Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Methods and systems are provided for a lubricant detection device. In one example, a system comprises one or more sensors arranged in an oil flow path for detecting if a particle is in an oil flow. Engine operating parameters are adjusted in response to sensing the particle, wherein the engine operating parameter adjustments are different in response to only a first sensor detecting the particle or to both the first sensor and a second sensor detecting the particle.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

A calibration method and system for a lubrication oil metal debris sensor includes applying an excitation to the lubrication oil metal debris sensor to be calibrated, obtaining a second output signal from the lubrication oil metal debris sensor to be calibrated based on a test metal ball with a known diameter, and determining a sensitivity characteristic parameter of the lubrication oil metal debris sensor to be calibrated according to the diameter of the test metal ball with the known diameter, the second output signal, and a preset data processing model. Large particulate metal balls with large diameter are used as calibration particles. The calibration performed by the combination of the particulate metal ball and the data processing model helps when the signal processing circuit cannot be matched with the actual performance of the sensor and avoids an underestimation of the monitoring capability of the lubrication oil metal debris sensor.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

A system for monitoring machine fluids is provided. The system includes a fluid handling unit configured to pump the fluid and the fluid handling unit includes at least one fluid handling element. A magnetic material on the at least one fluid handling element is configured to generate a magnetic field. A sensing element located within the magnetic field is configured to measure fluctuations in the magnetic field.

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.

The invention provides a microfluidic system for monitoring the condition of lubricating oil. The microfluidic system comprises a microfluidic device with at least one microchannel (40) configured to allow a sample of lubricating oil (52a) to pass therethrough as a laminar flow. The device has at least one separating device (41) configured to selectively separate at least one component (54b) from the lubricating oil (52a) in the fluid flow. The microfluidic system also comprises a detector device (20, 22) configured to detect the presence and/or measure at least one property of the at least one component passing through the microfluidic device after separation in the microchannel (40).

Systems, methods, and computer-program products for fluid analysis and monitoring are disclosed. Embodiments include a removable and replaceable sampling system and an analytical system connected to the sampling system. A fluid may be routed through the sampling system and data may be collected from the fluid via the sampling system. The sampling system may process and transmit the data to the analytical system. The analytical system may include a command and control system to receive and store the data in a database and compare the data to existing data for the fluid in the database to identify conditions in the fluid. Fluid conditions may be determined using machine learning models that are generated from well-characterized known training data. Predicted fluid conditions may then be used to automatically implement control processes for an operating machine containing the fluid.