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 includes an electronic fuel injection system of an engine, the electronic fuel injection system including an electronic governor control unit for controlling various functions of the engine.

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 includes an electronic fuel injection system of an engine, the electronic fuel injection system including an electronic governor control unit for controlling various functions of the engine.

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 and method for identifying engine wear based on engine oil debris is provided. The provided system and method utilize metallic debris data provided in summary reports. For each object of metallic debris, a zap count, an initial (pre-zap) impedance and an impedance change (post zap) are processed with configurable reference variables. Alerts (suggesting potential engine wear) are generated responsive to the impedance values of the objects of metallic debris and the reference variables.

An apparatus for diagnosing lubricant degradation includes a soot amount estimator configured to estimate an amount of soot formed in a predetermined period in a combustion chamber of an engine, and diagnoses the degradation of a lubricant based on a cumulative value of the amount of soot estimated. The soot amount estimator calculates the amount of soot based on a speed of the engine and a load parameter related to a load of the engine, and corrects, based on a temperature parameter correlated to the temperature of the combustion chamber, the amount of soot calculated into an increased value if the temperature parameter is lower than a predetermined first temperature.

Mobile devices emit signals in Bluetooth, Wi-Fi and cellular (i.e. 2G, 3G, 4G, Edge, H+, etc.). These signals attempt to connect to paired devices, hotspots, cell towers, or other suitable wireless connection points to greater networks. In order to connect to connect to hotspots mobile devices send out identifying data to establish a connection. Hotspots may unobtrusively collect the identification data of the mobile device, and then reject the connection request. The hotspots collect data in real time on the mobile device, and by association, the human carrying the mobile device without having alerted or impeded the human carrier. The system uses analytical software to determine, for example, an approaching unique ID user's presence, history, frequency of visits, duration of presence and so on.

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.