System and method for processing data from a particle monitoring sensor

Abstract

Data related to the amount of contaminants in the fluid is obtained from a contaminant detection device (115). Aeration detection raw voltage data output and particle detection raw voltage data output are received from the contaminant detection device at an external data processing system separate from and independent of the contaminant detection device. The aeration detection raw voltage data output and particle detection raw voltage data output are analyzed and cleaned to determine real-time information about the amount of air bubbles and solid contaminant particles in the fluid, and the real-time information is then supplied to an onboard monitoring and control module of a machine to control one or more operations on the machine based on the determined amount of solid contaminant particles.

Description

Technical Field

[0001] The present disclosure relates generally to a system and method for processing data, and more particularly, to a system and method for processing raw voltage data output from a particle monitoring sensor. Background Art

[0002] Heavy machinery on highways and off-highways typically includes multiple components that cooperate to perform various tasks. The failure of one or more components of a machine may typically result in some functional loss of the machine, which may limit the performance capabilities of the machine. Some component failure events may be relatively small and simple to detect and resolve without causing real collateral damage to other components of the machine. However, other failure events may be more serious, thereby potentially damaging other components of the machine. For example, in a hydraulic system, a catastrophic pump failure may discharge fault debris into the hydraulic fluid. This debris may be absorbed by one or more other components of the hydraulic system, thereby potentially damaging these components. In addition, contaminants such as particles and air bubbles in the hydraulic fluid may be early warnings of other potential problems, such as impending failures of filters or filter components, leakage of suction lines leading to pumps, operation of machines on slopes or machine attitudes beyond acceptable operating parameters, low hydraulic levels, or wear or failure of other machine components.

[0003] Although debris or contaminants can be prevented from circulating throughout a hydraulic system by using one or more hydraulic filters, the accumulation of debris or contaminants can reduce the flow rate of the fluid. In a hydraulic system that delivers hydraulic fluid for performing tasks associated with the hydraulic system, a reduction in flow rate can prevent the necessary amount of fluid from reaching one or more critical components. Over time, these components may overheat, increasing the likelihood of premature wear and, ultimately, failure of these components. Additionally, predictions of the remaining useful life of filters and / or other machine components that interact with the hydraulic fluid can be related to the presence of contaminants, such as particles and air bubbles, in the hydraulic fluid.

[0004] One method for identifying debris accumulation in a hydraulic system involves using a differential pressure switch that monitors the input and output pressures of a fluid filter. If the differential pressure exceeds a threshold, the switch is triggered and a warning signal is generated that notifies the machine operator that the filter may be clogged with debris, which can indicate a catastrophic failure of one or more components associated with the hydraulic system.

[0005] One problem with threshold-based fault detection circuits is that because component failures often manifest themselves very quickly, the pressure differential may not be sufficient to trigger an alarm until after the fault event has already occurred. Such delayed detection of a fault event can potentially cause damage to other components in the fluid path, as contaminant particles that may have been expelled by the faulty component may cause damage to components located between the faulty component and the fluid filter. These particles may be difficult to remove once introduced into the system, and in addition to causing catastrophic component failure of one or more components in the short term, may also cause long-term incidental component wear, as these residual particles may remain in the system indefinitely.

[0006] Furthermore, delayed detection of failure events of certain "critical" components (e.g., a hydraulic pump for driving a machine track) may significantly reduce job site productivity. For example, if a failure event disables or otherwise disables a machine, the performance of tasks that depend on the operation of the machine may be delayed. Additionally, if a machine fails while operating in a job site, the machine may present an obstruction to other machines. Therefore, in order to limit the impact of catastrophic component failures and / or accidental component failures on the operation of the machine and the job site, a system for early detection of trends (e.g., increasing levels of contaminants in the hydraulic fluid that may lead to machine component failure) may be beneficial.

[0007] At least one system has been developed for predicting the remaining useful life of a hydraulic pump. For example, U.S. Patent No. 7,082,758 (the '758 patent) issued to Kageyama et al. describes a system for monitoring long-term and short-term trends associated with differential pressure data measured across a hydraulic filter. The system can predict pump failure or estimate pump useful life based on the degree of deviation between the long-term trend data and the short-term trend data.

[0008] Although the system of the '758 patent may be effective in predicting component failure in certain circumstances, the system of the '758 patent may have several disadvantages. For example, the differential pressure across a pump filter typically begins to increase only after the filter chamber has filled, which may not occur until after a significant failure contaminant, such as particulates or air bubbles, has been introduced into the fluid. Thus, when the system of the '758 patent detects that the differential pressure across the filter is trending upward or that an unacceptable level of aeration is present, one or more components may have already been severely damaged. Furthermore, while identifying levels of solid contaminant particles and predicting the life expectancy of filters and other components associated with the hydraulic fluid, the system of the '758 patent does not provide any means for distinguishing between contaminants, such as particulates and air bubbles, in the hydraulic fluid, or compensating for aeration levels in the hydraulic fluid used in heavy machinery.

[0009] The presently disclosed systems and methods for calibrating and utilizing output from a particle monitoring sensor that monitors fluids used in heavy machinery are directed to overcoming one or more of the above-mentioned problems. Summary of the invention

[0010] According to one aspect, the present disclosure relates to a method for processing data related to the amount of contaminants in a fluid, wherein the data is obtained from a contaminant detection device, the contaminant detection device being configured to determine the level of contamination in the fluid using an analog integrator that sums the area of ​​shadows cast by contaminants in the fluid passing through the passage when a light emitting diode (LED) is used to illuminate the fluid in the passage and a plurality of photodiodes are used to generate a voltage signal indicative of the area of ​​each shadow, wherein the analog integrator integrates the resulting voltage signal over time. The method may include: receiving an aeration detection raw voltage data output from at least one of the photodiodes of the contaminant detection device at an external data processing system separate from and independent of the contaminant detection device; and receiving a particle detection raw voltage data output from at least another one of the photodiodes of the contaminant detection device at the external data processing system. The method may further include: analyzing and cleaning the aeration detection raw voltage data output and the particle detection raw voltage data output to determine real-time information about the amount of air bubbles and solid contaminant particles in the fluid; supplying the determined amount of solid contaminant particles to an onboard monitoring and control module of the machine; and controlling one or more operations on the machine based on the determined amount of solid contaminant particles.

[0011] According to another aspect, the present disclosure relates to a system for processing data related to the amount of contaminants in a fluid, wherein the data is obtained from a contaminant detection device. The system may include an external data processing system that is separate and independent from the contaminant detection device and is configured to receive aeration detection raw voltage data output and particle detection raw voltage data output from the contaminant detection device. The external data processing system may also be configured to: analyze and clean the aeration detection raw voltage data output and the particle detection raw voltage data output to determine real-time information about the amount of air bubbles and solid contaminant particles in the fluid; supply the determined amount of solid contaminant particles to an onboard monitoring and control module of a machine; and control one or more operations on the machine based on the determined amount of solid contaminant particles.

[0012] According to another aspect, the present disclosure relates to a non-transitory computer-readable medium containing program instructions for causing a controller to perform the following method: receiving an aeration detection raw voltage data output and a particle detection raw voltage data output from a pollutant detection device at an external data processing system separate from and independent of the pollutant detection device. The method may also include: averaging the aeration detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the particle detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the aeration detection raw voltage data output over time according to a slow exponentially weighted moving average; averaging the particle detection raw voltage data output over time according to a slow exponentially weighted moving average; and determining a difference between the fast exponentially weighted moving average and the slow exponentially weighted moving average for each of the aeration detection raw voltage data output over time and the particle detection raw voltage data output over time. The method may further include: determining whether each of the differences is positive, indicating a rising signal, or whether each of the differences is negative, indicating a falling signal; determining whether the rising signal is less than a rising rate threshold and whether the falling signal is greater than a decay rate threshold; and when the differences are greater than the decay rate threshold and less than the rising rate threshold, determining that the raw voltage data output is a clean and acceptable data output for further processing and implementation of actions by an onboard monitoring and control module of the machine. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A schematic diagram illustrating an exemplary system for utilizing a raw voltage signal indicative of particulate contaminants and aeration levels in a fluid passing through an optical particle sensor;

[0014] Figure 2 Description Used to process Figure 1 An exemplary method for obtaining a raw voltage signal output by an optical particle sensor of the exemplary system shown in FIG.

[0015] Figure 3 Description for further processing from Figure 2 An exemplary method of receiving a signal from a system; and

[0016] Figure 4 Provides a depiction for calibration from Figure 1 Flow chart of an exemplary method of analyzing the output of an optical particle sensor. DETAILED DESCRIPTION

[0017] Figure 1An exemplary machine system 100 is illustrated for utilizing a raw voltage signal generated by an exemplary optical particle sensor 115, the raw voltage signal being indicative of particulate contaminants and air bubbles or aeration levels in a fluid passing through the optical particle sensor. The exemplary optical particle sensor 115 may include an internal control system 120, characterized by an oil particle counter microprocessor 122, the oil particle counter microprocessor being configured to receive aeration detection raw voltage data 124 indicating the number of air bubbles or the amount of aeration detected in the fluid under test; and particle detection raw voltage data 126 indicating the number of solid contaminant particles detected during implementation of an optical particle counting method 128. Figure 1 As shown in , the optical particle sensor 115 can be configured to use the oil particle counter microprocessor 122 to internally process the raw voltage data 124, 126 and produce a particle count voltage output suitable for some applications.

[0018] The light emitting section of the optical particle sensor 115 may include one or more light emitting diodes (LEDs) that may be configured to continuously emit light into the fluid passing through the passage in the sensor, and the light receiving section including one or more photodiodes may be configured to continuously receive the light that is continuously emitted from the light emitting section and has passed through the fluid. The light receiving section may be configured to convert the continuously received light into a continuous electrical signal that includes aeration detection raw voltage data 124 indicating the number of air bubbles or aeration level in the fluid; and particle detection raw voltage data 126 indicating the number and size of solid contaminant particles in the fluid. The one or more photodiodes of the light receiving section may be configured to generate different amounts of voltage depending on the area of ​​shadows cast on the photodiodes by the solid contaminant particles and air bubbles in the fluid passing through the sensor passage.

[0019] The oil particle counter microprocessor 122 of the optical particle sensor 115 may include one or more algorithms designed to compensate for measurement errors in the amount of solid contaminant particles in the fluid, which may be caused by excessive air bubbles or aeration in the fluid. Such algorithms may include amplifying voltage signals generated by one or more photodiodes and affected by shadows cast by air bubbles and solid contaminant particles on the photodiodes. The voltage signal indicating air bubbles can be amplified by an amplification ratio that is smaller than the amplification ratio applied to the voltage signal indicating solid contaminant particles in the fluid, thereby reducing the impact on particle counts caused by air bubbles or excessive aeration in the fluid. In addition and / or alternatively, the algorithm implemented by the oil particle counter microprocessor 122 can use a voltage signal generated immediately before the voltage signal generated by air bubbles or aeration exceeding a threshold area in the fluid, ignoring the voltage signal generated by air bubbles exceeding the threshold area, thereby also reducing the impact caused by air bubbles. However, as air bubbles become more prevalent in the fluid, a shadow area exceeding a certain threshold is generated on the photodiode, and the aeration detection raw voltage data increases, the ability of the oil particle counter microprocessor 122 to compensate for potential errors in voltage signals and particle counts may be limited.

[0020] To avoid errors in solid contaminant particle counts that may result when using the optical particle sensor 115 to determine the amount of contaminants in a fluid that may be susceptible to high levels of aeration and air bubbles (e.g., hydraulic fluid used in heavy machinery), exemplary embodiments according to the present disclosure may include receiving the aeration detection raw voltage data output 132 and the particle detection raw voltage data output 134 from the control system 120 of the optical particle sensor 115 at an external data processing system 110 that is separate and independent from the optical particle sensor 115. In various embodiments according to the present disclosure, terminals or other connectors may be provided on the optical particle sensor 115 to enable the external data processing system 110 to obtain the raw voltage data generated by the photodiode in the sensor as the raw voltage data varies according to the amount of solid contaminant particles and air bubbles contained in the fluid tested by the sensor.

[0021] The external data processing system 110 may be located on an onboard device that benefits from real-time monitoring of contaminants present in the fluid used by the device. Alternatively, the output from the external data processing system 110 may be provided to a device such as a heavy machinery operating at various work sites via a wired, wireless or cellular network. Whether located on an onboard machine or on a non-onboard machine and communicatively coupled to the machine, the external data processing system 110 may be configured to receive raw voltage data from one or more photodiodes contained in the optical particle sensor 115. The photodiode may be configured to generate a voltage signal associated with the shadow area projected on the photodiode when the light from the light emitting diode (LED) in the optical particle sensor 115 illuminates the fluid when the tested fluid passes through the sensor passage. The passage may be a translucent or transparent medium through which the tested fluid is pumped, wherein the LED illuminates the light through the passage and onto the photodiode located on the opposite side of the passage from the LED. Solid contaminant particles and air bubbles in the fluid may cause shadows of different sizes and cumulative areas to be projected on the photodiode, thus affecting the amount of voltage generated by the photodiode. An analog integrator may be provided to sum the shadow area cast by contaminants in the fluid passing through the passage of the optical particle sensor 115 as a light emitting diode (LED) illuminates the fluid as the fluid passes through the passage, and to integrate the resulting voltage signal over time to produce a raw voltage data output 132, 134.

[0022] like Figure 1 , the external data processing system 110 may include one or more processors, such as processor 140, configured to analyze and clean up individual raw voltage data outputs retrieved from the optical particle sensor 115. The raw voltage data outputs may be accessed through electrical terminals or other connectors disposed on the optical particle sensor 115 and electrically connected to receive voltage outputs from photodiodes of the optical particle sensor 115, which are configured to generate aeration detection raw voltage data 132 and particle detection raw voltage data 134. The one or more processors 140 of the external data processing system 110 may receive the raw voltage data from the optical particle sensor 115, analyze the data, and determine when the presence of air bubbles or aeration in the fluid exceeds a threshold level, at which point the accuracy of the particle detection raw voltage 134 may be degraded to the point where it should be ignored.

[0023] The output of one or more processors 140 from the external data processing system 110 can be provided to the onboard monitoring and control module 150 of the machine system 100 on heavy machinery or other equipment, which benefits from real-time information about the amount of contaminants in the fluid used by the equipment. The equipment can include one or more components that perform various tasks that contribute to machine environmental tasks (e.g., mining, construction, transportation, agriculture, manufacturing) or any other type of tasks associated with other types of industries. In some embodiments, the onboard monitoring and control module 150 can be installed on one or more machines that are communicatively coupled to the condition monitoring system via one or more communication networks. The onboard monitoring and control module 150 can be configured to monitor, collect, control and / or filter information associated with the contamination level in the fluid used during the operation of one or more machines, and when necessary, can be configured to also distribute information to one or more back-end systems, such as a central condition monitoring system in a back-end or central command center, and / or a system that can be used for subscribers (e.g., customers and operators of machines).

[0024] Figure 2 and 3 An exemplary algorithm is described that may be implemented by one or more processors 140 of the external data processing system 110 before providing the cleaned and analyzed data derived from the raw voltage output of the optical particle sensor 115 to one or more machine onboard monitoring and control modules 150. Figure 2 As shown in , an initial acceptable diagnostic range 210 (e.g., 0.45>V>4.75) for the raw voltage data received from the photodiode of the optical particle sensor 115 can be predetermined in order to distinguish acceptable outputs from unacceptable outputs that may have been produced by an electrical short, one or more loose electrical connections, or other anomalies present somewhere in the system.

[0025] The aeration detection raw voltage data 132 and the particle detection raw voltage data 134 can be based on Figure 2 The exemplary algorithm described in is processed separately to produce the Figure 2 The acceptable cleaned signal of the algorithm output 240 is shown as Figure 3 The inputs of the algorithm are the cleaned aeration signal 340 and the cleaned solid contaminant particle signal 341. Figure 2In an exemplary algorithm of , the raw voltage data 132, 134 from the optical particle sensor 115 may be monitored over time and averaged according to a fast exponentially weighted moving average 241 and a slow exponentially weighted moving average 242. The result of the slow exponentially weighted moving average 242 may be used as a clean-up signal 243, if desired. Alternatively and / or in addition, the difference 244 between the fast exponentially weighted moving average 241 and the slow exponentially weighted moving average 242 may be further analyzed to determine whether the difference is positive, indicating a rising signal and less than a rising rate threshold 245, or whether the difference is negative, indicating a falling signal and greater than a decay rate threshold 246. The processed voltage signal may also be selected according to debounce times 247 and 248, which are designed to eliminate multiple fluctuating state transitions by reducing the jittering value of the voltage signal to a single determinable value and preventing false trips. The acceptable cleaned signal 240 derived from each of the aeration detection raw voltage data 132 and the particle detection raw voltage data 134 may then be provided to the DSP in the form of a cleaned aeration signal 340 and a cleaned solid contaminant particle signal 341. Figure 3 Another algorithm shown in .

[0026] like Figure 3 As shown in , when both the cleaned aeration signal 340 and the cleaned solid contaminant particle signal 341 are stable, the cleaned aeration signal 340 and the cleaned solid contaminant particle signal 341 can be analyzed within the measurement duration 342 to calculate the aeration average 343 and the solid contaminant particle average 344. In some exemplary embodiments, the aeration average 343 and the solid contaminant particle average 344 can be determined within a short measurement duration of less than one minute. Alternative embodiments may include other measurement durations. Multiple continuous aeration averages 343 can then be grouped or binned 345 over a longer duration, selectively subjected to additional cumulative and analysis (CUSUM), and batched as needed under different operating environments and parameters, and then provided to the onboard monitoring and control module 150 for further decision-making and application to control the parameters to be implemented on a specific machine. The solid contaminant particle average 344 can be compared with a lookup table 346 to determine whether the signal quality is acceptable based on the observed aeration signal. An exemplary output 350 may include an identification of whether the output quality of the solid contaminant particle signal falls within a diagnostic range and one of a poor, acceptable, or good category for tracking long duration changes in solid contaminant particle levels and air bubble or aeration levels so that the onboard monitoring and control module 150 may then assign different weights to the data.

[0027] The machine including the machine system 100 and the onboard monitoring and control module 150 can be a fixed or mobile machine configured to perform operations associated with a specific industry (e.g., mining, construction, agriculture, etc.), as well as operations between or within various project environments (e.g., construction sites, mines, power plants, etc.). Non-limiting examples of fixed machine systems include engine systems operating in factories, material conveyors, or offshore environments (e.g., offshore drilling platforms). Non-limiting examples of mobile machines include commercial machines such as trucks, cranes, earthmoving vehicles, mining vehicles, backhoes, material handling equipment, agricultural equipment, ships, aircraft, and any type of movable machine operating in a work environment. The machine can be driven by an internal combustion engine, a fuel cell, an electric motor, or any combination thereof. The types of machines listed above are exemplary and are not intended to be limiting.

[0028] The machine system 100 and the external data processing system 110 of a machine implementing features of the present disclosure may also include or be operably associated with onboard data collection and communication equipment to monitor, collect and / or distribute information associated with one or more components of the machine. According to one embodiment, the onboard data collection and communication equipment may include a system for early detection of failures of one or more components associated with the machine. It is contemplated that the machine system 100 may include additional onboard data collection and communication equipment. For example, the machine system 100 may include data monitoring equipment (e.g., sensors, control modules, data collectors, etc.) for monitoring the health, productivity, status and / or performance associated with the machine.

[0029] The machine system 100 can be configured to monitor one or more parameters associated with a fluid system of the machine. For example, the machine system 100 can be implemented as part of a hydraulic system for generating hydraulic fluid and delivering the hydraulic fluid to one or more hydraulic actuators or actuators that control the movement of implements or tools associated with the machine. Alternatively or in addition, the machine system 100 can be implemented as part of a hydraulic transmission system that uses a pump / motor to operate a drive system for controlling a traction system of the machine.

[0030] The machine fluid system can generally include a fluid channel for sucking fluid from a reservoir. The fluid channel can deliver the fluid through a filter device that collects and removes particulate matter from the fluid. The fluid channel can then deliver the fluid to a pump that compresses the fluid and promotes the fluid to circulate in the fluid channel to ultimately deliver it to a load, such as an actuator for extending and retracting a boom, a dipper, or a bucket on an excavator. The fluid channel can include any type of medium that can be configured to accommodate and guide fluid flow. For example, the fluid channel can include metal, semi-metal, alloy, rubber, polymer, and / or plastic pipe. The fluid channel can be used to connect a fluid source, such as that included in the reservoir, to one or more components that can operate on the fluid or otherwise utilize the fluid. The reservoir can include any type of tank for storing excess fluid that is not included in the fluid channel, or any component associated therewith. For example, the reservoir can include a tank suitable for storing hydraulic fluid or other suitable fluids, or for circulating any other type of fluid through the fluid channel.

[0031] The filter device can include any device for filtering, collecting and storing pollutants, impurities or other particulate matter from the fluid flowing through the fluid channel. For example, the filter device can include an oil filter, a fuel filter, or any other type of filter suitable for removing pollutants from the fluid. The pump can be configured to produce a pressurized fluid flow, and can include a variable displacement pump, a fixed displacement pump, a variable flow pump or any other device suitable for pressurizing the fluid. The pump can be drivably connected to a power source by, for example, a countershaft, a belt, an electrical circuit or in any other suitable manner. The pump can be dedicated to supplying pressurized fluid to a transmission, one or more hydraulic systems or components (for example, work tools, etc.), an engine system, or other components associated with the fluid channel.

[0032] The load can be fluidly coupled to the pump and can embody any system that uses a fluid pressurized by the pump to perform a task. For example, the load can include an appliance coupled to a fluid channel of a hydraulic system. The appliance can be configured to perform a task in response to receiving a pressurized fluid from the pump. Excess fluid that circulates through the load can be returned to the reservoir via the fluid channel. The machine fluid system can also include one or more devices for controlling the operation of one or more components associated with the fluid system. For example, the machine fluid system can include an electronic control module that is configured to control the operation of a pump, one or more valves, and / or a load. According to one embodiment, the electronic control module can be configured to provide one or more control signals for operating the pump in response to an operator instruction for moving an appliance associated with the machine.

[0033] The machine system 100 may include one or more components configured to monitor parameters associated with the fluid system and, based on the monitored parameters, predict failure of one or more components associated with the fluid system, or identify preventive actions that should be taken to avoid failure or inhibit performance degradation. The system 100 may include, for example, one or more monitoring devices, such as an optical particle sensor 115, and one or more pressure sensors, fluid flow sensors, temperature sensors, data collection devices, communication networks, and condition monitoring systems configured to provide data to a backend, one or more subscribers, or other end users of the data. The monitoring devices included in the machine system 100 may include devices configured to accumulate performance data associated with one or more machines. For example, the monitoring device may include one or more sensors for measuring operating parameters, such as engine and / or machine speed, and machine attitude and / or position; fluid pressure, flow rate, temperature, contamination level and or viscosity of a fluid; current and / or voltage levels; fluid (i.e., fuel, oil, etc.) consumption rate; load level (i.e., payload value, percentage of maximum payload limit, payload history, payload distribution, etc.); transmission output ratio, slip, etc.; towing level and traction data; drive shaft torque; intervals between planned or performed maintenance and / or repair operations; and any other operating parameter of the machine.

[0034] The optical particle sensor 115 may include any device configured to monitor and measure particle count data associated with a fluid flowing through a fluid channel. In various embodiments according to the present disclosure, the optical particle sensor 115 may be configured or modified to output raw voltage data representing the amount of air bubbles or aeration in the tested fluid, and raw voltage data representing the amount of solid contaminant particles in the fluid. The term "particle count data" as used herein may include any data indicating the particles present in the fluid. For example, the particle count data may include the number and size of particles present in the fluid flowing through the fluid chamber, the area occupied by many solid contaminant particles and / or air bubbles, the speed of particles within the communication, the flow rate of the fluid traveling through the fluid channel, or any other data that can be determined or derived from particles present in the fluid flowing through the fluid channel. According to an exemplary embodiment, the particle count data may include the International Organization for Standardization (ISO) 4406 method for contamination levels of solid particles. In various exemplary embodiments of the present disclosure, the external data processing system 110 of the machine system 100 may be configured to receive raw voltage data, such as aeration detection raw voltage data 132 and particle detection raw voltage data 134, from the internal photodiode of the optical particle sensor 115 rather than relying on any actual particle count output of the sensor from an application (e.g., a fluid system on a heavy machine) where high aeration levels in the hydraulic fluid may render the standard particle count output of the optical particle sensor 115 unsatisfactory or unreliable.

[0035] Systems and methods according to various exemplary alternative embodiments and embodiments of the present disclosure may be configured to calibrate a standard output from a contaminant detection device such as an optical particle sensor 115, rather than processing the raw voltage data obtained from the sensor in the exemplary embodiments and embodiments discussed above. As discussed above, the optical particle sensor 115 is an example of a contaminant detection device configured to determine the level of contamination in a fluid. Various exemplary embodiments of the optical particle sensor 115 may include an analog integrator configured to sum the shadow areas cast by the contaminants in the fluid passing through the sensor passage when a light emitting diode (LED) illuminates the fluid as the fluid passes through the passage and a photodiode generates a voltage signal indicating the area of ​​each shadow. The analog integrator may be configured to integrate the resulting voltage signal over time and output a voltage signal related to the amount of solid contaminant particles and air bubbles in the fluid. However, the optical particle sensor 115 may need to be calibrated to have a high confidence level in the particle count from the sensor when testing a fluid with a high level of air bubbles or aeration, such as a hydraulic fluid in a hydraulic system for heavy machinery.

[0036] An exemplary embodiment of a system for calibrating a contaminant detection device such as an optical particle sensor may include a test reservoir for holding a volume of a fluid to be tested, a light-blocking particle counter, and one or more of the contaminant detection devices fluidly connected in series to the test reservoir and the light-blocking particle counter. Non-limiting examples of light-blocking particle counters may include a HIACPODS+ portable liquid particle counter, a Parker iCount online particle detector, an ArgoHytos OPCount particle counter, and a PAMAS S40 GO portable particle counter.

[0037] Various exemplary embodiments of the system for calibrating a pollutant detection device according to the present disclosure may also include a low-end test dust dilution, wherein the low-end test dust dilution is prepared from a first size test dust grade mixed with a fluid to produce a first test dust mass per volume of the fluid. According to the International Organization for Standardization (ISO) 12103:2016 standard, a test dust produced by a controlled jet milling and / or sieving process to meet a test dust grade such as A.2Fine can be obtained from a source such as Powder Technology, Inc. (PTI). An intermediate test dust dilution may also be prepared from a first size test dust grade mixed with a fluid to produce a second test dust mass per volume of the fluid, wherein the second test dust mass per volume is greater than the first test dust mass per volume. In addition, a high-end test dust dilution may be prepared from a first size test dust grade mixed with a fluid to produce a third test dust mass per volume of the fluid, wherein the third test dust mass per volume is greater than the second test dust mass per volume.

[0038] An exemplary calibration system according to the present disclosure may still further include a pump configured to pump each of the low-end, mid-end, and high-end test dust dilutions from the test reservoir continuously through each of the one or more contaminant detection devices and the particle counter until the particle count measured by the particle counter for each of the consecutive test dust dilutions is stable. The first input mechanism may be configured to set a low-end gain, a mid-end gain, and a high-end gain of each of the one or more contaminant detection devices based on the stable particle count of each of the test dust dilutions using the first size test dust grade.

[0039] An exemplary calibration system according to the present disclosure may also include an aerated bubble counter test dust dilution prepared from a second size test dust grade having a size greater than a first size test dust grade and mixed with a fluid, wherein the second size test dust grade contains at least 90% of the volume containing dust particles belonging to a range of sizes greater than a cutoff size associated with a voltage signal generated by one of the one or more contaminant detection devices indicating the presence of air bubbles contained within the fluid during use of the fluid in heavy machinery. The pump may be configured to pump the aerated bubble counter test dust dilution from the test reservoir through each of the one or more contaminant detection devices and the particle counter until the particle count measured by the particle counter for the aerated bubble counter test dust dilution stabilizes. The second input mechanism may be configured to set a bubble count gain for each of the one or more contaminant detection devices such that each of the devices rejects approximately 70 to 80% or more of the voltage signal generated by the aerated bubble counter test dust dilution pumped through the device.

[0040] An exemplary calibration system may include three of the contaminant detection devices fluidly connected in series downstream of a test reservoir. In some exemplary embodiments of a calibration system according to the present disclosure, the test reservoir may be a conical tank. The first size test dust grade may be ISO 12103-1:2016, A.2 fine test dust. In an exemplary embodiment, the particle count of the low-end test dust dilution may be approximately 160 counts per mL of fluid, and the test dust mass of the low-end test dust dilution may be approximately 1.0 mg (milligrams) / L (liter) of fluid. One of ordinary skill in the art will appreciate that the term "approximately" as used throughout the present disclosure and claims encompasses, for example, a range of counts per mL and mass per volume of test dust within standard industry tolerances for particle counts and mass per volume of the measuring fluid. In an exemplary embodiment, the particle count of the intermediate test dust dilution can be any of approximately 640, 1,300, or 2,500 counts per mL of fluid, respectively, and the test dust mass of the intermediate test dust dilution can be any of approximately 0.414, 0.854, or 3.505 mg (milligrams) / L (liter) of fluid, respectively. In an exemplary embodiment, the particle count of the high-end test dust dilution can be approximately 5,000 counts per mL of fluid, and the test dust mass of the high-end test dust dilution can be approximately 5.0 mg (milligrams) / L (liter) of fluid. The second size test dust grade can be a test dust of a size in the range of approximately 75 to 150 microns in diameter. In an alternative embodiment, the second size test dust grade can be a test dust of a size in the range of approximately 53 to 106 microns in diameter.

[0041] The above exemplary system for calibrating the standard output from a pollutant detection device such as an optical particle sensor 115 may be included as part of a system for early detection of potential operating problems on heavy machinery. Various embodiments of the machine system 100 may include a data collector configured to receive, collect, package and / or distribute data collected by the optical particle sensor 115. For example, the data collector may receive particle count data from the optical particle sensor 115 and calibrate the particle count data according to the above system and method. Alternatively and / or in addition, the data collector may acquire, analyze and process raw voltage data generated by components of the optical particle sensor 115, such as the particle detection raw voltage data 134 and the aeration detection raw voltage data 132 discussed above. The data collector of the machine system 100 may package this data and transmit the received data to a condition monitoring system at a remote, back-end or other recipient via a communication network. Alternatively and / or in addition, the data collector of the machine system 100 may store the received data in a memory for a predetermined period of time for later transmission to the condition monitoring system. For example, if a communication channel between the machine system 100 and a remote condition monitoring system (e.g., a communication network or one or more communication devices associated with the data collector and / or the condition monitoring system) becomes temporarily unavailable, the collected data may be stored in memory for subsequent retrieval and transmission when the communication channel has been restored.

[0042] The communication network may include any network that provides two-way communication between a machine including the machine system 100 and an off-board system (e.g., a condition monitoring system at a remote site). For example, the communication network may communicatively couple the machine to the condition monitoring system across a wireless networking platform, such as a satellite communication system, a cellular communication system, or any other platform for communicating data to one or more geographically dispersed assets (e.g., Bluetooth, microwave, point-to-point wireless, point-to-multipoint wireless, multipoint-to-multipoint wireless). Alternatively, the communication network may also include or embody any suitable wireless and / or wired network, such as Ethernet, fiber optic, waveguide, or any other type of wired communication network. It is also contemplated that the communication network may support additional communication media or communication methods, such as smart card technology, manual data transfer methods (man-powered nets), or any suitable means for transmitting data between a machine including the machine system 100 and an off-board system. The communication network may also include any necessary infrastructure to support message routing and network operations, such as various hardware and software support systems and devices that facilitate the operation of one or more communication services.

[0043] The condition monitoring system as part of the machine system 100 and the processor 140 or coupled to the machine system 100 and the processor 140 in communication may include any type of processor-based system on which the processes and methods according to the disclosed embodiments may be implemented. For example, the condition monitoring system may include one or more hardware and / or software components configured to execute software programs, such as software for monitoring and analyzing data associated with one or more machines. The machine system 100 may include one or more hardware components, such as one or more processors 140, random access memory (RAM) modules, read-only memory (ROM) modules, storage systems, databases, one or more input / output (I / O) devices and interfaces. Alternatively and / or in addition, the machine system 100 may include one or more software components, such as a computer-readable medium containing computer executable instructions for executing methods according to certain disclosed embodiments. It is contemplated that one or more of the hardware components listed above may be implemented using software. For example, a storage device for the machine system 100 may include a software partition associated with one or more other hardware components.

[0044] The processor 140 may be configured to execute instructions and process data to perform one or more functions associated with the machine system 100. The processor 140 may be communicatively coupled to RAM, ROM, storage devices, one or more databases, I / O devices, and interfaces. The processor 140 may be configured to execute a sequence of computer program instructions to perform various processes. The computer program instructions may be loaded into the RAM for execution by the processor 140. The RAM and ROM may each include one or more devices for storing information associated with the operation of the machine system 100, the onboard monitoring and control module 150, and / or the processor 140. For example, the ROM may include a memory device configured to access and store information, including information for identifying, initializing, and monitoring the operation of one or more components and subsystems of the machine. The I / O device may include one or more components configured to communicate information to a user associated with the machine system 100, and to calibrate and make adjustments, such as setting various gains on the optical particle sensor 115 for different ranges of particle sizes to be detected and counted by the sensor. For example, the I / O devices may include a console with an integrated keyboard and mouse to allow a user to input parameters associated with the machine system 100 and the optical particle sensor 115. The I / O devices may also include a display including a graphical user interface (GUI) for outputting information on the monitor. The I / O devices may also include peripheral devices such as a printer for printing information associated with the machine system 100, a user-accessible disk drive (e.g., a USB port, a floppy disk, a CD-ROM or DVD-ROM drive, etc.) to allow a user to input data stored on a portable media device, a microphone, a speaker system, or any other suitable type of interface device.

[0045] The processor 140 of the machine system 100 may include one or more software applications for identifying trends in the amount of solid contaminant particles and air bubbles identified in the tested fluid and predicting future failures of components associated with the machine fluid system. The software application run by the processor 140 may also be configured to notify one or more subscribers (e.g., maintenance personnel, project managers, dispatchers, etc.). For example, the software application associated with the processor 140 may be configured to analyze historical and real-time levels of contaminants, such as the number and / or area of ​​solid contaminant particles and air bubbles, to estimate trends in particle count data. The processor 140 may compare current (e.g., real-time) particle count data with historical trend data. If the current particle count data exceeds a predetermined acceptable amount of the historical particle count trend, the processor 140 may trigger a fault event detection alarm, an indication of the need for maintenance or repair, and / or generate an event notification to be distributed to one or more subscribers.

[0046] The subscriber may include a computer system or mobile data device configured to receive data from the processor 140 in a manner according to the disclosed embodiments. For example, the subscriber may include one or more computer terminals operated by respective users. Alternatively and / or in addition, the subscriber may include a personal data assistant system (PDA), a wireless communication device (e.g., a pager, a telephone, etc.), a notebook computer, a diagnostic computer system, a data analyzer, or any other such client computing device configured to receive and process information. In one embodiment, the subscriber may be associated with one or more parts of a business entity that is associated with managing a remote project site within a project environment. For example, the subscriber may be associated with a specific department of a business entity associated with the project environment, such as a project management department, an operations department, a maintenance and / or repair department, a procurement department, a human resources department, and / or any other business entity that may be associated with the project environment.

[0047] In another embodiment, a subscriber may be associated with an enterprise entity related to one or more groups of machines. For example, a subscriber may be associated with a site manager that controls the operation and productivity of a first group of machines comprising machine system 100. Alternatively and / or in addition, different project entities may be associated with different enterprise entities and / or machines. Thus, the above description is exemplary and is not intended to be limiting. The disclosed embodiments contemplate any correlation (or no correlation at all) between one or more enterprise entities and / or portions thereof and components of a project environment.

[0048] The subscriber may be associated with a business entity related to a particular project environment or work site and may be configured to communicate with one or more processors 140 of the machine system 100. In one embodiment, the subscriber may transmit operational data to and receive operational data from the processors 140 associated with one or more machines operating within the particular project environment. For example, the subscriber may be a field maintenance and repair department that receives component failure warnings (and / or particle count or pressure data associated with potential failure trends) associated with one or more machines from the one or more processors 140.

[0049] The subscribers may also include portable communication devices associated with one or more personnel associated with the project environment or work site. For example, the subscribers may include wireless pagers or cell phones associated with project managers, machine operators, dispatchers, maintenance technicians, shift dispatchers, or machine owners. Thus, the subscribers may receive fault event alerts and warnings from the processor 140 and schedule the associated machine for inspection and / or maintenance before a predicted fault event occurs.

[0050] The processes and methods according to the disclosed embodiments provide a system for early detection of component failures associated with one or more components within a fluid flow path. More specifically, the system described herein provides a system for monitoring particle count data trends of a fluid flowing in a fluid channel. If the current data deviates from the monitored trend threshold amount, potential future failure events can be identified and the machine can be marked for investigation, testing and / or repair. As an alternative or additional (i.e., redundant) embodiment, the system described herein can monitor pressure at one or more locations along the fluid channel and identify trends in the pressure data. If the current pressure data deviates from the pressure trend, indicating a potential accumulation of contaminants and debris associated with a component failure, the system can notify a subscriber to report the potential problem.

[0051] The processor 140 of the machine system 100 may also be configured to utilize the particle count data to plan routine maintenance associated with the machines comprising the machine system 100. For example, because some machines may operate in harsher and / or more severe environments, each machine may require a different maintenance schedule for hydraulic and / or other fluid systems. More specifically, engine and / or transmission lubrication and / or cooling systems associated with machines operating in harsh environments may require more frequent maintenance to offset increases in particulate matter caused by normal wear compared to machines operating in less harsh conditions. Thus, the processor 140 may be adapted to determine when wear debris has reached a level requiring system maintenance, rather than prescribing regular maintenance at set intervals.

[0052] While nominal increases in particle count trends indicate normal "wear and tear," significant deviations from these trends may indicate material failure of one or more components in the system. Thus, as processor 140 receives current ("real-time" or near "real-time") particle count data from different machines, one or more processors 140 of various machine systems 100 may compare the current data to historical particle count trends. If the current particle count data does not deviate from the historical component count trend by a threshold amount, processor 140 may continue to monitor the current particle count data.

[0053] The threshold level for characterizing the deviation of current particle count data relative to historical particle count data may be predetermined, for example, based on experimental test data collected during a fault test analysis associated with a component of a fluid system. Alternatively, the threshold level may be determined by estimating the increase in particle discharge associated with certain "minor" failure events that may occur before a complete failure of the component. For example, a pump impeller bearing may begin to decompose some time before a complete pump failure. Thus, as the impeller bearing deteriorates, particle debris may be discharged into the fluid passage. As the pump impeller condition gradually degrades, additional material may be introduced into the fluid. These initial "alarm signs" may be viewed as an abnormal increase in particle counts in addition to normal "wear" particle count data. Therefore, the threshold level for an acceptable increase in historical particle count data may be determined to be a predetermined percentage above the historical increase. Thus, if the historical trend indicates that the particle count data has increased by 11 / 2% to 21 / 2% for every 10 hours of engine operation, the threshold for characterizing the deviation of the current data from the historical trend data may be determined to be, for example, 4%.

[0054] It is contemplated that additional and / or different methods may be used to determine acceptable thresholds for identifying failure events based on particle count data. For example, if during monitoring of a machine during a catastrophic failure event, the particle count data increases by 10% over historical trends, then the threshold level may be set to a level less than 10%. Those skilled in the art will recognize that the lower this level is set, the earlier the failure event may be predicted, which may also increase the number of false component failure predictions.

[0055] If the current particle count data deviates from the historical particle count trend by a threshold amount, the processor 140 of the machine system 100 may predict a component failure event. Because components often fail very quickly from the onset of certain failure symptoms, the processor 140 may be configured to immediately generate an event notification via the onboard monitoring and control module 150. This event notification may include an audible and / or visual alarm in the cab of the machine, notifying the operator of the potential component failure condition.

[0056] In addition to generating an onboard machine alert, the processor 140 and / or the onboard monitoring and control module 150 may also be configured to generate an electronic message, such as a text message, an email message, a page, a short message service (SMS) message, an automated voice message, a voicemail message, or any other suitable electronic message. The processor 140 and the onboard monitoring and control module 150 may transmit this message to one or more subscribers. Thus, the subscribers may be able to take appropriate actions to reduce the impact of the component failure. For example, a project manager or dispatcher may schedule immediate maintenance at a maintenance facility and arrange for a replacement rental machine to prevent a drop in productivity during the maintenance period.

[0057] Optionally, upon predicting a catastrophic failure associated with a machine, the processor 140 and the onboard monitoring and control module 150 may generate a control signal to shut down the machine before a failure event occurs. This may be particularly advantageous in fluid systems where the failure of one component may cause significant collateral damage to the machine or one or more critical components associated therewith. For example, if the processor 140 predicts a component failure associated with a fluid cooling system of an engine, the processor 140 may generate a command signal to shut down the engine to prevent engine overheating and damage associated therewith.

[0058] Industrial Applicability

[0059] The systems and methods according to the disclosed embodiments provide a solution for obtaining accurate and reliable particle counts using raw voltage data associated with the number of solid contaminant particles and air bubbles in the tested fluid to provide early detection of trends that may lead to failure or required maintenance of components associated with the machine fluid system. A machine employing the currently disclosed system and associated method can be configured to detect and monitor the number and size of solid contaminant particles and air bubbles or aeration present in a fluid flowing through a fluid channel. The systems and methods described herein can also detect trends in contamination levels in various fluids (e.g., hydraulic fluids used in heavy machinery) and predict potential future failures or maintenance intervals of one or more components of a machine affected by the fluid. Therefore, a working environment implementing the currently disclosed system for early detection of machine component failures can enable project managers, machine operators, and equipment owners to preemptively identify and repair damaged components before catastrophic component failures that may disable the machine and have a significant impact on the productivity of the working environment.

[0060] Figure 4 An exemplary embodiment of a method for calibrating an optical particle sensor or other contaminant detection device according to various embodiments of the present disclosure is described. Figure 4 As shown in , at step 420, one or more contaminant detection devices 115 can be fluidly connected in series to the test reservoir and the light-blocking particle counter. At step 422, a low-end test dust dilution can be prepared from a first size test dust grade mixed with a fluid to produce a first test dust mass per volume. At step 424, an intermediate test dust dilution can be prepared from a first size test dust grade mixed with a fluid to produce a second test dust mass per volume, wherein the second test dust mass per volume is greater than the first test dust mass per volume. At step 426, a high-end test dust dilution can also be prepared from a first size test dust grade mixed with a fluid to produce a third test dust mass per volume, wherein the third test dust mass per volume is greater than the second test dust mass per volume.

[0061] At step 428, after preparing each of the test dust dilutions described above, each test dust dilution may be continuously pumped through each of the contaminant detection devices serially fluidly connected to the test reservoir until the particle counts of each of the test dust dilutions are stabilized. At step 430, after each particle count of each of the test dust dilutions is stabilized, an input device may be used to set a low-end gain, a mid-gain, and a high-end gain of each of the contaminant detection devices based on the stabilized particle counts.

[0062] In order to calibrate the contaminant detection devices so that unacceptable particle count errors caused by the presence of air bubbles are eliminated or suppressed, at step 432, an aerated bubble counter test dust dilution may also be prepared from a second size test dust grade that is larger in size than the first size test dust grade and contains a majority volume percentage of test dust particle sizes larger than a cutoff particle size associated with air bubbles. At step 434, an input device may be used to set the bubble count gain of each contaminant detection device so that each contaminant detection device, such as the optical particle sensor 115, exhausts approximately 70 to 80% or more of the particle counts larger than the cutoff particle size as air bubbles.

[0063] Although the disclosed embodiments are described and illustrated as being associated with fluid systems for heavy machinery, they may be implemented in any mobile or stationary machine that includes at least one fluid delivery system. Specifically, the presently disclosed systems and methods for calibrating a contaminant detection device or for analyzing and processing raw voltage data generated by a contaminant detection device that indicates the number or area of ​​solid contaminant particles and air bubbles can enhance early detection of machine component failures. The disclosed systems and methods can be used in any machine or equipment system where monitoring the amount of contaminants or debris in a fluid may be advantageous, particularly in systems where the circulation of debris or high levels of contamination or air bubbles may potentially damage components of the machine or restrict fluid flow necessary to ensure proper operation of the machine. In addition, the presently disclosed systems and associated methods can be integrated with a connected work site environment that monitors, analyzes, and manages the operation of multiple machines to determine efficient operation of the work site.

[0064] The currently disclosed systems and methods for early detection of machine component failures can have several advantages. For example, the currently disclosed systems employ contaminant detection devices that directly monitor the area of ​​solid contaminant particles and air bubbles present in the fluid, which directly detects the presence of faulty debris or the amount of contaminants that indicate that the component is about to fail. In contrast, conventional systems use indirect methods to detect faulty debris (e.g., by monitoring the pressure drop across a fluid filter). However, such indirect methods may not respond immediately to an increase in faulty debris (e.g., until the fluid filter becomes clogged), thereby potentially delaying the detection of the fault condition, which may result in further damage to the machine.

[0065] It will be appreciated by those skilled in the art that various modifications and changes may be made to the disclosed systems and methods without departing from the scope of the present disclosure. Other embodiments of the present disclosure will be apparent to those skilled in the art by consideration of the specification and practice of the present disclosure. It is intended that the specification and examples be considered exemplary only, with the true scope of the present disclosure being indicated by the following claims and their equivalents.

Claims

1. A method for processing data related to the amount of contaminants in a fluid, wherein the data is obtained from a contaminant detection device (115), the contaminant detection device being configured to determine the level of contamination in the fluid using an analog integrator, the analog integrator summing the areas of shadows cast by contaminants in the fluid passing through the passage when a light emitting diode (LED) is used to illuminate the fluid in the passage and a plurality of photodiodes are used to generate a voltage signal indicative of the area of ​​each shadow, wherein the analog integrator integrates the resulting voltage signal over time, the method comprising: receiving an aeration detection raw voltage data output from at least one of the photodiodes of the pollutant detection device at an external data processing system separate from and independent of the pollutant detection device; receiving, at the external data processing system, a particle detection raw voltage data output from at least another one of the photodiodes of the contaminant detection device; analyzing and cleaning the aeration detection raw voltage data output and the particle detection raw voltage data output to determine real-time information about the amount of air bubbles and solid contaminant particles in the fluid; supplying the determined amount of solid contaminant particles to an onboard monitoring and control module of the machine; as well as controlling one or more operations on the machine based on the determined amount of solid contaminant particles, The analysis and cleaning of the aeration detection raw voltage data output and the particle detection raw voltage data output include: averaging the aeration detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the particle detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the aeration detection raw voltage data output over time according to a slow exponentially weighted moving average; and The particle detection raw voltage data output over time is averaged according to a slow exponentially weighted moving average.

2. The method of claim 1, wherein the one or more operations comprise: Generates onboard machine alerts; generating an electronic message; and The electronic message is transmitted to one or more subscribers to enable the one or more subscribers to take action to minimize any effect of the amount of the solid contaminant particles on the machine.

3. The method according to claim 1, comprising determining whether the amount of air bubbles has exceeded a threshold level, when the accuracy of the determined amount of solid contaminant particles may be reduced to a level that should be ignored. 4 . The method of claim 1 , comprising determining whether any of the raw voltage data falls outside of a predetermined acceptable diagnostic range for voltage. 5 . The method of claim 4 , wherein the acceptable diagnostic range of voltage is greater than or equal to 0.45 volts and less than or equal to 4.75 volts.

6. The method according to any one of claims 1 to 5, further comprising determining a difference between the fast exponentially weighted moving average and the slow exponentially weighted moving average for each of the aeration detection raw voltage data output over time and the particle detection raw voltage data output over time; determining whether each of the differences is positive, indicating a rising signal, or whether each of the differences is negative, indicating a falling signal; Determining whether the rising signal is less than a rising rate threshold, and whether the falling signal is greater than a decay rate threshold; as well as When the difference is greater than the decay rate threshold and less than the rise rate threshold, the raw voltage data output is determined to be a clean and acceptable data output for further processing and action by the onboard monitoring and control module of the machine.

7. The method according to claim 6, further comprising: selecting the aeration detection raw voltage data output according to a predetermined de-jitter time, wherein the predetermined de-jitter time is designed to eliminate multiple fluctuation state transitions of the aeration detection raw voltage data; and The particle detection raw voltage data output is selected according to another predetermined debounce time, the other predetermined debounce time being designed to eliminate multiple fluctuating state transitions of the particle detection raw voltage data output.

8. The method according to claim 7, further comprising: performing short-term averaging of the aeration detection raw voltage data output received from the pollutant detection device over a first time period to determine a short-term aeration detection average value; performing short-term averaging of the particle detection raw voltage data output received from the contaminant detection device during the first time period to determine a short-term particle detection average; binning the short-term aeration detection averages over a second time period longer than the first time period and classifying the results as falling within one of a poor, acceptable, or good category for further processing and action by the onboard monitoring and control module of the machine; as well as The short term particle detection average is compared to a lookup table to determine whether the particle detection raw voltage data output falls within one of a poor, acceptable or good category based on the aeration detection raw voltage data output for further processing and action by the onboard monitoring and control module of the machine.

9. A system for processing data related to the amount of contaminants in a fluid, wherein the data is obtained from a contaminant detection device, the system comprising: an external data processing system, which is separate and independent from the contaminant detection device and is configured to: receiving aeration detection raw voltage data output from the pollutant detection device; receiving particle detection raw voltage data output from the pollutant detection device; analyzing and cleaning the aeration detection raw voltage data output and the particle detection raw voltage data output to determine real-time information about the amount of air bubbles and solid contaminant particles in the fluid; supplying the determined amount of solid contaminant particles to an onboard monitoring and control module of the machine; as well as controlling one or more operations on the machine based on the determined amount of solid contaminant particles, The analyzing and cleaning the aeration detection raw voltage data output and the particle detection raw voltage data output comprises: averaging the aeration detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the particle detection raw voltage data output over time according to a fast exponentially weighted moving average; averaging the aeration detection raw voltage data output over time according to a slow exponentially weighted moving average; and The particle detection raw voltage data output over time is averaged according to a slow exponentially weighted moving average.

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