Patent Description:
Machinery and equipment often include components (e.g., rotating or moving components) that need support during operation. This support may be provided by bearings or similar devices. For example, certain power production equipment, such as reciprocating engines coupled to electrical generators may include a variety of bearing supporting moving components.

Document <CIT> teaches a method, wherein a bearing wear is monitored and subsequently used to warn of a mechanical failure. The monitoring is carried out by analyzing the information contained in the measurement signal on an angular position and/or velocity. <CIT> uses a model which predicts the evaluation of a time-series of measured parameters, which are directly or indirectly of a degree of breakdown of a lubricant film at a bearing. These documents teach to monitor or control a lubricant film or a mechanical failure.

In certain applications, the bearings include direct contact journal bearings systems used to support the moving component. It may be useful to improve journal bearing system operations.

Certain embodiments commensurate in scope with the originally claimed invention are summarized below. These embodiments are not intended to limit the scope of the claimed invention, but rather these embodiments are intended only to provide a brief summary of possible forms of the invention. Indeed, the invention may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

A first embodiment provides a method that includes executing a wear protocol to derive a start-stop (SS) wear, a steady-state operating hours (OH) wear, or a combination thereof, of a test journal bearing system. The method further includes observing operations of an engine via one or more sensors to derive a number of start-stops, steady-state operating hours, or a combination thereof. The method also includes determining a determined journal bearing system wear based on applying a physics-based model of a journal bearing system and the transfer function to the number of start-stops, the steady-state operating hours, or to the combination thereof. The method additionally includes executing one or more actionable items on the engine based on the determined journal bearing system wear.

A second embodiment provides a system that includes an electronic control unit (ECU) having one or both of a memory or storage device storing one or more processor-executable executable routines, and one or more processors configured to execute the one or more executable routines which, when executed, cause acts to be performed. The acts to be performed include controlling operations of an engine. The acts to be performed further include observing operations of the engine via one or more sensors to derive a number of start-stops, steady-state operating hours, or a combination thereof. The acts to be performed also include determining a determined journal bearing system wear based on applying a physics-based model of a journal bearing system and a transfer function to the number of start-stops, the steady-state operating hours, or to the combination thereof.

The present disclosure is directed towards systems and methods for improving journal bearing life and maintenance of journal bearings. In one embodiment, a model is constructed, which may advantageously include at least one transfer function and at least one physics-based journal bearing model. The transfer function may combine journal bearing wear and tear information due to both start-stop engine operations as well as steady state engine operations. Indeed, each transfer function may include both start-stop as well as steady state effects suitable for then predicting an amount of wear for a specific type of journal bearing. In certain embodiments, the transfer function may be created by capturing data in situ from one or more engines in the field. The data may then be processed as described in more detail below to derive the one or more transfer functions, as well as to derive certain calibration coefficients useful in calibrating the model to more accurately derive wear and tear based on observed field conditions.

Accordingly, the transfer functions may be applied to field wear and can separate out wear due to start-stop and steady running conditions. The physics based journal bearing model may additionally calculate bearing wear separately, including the wear due to the start stop and steady running conditions. The physics based journal bearing model may be calibrated and validated using test rig data and/or field wear data. Thus a difference between model predictive bearing wear and field bearing wear may be small (e.g., within +/- <NUM>%). The calibrated model can then predict total bearing wear due to start-stop conditions, steady state conditions, or combination thereof for certain inputs(e.g., engine load, engine speed, operating temperatures, bearing architecture, lube/oil properties, or a combination thereof). Advantageously, the engine may be observed during operations, and journal bearing wear predicted. The predicted wear may then be applied to improve journal bearing life and maintenance, for example, by issuing alerts or maintenance schedules that would eliminate or minimize undesired maintenance events.

It may be beneficial to describe a system that may include one or more journal bearings. Accordingly, turning now to the drawings and referring to <FIG>, the figure illustrates a block diagram of an embodiment of a portion of an engine driven power generation system <NUM> having one or more journal bearing systems <NUM>. More specifically, the one or more journal bearing systems <NUM> are disposed in an engine <NUM> (e.g., a reciprocating internal combustion engine) having one or more combustion chambers <NUM> (e.g., <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or more combustion chambers <NUM>). An air supply <NUM> is configured to provide an oxidant <NUM>, such as air, oxygen, oxygen-enriched air, oxygen-reduced air, or any combination thereof, to each combustion chamber <NUM>. The combustion chamber <NUM> is also configured to receive a fuel <NUM> (e.g., a liquid and/or gaseous fuel) from a fuel supply <NUM>, and a fuel-air mixture ignites and combusts within each combustion chamber <NUM>. The hot pressurized combustion gases cause a piston <NUM> adjacent to each combustion chamber <NUM> to move linearly within a cylinder <NUM> and convert pressure exerted by the gases into a rotating motion, which causes a shaft <NUM> to rotate. Further, the shaft <NUM> may be coupled to a load <NUM>, which is powered via rotation of the shaft <NUM>. For example, the load <NUM> may be any suitable device that may generate power via the rotational output of the system <NUM>, such as an electrical generator. Additionally, although the following discussion refers to air as the oxidant <NUM>, any suitable oxidant may be used with the disclosed embodiments. Similarly, the fuel <NUM> may be any suitable gaseous fuel, such as natural gas, associated petroleum gas, propane, biogas, sewage gas, landfill gas, coal mine gas, for example.

The system <NUM> disclosed herein may be adapted for use in stationary applications (e.g., in industrial power generating engines) or in mobile applications (e.g., in cars or aircraft). The engine <NUM> may be a two-stroke engine, three-stroke engine, four-stroke engine, five-stroke engine, six-stroke engine, or more. The engine <NUM> may also include any number of combustion chambers <NUM>, pistons <NUM>, and associated cylinders (e.g., <NUM>-<NUM>). For example, in certain embodiments, the system <NUM> may include a large-scale industrial reciprocating engine having <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM> or more pistons <NUM> reciprocating in cylinders. The system <NUM> may generate power ranging from <NUM> kW to <NUM> MW. In some embodiments, the engine <NUM> may operate at less than approximately <NUM> revolutions per minute (RPM). In some embodiments, the engine <NUM> may operate at less than approximately <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, or <NUM> RPM. In some embodiments, the engine <NUM> may operate between approximately <NUM>-<NUM> RPM, <NUM>-<NUM> RPM, or <NUM>-<NUM> RPM. In some embodiments, the engine <NUM> may operate at approximately <NUM> RPM, <NUM> RPM, <NUM> RPM, <NUM> RPM, or <NUM> RPM. Exemplary engines <NUM> may include General Electric Company's Jenbacher Engines (e.g., Jenbacher J624, Type <NUM>, Type <NUM>, Type <NUM>, Type <NUM>, or J920 FleXtra) or Waukesha Engines (e.g., Waukesha VGF, VHP, APG, 275GL), for example.

The driven power generation system <NUM> may include one or more sensors <NUM> suitable for detecting a variety of engine conditions, such as engine load, engine speed, operating temperatures, bearing lube/oil properties, and so on. The sensors <NUM> may additionally sense fluid flows (e.g., fuel flows, exhaust flows, O2 compositions) pressures, component temperatures, vibration, clearances between rotating and stationary components and so on. The sensors <NUM> are shown communicatively coupled to an engine control unit (ECU) <NUM>. The ECU <NUM> may include one or more processors <NUM> and a memory <NUM>. During operations, signals from the sensors <NUM> are communicated to the ECU <NUM> to determine a variety of conditions (e.g., pinging, fuel flow, exhaust flow, speed, valve timing, and so on). The ECU <NUM> may then adjust certain engine <NUM> parameters to control the engine <NUM>. For example, the ECU <NUM> may adjust ignition timing, adjust boost pressure, valve timing, fuel flows, air flows, exhaust flows, and so on.

As further described herein, the ECU <NUM> may include one or more models and transfer functions suitable for deriving journal bearing system <NUM> conditions. For example, the ECU <NUM> may derive approximate wear, remaining life, maintenance schedules, and so on, for the journal bearing system <NUM> and related components. Additionally or alternatively, an external computing system <NUM> may also be communicatively coupled to the engine system <NUM>. In one embodiment, the external computing system <NUM> may be included as a component of a test rig system <NUM>. The external computing system <NUM> may thus receive data from the engine system <NUM>, including engine logs and/or real time data, to derive journal bearing system <NUM> transfer functions using certain techniques described in more detail below. The test rig system <NUM> may include testing embodiments of the power production system <NUM>, including test benches suitable for testing wear and tear on the journal bearing systems <NUM>, sensor systems (e.g., microscopes) to observe the wear and tear, and so on. In some embodiments, the journal bearing system <NUM> may be an instrumented journal bearing that may transmit (wired or wirelessly) data regarding temperatures, oil flow rates, oil properties (e.g., viscosity, contaminants), journal speed, and the like.

Turning now to <FIG>, the figure is a front view illustrating an embodiment of the journal bearing system <NUM>. The illustrated embodiment includes two bearings <NUM> and <NUM> surrounding a journal or shaft <NUM>. Oil or lubricant <NUM> may be disposed between the bearings <NUM>, <NUM> and the journal <NUM>. The oil <NUM> may enter and or exit the bearing system <NUM> via one or more oil ports <NUM>. In use, the journal bearings <NUM>, <NUM> may support journals <NUM> such as crankshafts, camshafts, piston shafts, flywheel shafts, and so on, as the journals <NUM> rotate about certain axes, such as axis <NUM>, <NUM>. Axis <NUM> is representative of an axis concentric with the center of a circle formed by the bearings or shells <NUM>, <NUM>, while axis <NUM> is representative of a displacement axis caused by the displacement of the journal <NUM> during rotative operations, creating an eccentricity e. The bearings <NUM>, <NUM> may be disposed in a variety of housings (not shown) depending on where in the engine <NUM> the bearings <NUM>, <NUM> may be located.

As the journal <NUM> rotates inside of the bearings <NUM>, <NUM>, a pressurized lubricant film of the oil <NUM> may be generated by the journal <NUM> rotation, providing for a hydrodynamic journal bearing system <NUM>. During operations, the journal <NUM> may experience certain wear and tear. For example, the journal <NUM> may contact the bearings <NUM>, <NUM> at inner surfaces <NUM>, <NUM> which may cause wear and tear on the bearings <NUM>, <NUM>. Outside surfaces <NUM>, <NUM>, may not experience much, if any wear and tear. As more and more operating hours accumulate, the bearings <NUM>, <NUM> may eventually need replacing. Bearings <NUM>, <NUM> may accumulate wear and tear differently based on operating conditions. For example, engine <NUM> start-stops may result in higher levels of bearing <NUM>, <NUM> wear when compared to the engine <NUM> operating at steady state (e.g., base load) conditions. In some maintenance programs for the power system <NUM>, the bearings <NUM>, <NUM> may be replaced at a certain number of operating hours for the engine <NUM> irrespective of the actual wear on the bearings <NUM>, <NUM>. The techniques described herein may provide for predictive actual wear on the bearings <NUM>, <NUM> and/or the journal <NUM>. Accordingly, the lifecycle and maintenance schedule for the bearings <NUM>, 36and the journal <NUM> may be improved. Advantageously, the predicted actual wear and tear on the bearings <NUM>, <NUM> and/or the journal <NUM> may be within +/- <NUM>% of observed actual wear and tear, thus providing for improved derivation of remaining life, and thus more efficient maintenance schedules.

<FIG> is a flowchart illustrating an embodiment of a process <NUM> suitable for deriving a mixed lubrication model. The process <NUM> may be implemented as computer code or instructions stored in the memory <NUM> and executable via the processor <NUM>. Additionally or alternatively, the process <NUM> may be implemented in hardware, such as in a custom chip, FPGA chip, and so on. In the depicted embodiment, tribology theory may be used to derive (block <NUM>) a pressure p and a film thickness h. For example, Reynold's equation and elastic deformation equations may be used to derive p(x,y) and h(x,y) along x and y axis where the x axis may include a circumference for the journal <NUM> and the y axis may include a length for the journal <NUM>. The process <NUM> may then derive (decision <NUM>) if the film thickness h divided by a root mean square roughness (Rq) for the journal surface is greater than a constant, such as <NUM>. If the film thickness h divided by Rq is greater than <NUM> (decision <NUM>), then the process <NUM> may determine (block <NUM>) that the journal <NUM> is operating under a lubricated regime and thus, that the pressure p is equivalent to a fluid pressure pf. Otherwise, there is likely a contact between the journal <NUM> and the bearings <NUM>, <NUM> and thus, the process <NUM> may derive (block <NUM>) an asperity contact pressure pc. For example, the Greenwood-Tripp asperity contact model may be used to derive pc. It is to be understood that the constant of <NUM> is one example. Constants larger than <NUM> may provide for situations where asperity contact is more prevalent, and constants smaller than <NUM> may provide for situations having less asperity contact.

The Greenwood-Trip asperity contact model gives a general theory of contact between two rough plane surfaces. The model shows that the load and the area of contact remain almost proportional, independently of the detailed mechanical and geometrical properties of the asperities. Further, a single-rough-surface Greenwood-Trip asperity contact model can always be found which will predict the same laws as a given two-rough-surface model.

Based on decision <NUM>, the process <NUM> may then calculate a load Wf (block <NUM>) or a load Wc (block <NUM>) via equations Wf = ∫∫ pf(x,y)dx dy and Wc = ∫∫ pc(x,y)dx dy, respectively. A total load W may then be calculated (block <NUM>) as W = Wf + Wc. If the process <NUM> derives that W converges (decision <NUM>) to an applied load, for example, by looking at field and or simulation data for applied load, e.g., bearing <NUM>, <NUM> load and/or journal <NUM> load, the process <NUM> may derive (block <NUM>) that a mixed lubrication model is derivable. Otherwise, the process <NUM> may change eccentricity e (block <NUM>) and loop to block <NUM>. The process <NUM> may then derive (block <NUM>) a mixed lubrication model <NUM>. For example, the mixed lubrication model <NUM> may include the equation <MAT> where δ is a wear in micrometers (µm), K<NUM> is a calibration parameter, H is a bearing <NUM>, <NUM> hardness, and v is a linear relative velocity for the journal <NUM>. By executing process <NUM>, the techniques described herein may provide for a more accurate mixed lubrication model <NUM> that incorporates both lubricated regimes as well as asperity contact modeling to derive wear. The model <NUM> may then be used, as described in more detail below with respect to <FIG>, to more accurately predict wear based on number of start-stops and/or steady state operating hours for the engine <NUM>.

<FIG> is a flowchart illustrating an embodiment of a process <NUM> suitable for predicting wear and tear for the journal bearing system <NUM>. The process <NUM> may be implemented as computer code or instructions stored in the memory <NUM> and executable via the processor <NUM>. Additionally or alternatively, the process <NUM> may be implemented in hardware, such as in a custom chip, FPGA chip, and so on. In the depicted embodiment, the process <NUM> may first create (block <NUM>) one or more models <NUM>, such as physics-based models of the journal bearing system <NUM> and/or power production system <NUM>. The physics-based models may simulate operations of the journal bearing system <NUM> and/or power production system <NUM>. For example, one of the models <NUM> may include model <NUM> described above. Other models <NUM> may include computational fluid dynamic (CFD) models, thermodynamic models, abrasion models, and so on, may be used to model the behavior of the oil <NUM>, the bearings <NUM>, <NUM>, and/or the journal <NUM> to determine expected temperatures (e.g., oil temperature, temperatures at various sections of the bearings <NUM>, <NUM> and the journal <NUM>), pressures (e.g., oil pressure, pressures experienced by the bearings <NUM>, <NUM> and the journal <NUM>), flow rates (e.g., oil flow rate into/out of the bearing system <NUM>), speeds (e.g., rotational journal speed), clearances (e.g., distance between the journal <NUM> and the bearings <NUM>, <NUM>), expected contact between the journal <NUM> and the bearings <NUM>, <NUM>, and the like. Additionally, oil <NUM> properties such as viscosity, temperature, isothermal compressibility, interfacial tension, and the like, may be modeled. The power production system <NUM> models may include models detailing speed, torque, power production, temperature of various components, pressures, flow rates, and so on, of the various components.

The models <NUM> may additionally take into account material make up for the bearings <NUM>, <NUM> and journal <NUM>, as well as certain geometries and/or architectures of the bearing system <NUM>, such as eccentricity e, oil clearance, crush height, journal diameter, journal shape (e.g., circular, concave, convex, tapered), and so on. In certain embodiments, the journal bearing system models <NUM> may take as input start-stop (SS) operating hours and steady-state (OH) operating hours to increase accuracy. That is, rather than simply input the number of operating hours, the models <NUM> may receive as input SS hours of operation and OH hours of operation, and then derive wear. For example, physics-based models may model degradation due to heat, abrasion, abutment of journal <NUM> against bearings <NUM>, <NUM>, oil viscosity, pressures, flow rates, temperatures, speeds, and so on. The degradation may thus be indicative of wear. That is, successive degradation is representative of cumulative wear. Accordingly, wear and remaining life may be determined for the bearings <NUM>, <NUM>, the journal <NUM>, and/or the oil <NUM>.

In certain embodiments, the process <NUM> may improve accuracy by using field data. The field data may provide for the derivation of certain test procedures that more accurately may derive one or more transfer functions as well as calibration coefficients, and described in more detail below. Accordingly, the process <NUM> may communicate the field data (block <NUM>), for example via wired or wireless techniques. The field data may include real-time data from the sensors <NUM> and/or instrumented journal bearing system <NUM> embodiments, as well as data from logs stored in the ECU <NUM> and/or other systems. As mentioned earlier, the data may include temperatures, pressures, fluid flows, clearances, operating hours, and/or start-stops for the components of the system <NUM>.

The process <NUM> may then derive a wear protocol (block <NUM>), such as a journal bearing system wear protocol suitable for studying how SS and OH hours may affect the various components of the journal bearing system <NUM>. For example, the test rig <NUM> may include mechanical systems to operate a test journal bearing system, for example, by operating the test journal bearing system at various speeds, accelerations, decelerations, loads, clearances, temperatures, pressures, fluid flows, and so on.

Table <NUM> above is illustrative of an example wear protocol (second column) suitable for modeling a J624 engine bearing field conditions (first column). The J624 engine bearing has a diameter of <NUM> millimeters (mm), while the test bearing used in the wear protocol has a diameter of <NUM> millimeters (mm). Indeed, the wear protocol for larger sized bearings may test a smaller bearing and provide results that would be indicative of testing the larger sized bearing. The wear protocol, for example, may operate the smaller test bearing at faster journal speeds, and/or accelerations to provide for wear similar to the larger sized bearing. Also shown is a generic wear protocol (third column) that may be customized to a variety of bearing types and sizes.

The wear protocol may then be executed (block <NUM>) via the test rig <NUM>, for example, to observe or obtain SS and/or OH wear at various operating times. For example, the test bearing may be operated at start-stop conditions and then tested to obtain SS wear. Likewise, the test bearing may be operated at steady state conditions and then tested to obtain OH wear. Similarly, the test bearing may be operated at both start-stop conditions and steady state conditions and then tested to obtain a combination of SS and OH wear. To operate at test start-stop conditions and turning now to <FIG>, the test rig <NUM> may apply certain speeds and/or loads for a desired time.

As shown in an example bearing system profile <NUM> of <FIG>, the test rig <NUM> may simulate start-stop conditions by applying a journal speed profile <NUM>, a bearing unit profile <NUM>, and an inlet oil temperature profile <NUM>. The profiles <NUM>, <NUM>, <NUM> may be executed by the test rig <NUM> at a desired time range shown in x-axis <NUM>. Y-axis <NUM> shows values for journal linear speed in m/s and bearing unit load in MPa. Y-axis <NUM> shows temperature in C°. By following the profiles <NUM>, <NUM>, <NUM>, one start-stop event may be modeled. Accordingly, multiple start-stop events may be modeled by running the profiles <NUM>, <NUM>, <NUM> multiple times. Steady state conditions may be modeled by operating the test rig <NUM>, for example, at constant speed, such as the journal speed, surface speed, or combination thereof. Likewise, constant bearing loads may be tested during steady state testing. Test steady state operations, like field steady state operations, may be measured in minutes and/or hours.

Turning now back to <FIG>, the process <NUM> may derive (block <NUM>) one or more transfer functions <NUM>. Each of the transfer functions <NUM> may be based on the following equations:
<MAT> where δ is representative of wear, for example, in micrometers (µm) and f<NUM> is a function that takes as input the number of start-stops to arrive at the wear δ. <MAT> where δ is representative of wear and f<NUM> is a function that takes as input the number of steady state operating hours to arrive at the wear δ.

The transfer function <NUM> suitable for deriving a number of start-stops based on either wear δ and/or steady-state operating hours may then be defined as:
<MAT> where <MAT> is the inverse function to f<NUM>.

Accordingly, the number of start-stops may be derived based on knowing the number of steady state operating hours and/or based on a known wear, and vice versa. Indeed, the transfer function <NUM> shown above enables for the calculation of wear due to the number of start-stops, due to the number of steady state operating hours, or a combination thereof. To provide for added accuracy, the process <NUM> may calibrate (block <NUM>) certain field wear coefficients and apply the calibrated coefficients to the models(s) <NUM> and/or transfer function(s) <NUM> to derive and calibrate (block <NUM>) calibrated models <NUM> and/or transfer functions <NUM>, as further detailed below. Accordingly, log data including SS and/or OH may be for the engine <NUM> may be retrieved, and/or similar data provided real-time, and used to predict (block <NUM>) a more accurate wear for components of the journal bearing system <NUM> (e.g., bearings <NUM>, <NUM>, journal <NUM>, oil <NUM>) via the calibrated models <NUM> and/or transfer functions <NUM>. More specifically, the ECU <NUM> may solve for wear δ via the calibrated models <NUM> and transfer functions <NUM> by using SS and/or OH data as inputs. Based on the predictive wear δ, the process <NUM> may then provide (block <NUM>) certain actionable item, such as alarms/alerts, improved maintenance schedules, and the like. For example, if the predictive wear δ exceeds a threshold, and/or a growth rate, alarms/alerts may be provided. Likewise, maintenance schedules may be based on the predictive wear δ as opposed to number of operating hours for the engine <NUM>. In this manner, life and operations for the engine system <NUM> may be improved.

<FIG> illustrates example graphs <NUM>, <NUM>, showing calibration of parameters K based on experimental and/or field data <NUM>, <NUM>, respectively. As mentioned earlier, the models <NUM> may include calibration parameters K suitable for calibration certain models. For example, the wear equation <MAT> described above includes K<NUM> as a calibration parameter. The calibration parameters K (e.g., K<NUM>) may be calibrated by applying certain techniques, such as statistical techniques (e.g., linear regression, non-linear regression, data mining [e.g., k-means clustering and the like]), onto the data <NUM>, <NUM>. For example, graph <NUM> shows calibration parameters <NUM>, <NUM>, <NUM>, and <NUM> that may be used, but in the example, calibration parameter <NUM> appears to fit the data <NUM> with improved accuracy. Accordingly, the calibration parameter <NUM> may be used to calibrate models <NUM>, thus better "fitting" the experimental and/or field use data for start-stop wear.

Likewise, graph <NUM> shows calibration parameters <NUM>, <NUM>, and <NUM> that may be used, but in the example, calibration parameter <NUM> appears to fit the data <NUM> with improved accuracy. Accordingly, the calibration parameter <NUM> may be used to calibrate models <NUM>, thus better "fitting" the experimental and/or field use data for steady state wear. The calibrations may then be applied, for example, to the models <NUM> to derive the calibrated models <NUM>. By calibrating the models, the techniques described herein may increase predictive accuracy.

Turning now to <FIG>, the figure illustrates an embodiment of a calibrated graph <NUM> that may be derived via the models <NUM> and applied to find a wear based on a number of start-stops, steady state operating hours, or a combination thereof. More specifically, the graph <NUM> includes a wear (in microns) axis <NUM>, a number of start-stop axis <NUM>, and an operating hours axis <NUM>. A process, such as the process <NUM>, may apply the graph <NUM> as a "lookup" graph or table suitable for more quickly deriving wear based on the number of start-stops, steady state operating hours, or the combination thereof. The graph <NUM> may be created by combining the number of start stops with steady state operating hours as describe above, and then by calibrating the results, for example, via field wear data as described earlier with respect to calibration parameters K.

Technical effects of the disclosed embodiments include providing systems and methods for.

Claim 1:
A method for determining a wear of a journal bearing system (<NUM>) of an engine, comprising:
executing a wear protocol to derive a start-stop (SS) wear, a steady-state operating hours (OH) wear, or a combination thereof, of a test journal bearing system (<NUM>) on a test rig (<NUM>);
deriving a transfer function suitable for determining a total wear for the test journal bearing system (<NUM>) based on the SS wear, the OH wear, or the combination thereof;
observing operations of the engine (<NUM>) via one or more sensors (<NUM>) to derive a number of start-stops, steady-state operating hours, or a combination thereof;
determining as the wear of the journal bearing system (<NUM>) a determined test journal bearing system (<NUM>) wear based on applying the transfer function to the number of start-stops, the steady-state operating hours, or to the combination thereof; and
executing one or more actionable items on the engine (<NUM>) based on the determined journal bearing system (<NUM>) wear
wherein the determination of the wear of the journal bearing system (<NUM>) is furthermore based on a physics-based model of the journal bearing system (<NUM>).