Methods and systems are disclosed for operating an engine that includes a knock control system that may determine contributions of individual noise sources to an engine background noise level. The contributions of the individual noise sources may be the basis for establishing the presence or absence of knock in one or more engine cylinders.

A method for sensing damage to a bearing of an engine using a vibration signal may sense the damage to the bearing even without a sensor for directly sensing the damage to the bearing using a frequency signal input to a knocking sensor from the engine for each traveling state (acceleration traveling, cruise traveling, deceleration traveling) of a vehicle.

A motor noise detecting device according to an embodiment of the present disclosure includes a signal sensing part for sensing an acoustic signal generated from an object to be tested, a data acquisition part for receiving the acoustic signal sensed by the signal sensing part and converting it into an acoustic digital signal, and a data analysis part for receiving and analyzing the acoustic digital signal to perform a detection on whether the object to be tested is abnormal. In addition, the signal sensing part includes an AE (Acoustic Emission) sensor for sensing an elastic wave included in the acoustic signal, and the data analysis part generates result data of analyzing the acoustic digital signal, analyzes the generated result data through a pre-learned model, and detects whether the object to be tested is abnormal.

Embodiments are disclosed for enhancing engine sound. An example method for a vehicle comprises acquiring a signal including harmonic content generated by an engine of the vehicle, upmixing the signal into a plurality of channels for a given number of engine orders, adjusting an order filter for each engine order of the given number of engine orders based on operating conditions of the engine, filtering each channel of the plurality of channels with the corresponding order filter, mixing the filtered channels into a mono output, and outputting the mono output to at least one speaker in the vehicle. The mono output is delayed based on a position of the at least one speaker such that an occupant of the vehicle perceives the mono output as originating from the engine.

Embodiments are disclosed for enhancing engine sound. An example method for a vehicle comprises acquiring a signal including harmonic content generated by an engine of the vehicle, upmixing the signal into a plurality of channels for a given number of engine orders, adjusting an order filter for each engine order of the given number of engine orders based on operating conditions of the engine, filtering each channel of the plurality of channels with the corresponding order filter, mixing the filtered channels into a mono output, and outputting the mono output to at least one speaker in the vehicle. The mono output is delayed based on a position of the at least one speaker such that an occupant of the vehicle perceives the mono output as originating from the engine.

The present disclosure relates to a computer-implemented method of, a data processing system for, and a computer program product for indicating machine failures as well as to a corresponding machine and a computer-implemented method of training a neural network for indicating machine failures. At least one input signal based on at least one physical quantity of at least one machine part is transformed into at least one feature. A neural network predicts a class and/or a severity of at least one machine failure based on the at least one feature.

An engine oil dipstick for monitoring an internal combustion engine comprises a processor and a sensor module. The sensor module is configured to sense a characteristic of the internal combustion engine and to output data representative of the sensed characteristic to the processor. The engine oil dipstick is configured to provide a housing for the processor. The processor is configured to determine a value representative of the firing frequency of the internal combustion engine based on the output data.

An engine oil dipstick for monitoring an internal combustion engine comprises a processor and a sensor module. The sensor module is configured to sense a characteristic of the internal combustion engine and to output data representative of the sensed characteristic to the processor. The engine oil dipstick is configured to provide a housing for the processor. The processor is configured to determine a value representative of the firing frequency of the internal combustion engine based on the output data.

Systems, methods, and other embodiments associated with autonomous discrimination of operation vibration signals are described herein. In one embodiment, a method includes partitioning a frequency spectrum of output into a plurality of discrete bins, wherein the output is collected from vibration sensors monitoring a reference device; generating a representative time series signal for each bin while the device is operated in a deterministic stress load; generating a PSD for each bin by converting each signal from the time domain to the frequency domain; determining a maximum power spectral density value and a peak frequency value for each bin; selecting a subset of the bins that have maximum PSD values exceeding a threshold; assigning the representative time series signals from the selected subset of bins as operation vibration signals indicative of operational load on the reference device; and configuring a machine learning model based on at least the operation vibration signals.

A method includes obtaining a vibration signal acquired by an accelerometer sensor; eliminating a deterministic component of the vibration signal; obtaining, for a determined defect, a characteristic theoretical frequency of this defect and a determined maximum deviation around this theoretical frequency; computing, as a function of a cyclic frequency, an integrated cyclic coherence of the processed vibration signal; estimating an actual frequency of the defect on the basis of the integrated cyclic coherence, of the theoretical frequency of the defect and of the maximum deviation; computing a diagnostic indicator of the defect by summing M integrated cyclic coherences of the vibration signal evaluated as M cyclic frequencies respectively equal to M harmonics of the estimated actual frequency of the defect; comparing the diagnostic indicator of the defect with a predetermined threshold, and in the event of it being exceeded, detecting the defect on the bearing.