The disclosure is directed to a method comprising the steps: carrying out multiple measurements on a mechanical object, the measurements each differing by one or more parameters influencing the measurement; determining a spectrogram on the basis of the measurement data of the measurements and depending on a predefined parameter of the mechanical object; determining one or more excitations of the mechanical object; reproducing the excitations in the spectrogram.

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 sensor system includes one or more sensors that sense vibrations of a vehicle and one or more processors that can determine a speed of the vehicle and determine whether the vibrations occurring at one or more frequencies of interest (that are based on the speed of the vehicle) indicate damage to a propulsion system of the vehicle. The one or more processors optionally may determine a hunting frequency of a wheel and axle set and/or a lateral acceleration of the wheel and axle set from the vibrations. The one or more processors can determine a conicity of a wheel in the wheel and axle set based on the hunting frequency and/or the lateral acceleration that is determined.

A sensor system includes one or more sensors that sense vibrations of a vehicle and one or more processors that can determine a speed of the vehicle and determine whether the vibrations occurring at one or more frequencies of interest (that are based on the speed of the vehicle) indicate damage to a propulsion system of the vehicle. The one or more processors optionally may determine a hunting frequency of a wheel and axle set and/or a lateral acceleration of the wheel and axle set from the vibrations. The one or more processors can determine a conicity of a wheel in the wheel and axle set based on the hunting frequency and/or the lateral acceleration that is determined.

A sampling device receives, from a transducer computing device located within a predefined proximity to an equipment in an operating environment, a vibration sample from the operating environment and increments a retrain counter. In response to determining that the incremented retrain counter does not meet or exceed a retrain threshold, the sampling device predicts, using a model, an anomalous or non-anomalous designation for the vibration sample and a cluster assignment, to a particular cluster of a set of clusters, for the vibration sample when the model predicts the non-anomalous designation for the vibration sample. The sampling device receives a subsequent vibration sample and further increments the retrain counter. In response to determining that the further incremented retrain counter exceeds a retrain threshold, the sampling device receives a subsequent set of vibration samples and retrains, using the subsequent vibration sample and the subsequent set of vibration samples, the model.

A vibration management device for managing the vibrations of a motor vehicle, the device including an electronic controller> The electronic controller is able to be fixed to a vibrating wall receiving vibrations from a component or set of components of the vehicle, by a fixing device including a vibration sensor, generating a vibration signal. The electronic controller includes a signal processor for processing the signal originating from the sensor, and the electronic controller is connected to a device for managing the vibrations.

A vibration management device for managing the vibrations of a motor vehicle, the device including an electronic controller> The electronic controller is able to be fixed to a vibrating wall receiving vibrations from a component or set of components of the vehicle, by a fixing device including a vibration sensor, generating a vibration signal. The electronic controller includes a signal processor for processing the signal originating from the sensor, and the electronic controller is connected to a device for managing the vibrations.

Apparatus and method for monitoring operational performance of an engine, the apparatus comprising a sensor (10) for detecting torsional strain of a shaft (21) of an engine, and the apparatus further comprising a data processor arranged to process signals received from the strain sensor, and the data processor further arranged to compare one or more operational characteristics of the measured torsional strain data with idealised operational data so as to provide a measure of operational performance indicator of the engine.

Apparatus and method for monitoring operational performance of an engine, the apparatus comprising a sensor (10) for detecting torsional strain of a shaft (21) of an engine, and the apparatus further comprising a data processor arranged to process signals received from the strain sensor, and the data processor further arranged to compare one or more operational characteristics of the measured torsional strain data with idealised operational data so as to provide a measure of operational performance indicator of the engine.

Techniques for using a trained machine learning (ML) model to detect presence of vehicle defects from audio acquired at least in part during operation of an engine of a vehicle. The techniques include using at least one computer hardware processor to perform: obtaining, via at least one communication network, a first audio recording that was acquired, using at least one acoustic sensor, at least in part during operation of the engine; processing the first audio recording using the trained ML model to detect, from the first audio recording, presence of at least one vehicle defect, the processing comprising: generating an audio waveform from the first audio recording, generating a two-dimensional (2D) representation of the audio waveform, and processing the audio waveform and the 2D representation of the audio waveform using the trained ML model to obtain output indicative of presence or absence of the at least one vehicle defect.