Methods and systems for monitoring lubricant oil condition using photoacoustic modelling

A system comprising:

• a memory storing instructions;
• one or more input/output (I/O) interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more I/O interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive photoacoustic signals from a plurality of lubricant oil samples, and a classification label for each of the plurality of lubricant oil samples;
• simulate photoacoustic signal from each lubricant oil sample, to obtain a plurality of simulated photoacoustic signals from the plurality of lubricant oil samples, using a photoacoustic simulation model, wherein the one or more hardware processors are configured by the instructions to simulate the photoacoustic signal from each lubricant oil sample to obtain corresponding simulated photoacoustic signal using the photoacoustic simulation model by:
• determining one or more optical parameters, a contaminant concentration, a temperature, and one or more acoustic parameters, for the corresponding lubricant oil sample, wherein the one or more optical parameters of the lubricant oil sample comprises an optical absorption coefficient μa, a scattering coefficient μs and a scattering anisotropy g, wherein the one or more optical parameters of the lubricant oil sample are determined based on a predefined optical wavelength of the photoacoustic signal of the corresponding lubricant sample, the contaminant concentration of the lubricant oil sample is determined in the form of an effective absorption coefficient () and the one or more acoustic parameters comprises a speed of sound, a density of the lubricant oil sample and a Grüneisen parameter;
• determining a fluence for the corresponding lubricant oil sample, based on the one or more optical parameters and the contaminant concentration, using a photonic model;
• generating an initial acoustic pressure signal for the lubricant oil sample, using a finite element technique based on the one or more acoustic parameters, the contaminant concentration, the temperature, and the fluence;
• propagating the initial acoustic pressure signal to obtain a propagated acoustic pressure signal for the corresponding lubricant oil sample, using the finite element technique;
• and
• obtaining the simulated photoacoustic signal for the corresponding lubricant oil sample, by correlating the initial acoustic pressure signal and the propagated acoustic pressure signal;
• determine one or more statistical features, for each lubricant oil sample, from the corresponding simulated photoacoustic signal, using a signal processing technique, wherein the one or more hardware processors are configured by the instructions to determine the one or more statistical features for each lubricant oil sample from a time-frequency signal of the corresponding simulated photoacoustic signal;
• and
• train a machine learning (ML) model with (i) the one or more statistical features for each lubricant oil sample of the plurality of lubricant oil samples and (ii) the classification label for each of the plurality of lubricant oil samples, to obtain a trained ML model;
• receive a test photoacoustic signal from a test lubricant oil sample for which lubricant oil condition to be monitored;
• determine one or more statistical features of the test lubricant oil sample, from the test photoacoustic signal, using signal processing technique;
• and
• pass the one or more statistical features of the test lubricant oil sample, to the trained ML model, to obtain the classification label for the lubricant oil sample, wherein the classification label provides the lubricant oil condition of the test lubricant oil sample.

One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:

• receiving, photoacoustic signals from a plurality of lubricant oil samples, and a classification label for each of the plurality of lubricant oil samples, wherein the classification label for each lubricant oil sample defines a contamination type of corresponding lubricant oil sample;
• simulating, the photoacoustic signal from each lubricant oil sample, to obtain a plurality of simulated photoacoustic signals from the plurality of lubricant oil samples, using a photoacoustic simulation model, wherein the photoacoustic simulation model makes use of both one or more optical parameters and one or more acoustic parameters of the photoacoustic signals of the corresponding lubricant oil sample for simulation to obtain the corresponding simulated photoacoustic signals, wherein simulating each lubricant oil sample to obtain corresponding simulated photoacoustic signal using the photoacoustic simulation model, comprises:
• determining the one or more optical parameters, a contaminant concentration, a temperature, and the one or more acoustic parameters, for the corresponding lubricant oil sample, wherein the one or more optical parameters of the lubricant oil sample comprises an optical absorption coefficient μa, a scattering coefficient μs and a scattering anisotropy g, wherein the one or more optical parameters of the lubricant oil sample are determined based on a predefined optical wavelength of the photoacoustic signal of the corresponding lubricant sample, the contaminant concentration of the lubricant oil sample is determined in the form of an effective absorption coefficient () and the one or more acoustic parameters comprises a speed of sound, a density of the lubricant oil sample and a Grüneisen parameter;
• determining a fluence for the corresponding lubricant oil sample, based on the one or more optical parameters and the contaminant concentration, using a photonic model;
• generating an initial acoustic pressure signal for the lubricant oil sample, using a finite element technique based on the one or more acoustic parameters, the contaminant concentration, the temperature, and the fluence;
• propagating the initial acoustic pressure signal to obtain a propagated acoustic pressure signal for the corresponding lubricant oil sample, using the finite element technique;
• and
• obtaining the simulated photoacoustic signal for the corresponding lubricant oil sample, by correlating the initial acoustic pressure signal and the propagated acoustic pressure signal;
• determining, one or more statistical features, for each lubricant oil sample, from the corresponding simulated photoacoustic signal, using a signal processing technique, wherein the one or more statistical features for each lubricant oil sample are determined from a time-frequency signal of the corresponding simulated photoacoustic signal;
• and
• training, a machine learning (ML) model with (i) the one or more statistical features for each lubricant oil sample of the plurality of lubricant oil samples and (ii) the classification label for each of the plurality of lubricant oil samples, to obtain a trained ML model;
• receiving, via the one or more hardware processors, a test photoacoustic signal from a test lubricant oil sample for which lubricant oil condition to be monitored;
• determining, via the one or more hardware processors, one or more statistical features of the test lubricant oil sample, from the test photoacoustic signal, using the signal processing technique;
• and
• passing, via the one or more hardware processors, the one or more statistical features of the test lubricant oil sample, to the trained ML model, to obtain the classification label for the lubricant oil sample, wherein the classification label provides the lubricant oil condition of the test lubricant oil sample.