Abstract: A computer implemented data augmentation method comprising receiving a dataset to be processed and, upon the received dataset being unclassified into classes, performing a clustering algorithm to partition the dataset whereby clusters formed are interpreted as the signal classes. The method further includes forming a sample dataset by gathering, for each class of a plurality of classes, at least two sample signals then applying a discrete Fourier transform (DFT) to each sample signal of the sample dataset. The method includes computing frequency parameters of each sample signal to determine, based on a spectral coherence threshold, frequency bands: relevant bands that characterizes a class. The method further includes injecting random noise in a phase spectrum of the non-relevant frequency bands of each sample signal of the sample dataset, to generate a set of augmented sample signals, and applying an inverse DFT, in each of the generated augmented sample signals.

Abstract: A computer implemented pre-processing method for facilitating machine learning signal classes separability by evaluating candidate time-frequency transformations to be applied to a set of input signals to generate less redundant features. The computer implemented pre-processing method includes obtaining M candidates of time-frequency transformation rules for the set of input signals, for the M candidates, employing the obtained transformations to calculate time-frequency matrices for at least one signal of each class within the set of input signals and assessing class separability in feature space through the minimization of a multi-parameter objective function that considers the value of the overlapping between the areas of intra-class and the inter-class distributions of distances between the time-frequency matrices. A system and a non-transitory computer readable storage medium for performing the method for evaluating candidate time-frequency transformations and data-augmentation method effects.

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.

Abstract: A monitoring system includes an analytical engine system coupled to a plurality of sensors of an engine system. The analytical engine system is configured to determine a model probability distribution based on model data, determine a distance threshold value of the model probability distribution based at least in part on a threshold percentage, determine a window probability distribution based on window data sampled from the engine system, determine a fraction of the window probability distribution that is greater than the distance threshold value, and generate a lubricant alert signal when the fraction is greater than a temperature anomaly threshold. The model data includes model temperature data and model load data. The window data includes window temperature data and window load data that is based at least in part on feedback from the plurality of sensors during operation of the engine system.