Engine Cylinder Pressure Reconstruction using a Crank-Shaft-Dynamics Neural Network

No description supplie

Time-Frequency Analysis of Single-Point Engine-Block Vibration Measurements for Multiple Excitation-Event Identification

The short-term-Fourier-transform (STFT) is used to identify different sources of IC engine-block vibration from single-point acceleration measurements taken with a commercial knock sensor. Interest is focused on using the STFT to distinguish normal combustion from other sources of excitation including valve impact, injector pulses, and abnormal combustion, such as knocking. Positive identification of these other events using a single method can be useful for pre-processing of measured knock-sensor data for neural-network-based reconstruction of cylinder pressure. It can also be useful separately as part of a fast knock detection system. A series of experiments is discussed to create the data to isolate these different events on a 3-cylinder gasoline engine. In each case, the measured data is processed using the STFT to attempt to isolate the occurrence of particular events in the time domain. Four classes of experiments are undertaken: (i) an un-fired (motored) engine, driven by a dynamometer, with spark plugs fitted, and then removed, to isolate valve impact; (ii) a fired engine running under idle conditions, to contrast no-load combustion with no combustion; (iii) a part-loaded engine running normally, and then running with one injector switched-off, and (iv) a fully-loaded engine running normally, and then running with knock-control switched-off. The paper shows that a single Time-frequency analysis method, applied to knock sensor data in the form of an appropriately-tuned STFT, can effectively identify the occurrence of these events in the time domain if responses are adequately separated and strong enough

A Recurrent Neural Network for Vibration-based Engine Cylinder Pressure Reconstruction.

No description supplie

A Model for Simulating the Instantaneous Crank Kinematics and Total Mechanical Losses in a Multi-Cylinder In-Line Engine

A two-degree-of-freedom dynamic model is constructed to simulate the instantaneous crank kinematics and total mechanical losses arising in a multicylinder gasoline engine coupled to a dynamometer. The simulation model is driven using specified cylinder gas pressures, and loaded by nominal brake torque and total friction losses. Existing semi-empirical torque loss models (based on calibrated single-cylinder diesel engine data) are used to account for the instantaneous friction losses in the piston-ring assembly, in bearings, and in auxiliaries. The model is specialized to the simulation of crank kinematics and matched brake torque for a three-cylinder in-line direct injection spark ignition (DISI) engine, without a gearbox. This allows the total friction loss to be separated from the brake torque for an engine not fitted with the very large number of sensors otherwise needed to calibrate analytical friction models. An equivalent simulation model is also constructed using GT-Crank, which excludes explicit reference to friction. In using both models to simulate steady state operation at a specified mean engine speed, the output torque is matched by iteration. The GT-Crank model necessarily compensates for internal losses by exaggerating the total output torque. Both simulation models are compared with measured crank kinematics and brake torque obtained from a dynamometer-loaded I3 DISI engine. The paper shows that by comparing the matched output torque from simulation with the measured output torque from the engine, the proposed model gives a very good high-speed prediction of the total mechanical losses. At low speed, the instantaneous model is still not accurate. It is also shown, however, that apart from the no-load condition, use of an average torque to compensate for friction (as in GT-Crank) is wholly acceptable for simulating instantaneous crank kinematics. This is the first reported instance of a simulation model (which includes the partic

Engine Misfire Detection with Pervasive Mobile Audio

We address the problem of detecting whether an engine is misfiring by using machine learning techniques on transformed audio data collected from a smartphone. We recorded audio samples in an uncontrolled environment and extracted Fourier, Wavelet and Mel-frequency Cepstrum features from normal and abnormal engines. We then implemented Fisher Score and Relief Score based variable ranking to obtain an informative reduced feature set for training and testing classification algorithms. Using this feature set, we were able to obtain a model accuracy of over 99 % using a linear SVM applied to outsample data. This application of machine learning to vehicle subsystem monitoring simplifies traditional engine diagnostics, aiding vehicle owners in the maintenance process and opening up new avenues for pervasive mobile sensing and automotive diagnostics. Keywords: Pervasive sensing, Mobile phones, Sound classification, Audio processing, Fault detection, Machine learnin