlling production of microticks from a crank sensor signal continuously generated by engine RPM may include: dividing the crank sensor signal within a present period of the crank sensor signal to produce the microticks having a first period through the controller; monitoring the number of microticks produced through the controller; and controlling production of the microticks having the first period by using a direct memory access (DMA) based on the monitoring result through the controller.

A method for controlling production of microticks from a crank sensor signal continuously generated by engine RPM may include: dividing the crank sensor signal within a present period of the crank sensor signal to produce the microticks having a first period through the controller; monitoring the number of microticks produced through the controller; and controlling production of the microticks having the first period by using a direct memory access (DMA) based on the monitoring result through the controller.

An Engine Control Unit (ECU) for adapting to irregularities in a trigger wheel includes a memory element that stores a table with data of dimensions of the trigger wheel. The ECU is configured to (i) use a position sensor to detect a tooth and a corresponding tooth number, and (ii) set a time range for detection of a subsequent tooth with reference to the data in the memory element. The ECU is further configured to (iii) prevent errors due to irregularities in the trigger wheel in order to reinforce the ECU or an Engine Position Management System (EPMS) by adapting to a profile of each tooth of the trigger wheel, and in order to avoid an error in a plausibility check of the position sensor due to irregularities in the trigger wheel.

An Engine Control Unit (ECU) for adapting to irregularities in a trigger wheel includes a memory element that stores a table with data of dimensions of the trigger wheel. The ECU is configured to (i) use a position sensor to detect a tooth and a corresponding tooth number, and (ii) set a time range for detection of a subsequent tooth with reference to the data in the memory element. The ECU is further configured to (iii) prevent errors due to irregularities in the trigger wheel in order to reinforce the ECU or an Engine Position Management System (EPMS) by adapting to a profile of each tooth of the trigger wheel, and in order to avoid an error in a plausibility check of the position sensor due to irregularities in the trigger wheel.

A rapid compression machine (RCM) employs an electrical drive to move a piston disposed within a chamber housing. The electrical drive converts electrical power into linear motion of the piston, for example, to compress contents in a reaction chamber defined by the chamber housing and the piston. The temperature and pressure changes induced by the compression can cause reaction of contents within the chamber, for example, autoignition of the contents. The RCM can thus be used to study chemical kinetics. In some embodiments, the electrical drive can also rapidly move the piston in reverse to expand a volume of the reaction chamber, for example, to quench the compression-induced reaction therein. In such embodiments, the RCM may be considered a rapid compression-expansion machine (RCEM) and can be used for speciation studies.

A misfire detection device for an internal combustion engine is provided. A mapping takes time series data of instantaneous speed parameters as inputs. Each instantaneous speed parameter corresponds to one of a plurality of successive second intervals in a first interval. The instantaneous speed parameters correspond to the rotational speed of the crankshaft. The first interval is a rotational angular interval of the crankshaft 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 that a misfire has occurred in at least one cylinder that reaches compression top dead center in the first interval. The mapping data defining the mapping has been learned by machine learning.