The disclosure relates to a method for determining and/or monitoring the state of a transformer oil, comprising the steps of a) performing an acoustic spectroscopy of the transformer oil, multiple ultrasonic emission signals of different frequencies and/or amplitudes being emitted into the transformer oil and corresponding reflected and/or transmitted ultrasonic reception signals of different frequencies and/or amplitudes being received after having passed through the transformer oil; and b) comparing the ultrasonic emission signals with the corresponding ultrasonic reception signals, an n-dimensional function characteristic of the transformer oil being ascertained; and c) matching the ascertained characteristic n-dimensional function from step b) with a reference function of corresponding dimension known for transformer oils, a reference transformer oil being determined; and d) registering a first value of at least one characteristic physical property of the transformer oil; and e) comparing the first value with a corresponding value of the reference transformer oil; and f) ascertaining the state of the transformer oil based on the comparison performed in step e). Furthermore, the disclosure relates to a device (100, 200) for determining and/or monitoring the state of a transformer oil.

A predictive diagnostics system for monitoring mechanical seals. The system autonomously detects a loss of lubrication within a sliding seal interface of a mechanical seal, the system including a loss of lubrication failure mode logic module configured to monitor data sensed by one or more sensors and diagnose conditions relating to a loss of lubrication within the sliding seal interface, and a plurality of other failure mode logic modules configured to monitor data sensed by the one or more sensors and diagnose conditions relating to specific types of mechanical failures known to occur in mechanical seal systems, the loss of lubrication failure mode logic module configured to determine which of the plurality of other failure mode logic modules are activated during the diagnosis of conditions related to a loss of lubrication within the sliding seal interface.

The present application describes a method to reduce noise and improve data quality when analyzing hydrocarbon compositions with a Quartz Crystal Microbalance (QCM). In some approaches, the methods described in this disclosure remove at least a portion of volatile components from the hydrocarbon composition to be tested with the QCM.

A predictive diagnostics system for monitoring mechanical seals. The system autonomously detects a loss of lubrication within a sliding seal interface of a mechanical seal, the system including a loss of lubrication failure mode logic module configured to monitor data sensed by one or more sensors and diagnose conditions relating to a loss of lubrication within the sliding seal interface, and a plurality of other failure mode logic modules configured to monitor data sensed by the one or more sensors and diagnose conditions relating to specific types of mechanical failures known to occur in mechanical seal systems, the loss of lubrication failure mode logic module configured to determine which of the plurality of other failure mode logic modules are activated during the diagnosis of conditions related to a loss of lubrication within the sliding seal interface.

This disclosure relates generally to a method and system for determining viscosity information of fluids. The present disclosure utilizes an intensity modulated continuous wave (CW) laser diode-based PA sensing method to obtain a continuous wave photoacoustic (CWPA) spectra. Through this CWPA spectra, a full width half maximum (FWHM) and a spectral area is determined to obtain the information about the viscosity of fluids. Although, the CWPA based sensing technique is used for distinguishing different types of abnormalities in tissues, so far it is not used for measuring viscosity which is an important thermo-physical property. The viscosity information of the fluids from the normalized Gaussian fitted CWPA spectra is based on a viscosity feature computed from a FWHM, and a spectral area. The viscosity feature improves the good of fit parameter (R.sup.2) significantly to 0.98 as compared to the traditional only FWHM based viscosity determination for which R.sup.2 is 0.91.

A fluid analysis system for characterizing a multiphase fluid includes a first set of acoustic probes disposed at a first angular position about a central axis of the fluid analysis system and oriented to direct first sound waves along a first direction that is parallel to the central axis, a second set of acoustic probes disposed at a second angular position about the central axis that is opposite to the first angular position and oriented to direct second sound waves along the first direction, a third set of acoustic probes spanning the central axis and oriented to direct third sound waves along a second direction that is perpendicular to the central axis, and an analysis unit. The analysis unit is configured to determine a location of a fluid interface within the multiphase fluid based on first, second, and third parameters respectively associated with the first, second, and third sounds waves.

Methods, systems, and devices for downhole evaluation using a sensor assembly that includes a sensor plate, wherein a surface of the sensor plate forms a portion of a surface of a downhole tool. Methods include bringing the surface of the sensor plate into contact with downhole fluid; generating a guided wave that propagates in the sensor plate by activating the sensor assembly at at least one frequency configured to excite both a symmetric mode and an anti-symmetric mode; making at least one first attenuation measurement of the symmetric mode of the guided wave; making at least one second attenuation measurement of the anti-symmetric mode of the guided wave; and using the at least one first attenuation measurement and the at least one second attenuation measurement to estimate at least one parameter of interest of the fluid. Methods may include submerging the surface of the sensor plate in a downhole fluid.

A fluid analysis system for characterizing a multiphase fluid includes a first set of acoustic probes disposed at a first angular position about a central axis of the fluid analysis system and oriented to direct first sound waves along a first direction that is parallel to the central axis, a second set of acoustic probes disposed at a second angular position about the central axis that is opposite to the first angular position and oriented to direct second sound waves along the first direction, a third set of acoustic probes spanning the central axis and oriented to direct third sound waves along a second direction that is perpendicular to the central axis, and an analysis unit. The analysis unit is configured to determine a location of a fluid interface within the multiphase fluid based on first, second, and third parameters respectively associated with the first, second, and third sounds waves.

A system and method for determining fluid quality or change in quality by training machine learning algorithms using ultrasound Fast Fourier Transform (FFT) signatures. The system includes one or more ultrasonic transducers embedded inside or outside of a pipe, piping, or vessel that allows a fluid to flow past or between the ultrasound transducers. The transducer's acoustic energy creates ultrasound pressure waves and localized heat due to acoustic cavitation. The cavitation creates bubbles that collapse creating ultrasound sonic energy in the time domain. Unique sonic time-domain signatures, distinctively associated with the characteristics of the fluids, are converted to FFTs to produce unique, well-defined frequency response signatures. Machine learning algorithms are used to identify, measure, and classify the unique frequency response signatures associated with a wide range of fluids.

A predictive diagnostics system for monitoring mechanical seals. The system autonomously detects a loss of lubrication within a sliding seal interface of a mechanical seal, the system including a loss of lubrication failure mode logic module configured to monitor data sensed by one or more sensors and diagnose conditions relating to a loss of lubrication within the sliding seal interface, and a plurality of other failure mode logic modules configured to monitor data sensed by the one or more sensors and diagnose conditions relating to specific types of mechanical failures known to occur in mechanical seal systems, the loss of lubrication failure mode logic module configured to determine which of the plurality of other failure mode logic modules are activated during the diagnosis of conditions related to a loss of lubrication within the sliding seal interface.