Caterpillar Inc.

Caterpillar Trimble Control Technologies LLC

Yanchai Zhang

Xuefei Hu

Zhijun Cai

Zhaoxu Dong

Perry Dwain Converse

Evan Earl Jacobson

Maurice J Dust

Venkata Dandibhotla

Vijay Janardhan

Insu Chang

Michael Andrew Snopko

Yong Sun

Baoyang Deng

Casey Alan Otten

Wenming Zhao

Francisco Roberto Green

Nathan Stephen Pauli

Bryan Thomas Siegel

Albert A Wray

Salim A Jaliwala

Kyle B Walton

Theodore E Wiersema

Chunhui Pan

James Chase

Koti Ratnam Padarthy

Mallikharjuna Rao Boddu

Shiferaw Damtie Beyene

Yifeng Lu

John James Krone

Eric James Johannsen

Timothy Michael O'Donnell

Todd R Kabrich

Suresh Baddam Reddy

Anthony C Rodman

Jason Kenneth Bloms

Andy Publes

Richard A Carpenter

James David Siegle

Tazio Stephan Grivetti

Antony James Eager

Matthew Edward Leustek

Mohamed I Daoud

Kevin Dee Ehrenhardt

Erik L Olsen

Daniel Wilson Hoyt

Stephen C Nofsinger

Eric W Ohlson

Daryl Belshan

Li Zhang

Jason Weber

Ronald C Gayles

Jason T Herlehy

John Gates

David Kapparos

Purna Chander Nalla

Joseph C Bufkin

Rodney D Harms

Nathan J Wieland

Arun Rajendran

Nausheen Ahmad

Michael Coppinger

Daojie Zhang

Nimesh Akalanka Jayakody

Robin Ernest De Ruiter

Syed Ameenuddin Hussain

Theodore Ernest Wiersema, Iii

Richard Severns, Ii

Mingang Wang

Lidi Chen

Brian W Young

Derek Paul Kamp

Christopher G Y Haddad

An electronic control module (ECM) on-board a machine executes a reduced-order model configured to generate virtual sensor data associated with a machine component based on sensor data measured by actual sensors. The ECM can also execute a machine learning model configured to generate offset data for the sensor data and/or the virtual sensor data, based on operating conditions, an age of the machine component, and/or other factors that the reduced-order model may not consider. The sensor data and/or the virtual sensor data, adjusted based on the offset data, can indicate whether the machine component has failed or is predicted to fail at a future point in time, and/or a remaining usable life of the machine component.

On-board machine component failure detection

A system may determine configuration information associated with a power system for a work site that includes one or more power sources. The system may determine respective operation parameters of the one or more power sources and one or more performance criteria associated with the power system. The system may determine a baseline operation plan for the power system. The system may identify an optimization analysis technique and may process, using the optimization analysis technique, the configuration information associated with the power system, the respective operation parameters of the one or more power sources, and the one or more performance criteria associated with the power system to generate the optimized operation plan for the power system. The system may cause, based on the baseline operation plan for the power system and the optimized operation plan for the power system, one or more actions to be performed.

Optimized operation plan for a power system

A method may include receiving information related to operation or a configuration of a hydraulic fracturing system. The hydraulic fracturing system may include a plurality of electric power source outputs and a plurality of hydraulic fracturing rigs. The method may further include performing, based on the information, asymmetric power management of the plurality of electric power source outputs. The method may further include performing, based on the information, asymmetric load management of the plurality of hydraulic fracturing rigs.

Asymmetric power management and load management

A system may comprise a device. The device may be configured to receive, from one or more sensor devices of the machine, sensor data associated with wear of one or more components of an undercarriage of the machine; and predict, using a machine learning model and the sensor data, an amount wear of the one or more components based on a wear rate of the one or more components. The machine learning model is trained, using training data, to predict the wear rate of the one or more components. The training data includes two or more of: historical sensor data, historical inspection data, or simulation data, of a simulation model, from one or more third devices. The device may perform an action based on the amount of wear.

Undercarriage wear prediction using machine learning model

An automated hydraulic fracturing system and method for controlling various aspects of a fracking operation is disclosed. A master controller receives various operating parameters, such as desired operating conditions, information about a fracking site, information about trailers and/or other equipment at the fracking site, and/or sensor signals. The master controller may access one or more trailer model(s) that model various parameters of equipment (e.g., engines, transmissions, pumps, trailers, etc.) at the fracking site as a function of controlled operating conditions. The master controller uses the trailer models in conjunction with operating parameters to generate control signals that automatically control the various equipment at the fracking site to optimize various desired outcomes, such as reduced operating costs, reduced emissions, reduced idle time, increased efficiency, etc. The control signals are sent to controllers of the individual equipment to control the operations of those equipment and achieve an overall optimized fracking operation.

Automated hydraulic fracturing operation

A detection system for detecting winding faults, such as inter-turn winding faults in the stator and/or rotor of an electrical generator utilizes one or more vibration sensors that can be located on a generator housing. The vibration sensors make mechanical vibration measurements and transmit them to a fault analyzer. The fault analyzer can compare the measured vibrations with a threshold to determine if a winding is occurring. In an embodiment, the fault analyzer can convert the mechanical vibration measurements from the time domain to a frequency domain to facilitate analysis.

System and method for detecting winding faults in a generator

A control system implementing a hybrid energy storage system (ESS) optimization strategy is disclosed. The hybrid ESS optimization strategy may be implemented in a machine that comprises a power system that includes a plurality of power sources and a power controller that includes one or more processors. The power controller may receive information related to a set of brake-specific fuel consumption (BSFC) maps associated with the plurality of power sources, determine a performance indicator using a cost function associated with the plurality of power sources, and generate a command to operate the power system based on a power distribution that minimizes an energy cost to operate the power system based on the information related to the set of BSFC maps, the performance indicator, and a load associated with the power system.

Hybrid energy storage system optimization strategy with intelligent adaptive control

A hydraulic fracturing machine includes a pump failure detection system. The hydraulic fracturing machine includes a hydraulic fracturing pump with a power end and a fluid end. The power end includes a plurality of roller bearings, and the fluid end includes a flow of fluid. A particle sensor coupled to the power end is configured to transmit particle information regarding a quantity of particles in the fluid. A temperature sensor, also coupled to the power end, is configured to transmit temperature information regarding a temperature of the fluid. A vibration sensor coupled to the power end is configured to transmit vibration information regarding a vibration of each of the plurality of roller bearings. An electronic control module analyzes the particle information, the temperature information and the vibration information, and calculates a failure warning level based on the analysis.

Hydraulic fracturing pump health monitor

A system and method for protecting a genset from pole slip is disclosed. The system may comprise a generator, a prime mover and a controller. The generator includes a stator and a rotor. The prime mover is configured to drive rotation of the rotor. The controller may be configured to: determine mechanical status of the generator based on data associated with a translational displacement of the rotor; determine electrical status of the generator based on (a) a load angle or (b) the load angle and a rate of change of the load angle associated with rotation of the rotor in the stator; determine an operating condition of the generator based on fusion of the mechanical status and the electrical status; if the operating condition is a pole-slip-warning, activate an output member to display or emit a warning; and, if the operating condition is a pole-slip, activate a protective action.

Generator pole slip detection

A method for adjusting a valve lash in an internal combustion engine includes receiving a first signal generated by a sensor secured to the internal combustion engine, the first signal being indicative of a closing of a valve, receiving a second signal indicative of at least one of an engine speed of the internal combustion engine or a position of a camshaft of the internal combustion engine, and automatically determining an adjusted amount of lash associated with the valve based on the received first signal and the received second signal. The method also includes comparing the adjusted amount of lash to at least one predetermined threshold, and providing, in response to determining that the adjusted amount of lash is greater than the at least one predetermined threshold, a valve lash re-adjustment notification.

System and method for engine valve lash calibration

A device may receive a power demand for a load and a weather forecast for a time period. The device may determine a first supply of power available from a photo-voltaic (PV) installation for the time period based on the weather forecast. The device may determine a power deficit for the time period based on the power demand and the first supply of power. The device may determine a first cost associated with utilizing a second supply of power available from a battery and a second cost associated with utilizing a third supply of power available from an engine for the time period. The device may determine a power source to overcome the power deficit based on the first cost and the second cost and may cause the PV installation and the power source to supply power to satisfy the power demand for the load.

Micro-grid site predictive control for multipower resource management with machine learning

A fluid degradation monitoring system may detect a replacement of a fluid of a machine. The fluid degradation monitoring system may determine, after detecting the replacement of the fluid, a first degradation estimate of the fluid using a first technique based on characteristics relating to an operation of the machine. The fluid degradation monitoring system may detect a transition in a value of a dielectric constant of the fluid from a first phase to a second phase. The fluid degradation monitoring system may determine, after detecting the transition in the value of the dielectric constant, a second degradation estimate of the fluid using a second technique based on characteristics relating to the fluid.

Systems and methods for remaining useful life prediction of a fluid

A power management system may monitor data relating to respective loads on a plurality of power sources. The plurality of power sources may be associated with a drilling rig engaged in a drilling process. The power management system may determine a stage of the drilling process based on the respective loads on the plurality of power sources. The power management system may determine, based on the stage of the drilling process that is determined, respective load shares for the plurality of power sources. The power management system may manage load sharing among the plurality of power sources according to the respective load shares that are determined.

Power management system for a drilling rig

A rig management system is disclosed. The rig management system may be configured to receive information to be used to control shifting of a transmission to prevent cavitation during use of the hydraulic fracturing rig. The rig management system may be configured to determine a flow rate for a pump based on the information. The rig management system may be configured to determine an output torque of the transmission based on the flow rate. The rig management system may be configured to determine a transmission gear for the transmission based on the output torque and a fuel consumption map, wherein the fuel consumption map identifies a respective fuel consumption rate for different combinations of transmission gears and output torques. The rig management system may be configured to cause the transmission to shift into the transmission gear after determining the transmission gear.

Rig management system for transmission control of a hydraulic fracturing rig

An engine controller to control a plurality of engines is disclosed. The engine controller may identify a plurality of engines configured to provide power to a load, wherein the plurality of engines have a first set of priorities associated with providing the power to the load; receive a plurality of parameters from a plurality of monitoring devices monitoring the plurality of engines; calculate a plurality of metrics corresponding to the plurality of engines based on the plurality of parameters; determine, based on the plurality of metrics, that a switching condition is satisfied to switch from the first set of priorities to a second set of priorities for the plurality of engines; determine the second set of priorities for the plurality of engines based on the plurality of metrics; and cause the plurality of engines to provide respective amounts of power to the load based on the second set of priorities.

Control of multiple engines using one or more parameters associated with the multiple engines

Growing community of inventors

Dunlap, IL, United States of America

Yanchai Zhang