A system monitors operation of a component in a hydraulic fracturing fleet. A sensor exposed to an external environment of the component is configured to detect external indicia of the operation of the component. Memory stores an artificial intelligence (AI) model, the AI model being trained to monitor the operation of the component in the system. One or more processors are operatively coupled to the memory and the sensor. The one or more processors are configured to obtain data of the external indicia detected with the sensor; input the obtained data into the AI model; detect, with the AI model and based on the input data of the external indicia, one of a plurality of predetermined states corresponding to the operation of the component; and perform a predetermined function based on the detected one of the plurality of predetermined states.

A testing system for pulsating pressure measurement of the pressure of a fluid in dynamic pressure service in a pipe system is disclosed. For example, the testing system can include an accelerometer mounted to an exterior of the pipe system. An analyzer can be provided for sampling and filtering data from the only one accelerometer. In addition, a computer can be provided for running an algorithm to convert the data from the only one accelerometer to data regarding the pressure of the fluid in the pipe system.

A gas compression device (10) is provided with a compressor (16) and a recovery section (20). The recovery section (20) is provided with a recovery passage (22), an auxiliary compressor (24) which is provided in the recovery passage (22), and a recovery tank (26) which is provided in the recovery passage (22). When gas leaks from the main compressor (16), the auxiliary compressor (24) is driven and the leaked gas is compressed. The leaked gas, the pressure of which has been increased by the auxiliary compressor (24), flows into the recovery tank (26) and is stored therein. The gas within the recovery tank (26) is fed to a suction passage (14).

A dynamic compressor control is provided. The dynamic compressor control includes sensors to sense operating parameters of a compressor and a compressor analytic software package. The compressor analytic software package uses the sensed operating parameters of the compressor to generate key performance indicators. The key performance indicators are used to calculate process variables for the compressor. The dynamic compressor control uses the sensed operating parameters and the process variables calculated from the key performance indicators to provide operating alarms and/or shutdowns.

A pump system includes a cylinder, a piston disposed within the cylinder to define a first chamber portion and a second chamber portion within the cylinder, and a conduit system fluidly coupled to the second chamber portion. The piston is configured to move in a first direction within the cylinder to draw a working fluid into the first chamber portion, and the piston is configured to move in a second direction within the cylinder to pressurize the working fluid. The conduit system is configured to receive fluid discharged from the second chamber portion due to movement of the piston in the first direction within the cylinder, and the conduit system includes a sensor configured to determine a parameter of the fluid flowing through the conduit system, the parameter being indicative of leakage of the working fluid from the first chamber portion to the second chamber portion.

A system monitors operation of a component in a hydraulic fracturing fleet. A sensor exposed to an external environment of the component is configured to detect external indicia of the operation of the component. Memory stores an artificial intelligence (AI) model, the AI model being trained to monitor the operation of the component in the system. One or more processors are operatively coupled to the memory and the sensor. The one or more processors are configured to obtain data of the external indicia detected with the sensor; input the obtained data into the AI model; detect, with the AI model and based on the input data of the external indicia, one of a plurality of predetermined states corresponding to the operation of the component; and perform a predetermined function based on the detected one of the plurality of predetermined states.

A system monitors operation of a component in a hydraulic fracturing fleet. A sensor exposed to an external environment of the component is configured to detect external indicia of the operation of the component. Memory stores an artificial intelligence (AI) model, the AI model being trained to monitor the operation of the component in the system. One or more processors are operatively coupled to the memory and the sensor. The one or more processors are configured to obtain data of the external indicia detected with the sensor; input the obtained data into the AI model; detect, with the AI model and based on the input data of the external indicia, one of a plurality of predetermined states corresponding to the operation of the component; and perform a predetermined function based on the detected one of the plurality of predetermined states.

A liquid delivery pump delivers a liquid with high flow rate accuracy in consideration of solvent expansion, pressure dependent leakage of the solvent, and flow rate dependent leakage of the solvent. A liquid delivery pump includes first and second plunger pumps connected together, having respective first and second plungers. A pressure sensor measures a liquid delivery pressure (P2) from the second plunger pump, and a controller controls the driving of the first and second plungers. The controller obtains a moving speed (v1) of the first plunger and a moving speed (v2) of the second plunger using a parameter (C1, C0) depending on the liquid delivery pressures (P1, P2), the liquid delivery pressure, and a target flow rate of the liquid. The parameter (C1, C0) is a parameter of an expression representing a relationship between the liquid delivery pressure (P2) and a flow rate (Q0) of the liquid.