BR112023012243B1 - MONITORING SYSTEM FOR A PIVOT IRRIGATION SYSTEM, COMPUTER IMPLEMENTED METHOD FOR MONITORING A PIVOT IRRIGATION SYSTEM AND NON-TRANSIENT COMPUTER READABLE MEDIA - Google Patents

Abstract

CONDITION-BASED IRRIGATION MONITORING. It is a monitoring system for an irrigation system. The irrigation system includes a pivot and a movable end gun operatively associated with the pivot. The monitoring system includes a sensor configured to generate an electrical signal indicative of movement and/or positioning of the moving end gun relative to the pivot over time, a processor, and a memory. The memory includes instructions stored therein, which when executed by the processor induce the system to: receive the generated electrical signal, determine whether the moving end gun, one or more components thereof, require maintenance based on the electrical signal, and determine when the moving end gun is in an on and/or off activation state based on the electrical signal.

Description

REFERENCE TO RELATED DEPOSIT REQUESTS

[001] The present application claims the benefit of the Provisional Patent Application in U.S. 63/129,799, filed on December 23, 2020, its contents being incorporated into the present document in its entirety by way of reference.

FIELD OF TECHNIQUE

[002] The present disclosure relates to irrigation systems and, more particularly, to structures and methods for performing predictive maintenance of irrigation systems.

FUNDAMENTALS

[003] Irrigation systems such as pivots, lateral movement systems, drip irrigation systems, etc. They break on average three times a year after 40 uses. These breaks occur during critical growth stages and, in many cases, in the middle of the field.

SUMMARY

[004] In order to limit delays, increased costs and other problems associated with irrigation system breakdowns, this disclosure details a solution that includes digital observation of the irrigation system during normal operation and defines parameters that indicate abnormal operation. To observe these operational anomalies, sensors can be added to the irrigation system to provide data for algorithms to process. These algorithms can be based on logic or analysis. Existing operational data from those out of the box can be used in some cases. In aspects, other data sources may be external to the system, such as National Oceanic and Atmospheric Administration (NOAA) weather, topographic maps, soil moisture, etc., or combinations thereof.

[005] According to one aspect, a monitoring system for an irrigation system is presented. The irrigation system includes a movable end gun operatively associated with a portion of the irrigation system. The monitoring system includes a sensor configured to couple to the tail gun and configured to generate an electrical signal indicative of the movement and/or positioning of the tail gun relative to the portion of the irrigation system over time, a processor and memory. The memory includes instructions stored therein, which when executed by the processor, induce the system to: receive the generated electrical signal, determine whether the moving end gun, or one or more components thereof, require maintenance based on the electrical signal, and determine when the trailing end gun is in an on and/or off activation state based on the electrical signal.

[006] In another aspect of the present disclosure, the instructions, when executed, can further induce the system to determine an angular rate of the moving end gun and the time spent moving forward and/or backward based on the signal electric.

[007] In yet another aspect of the present disclosure, the instructions, when executed, may further induce the system to generate a report based on the determinations.

[008] In a further aspect of the present disclosure, the instructions, when executed, may further induce the monitoring system to determine whether the end gun pivots beyond a predetermined number of degrees without an end gun in a state of activation and provide an indication to a user that a location has not been irrigated based on the determination.

[009] In yet a further aspect of the present disclosure, wherein the portion of the irrigation system includes at least one of a side actuator, a water nozzle or a pivot, and the movable end gun may be movably mounted on the pivot.

[010] In another aspect of the present disclosure, the movable end gun may be part of the same system, but separate from the irrigation system portion.

[011] In yet another aspect of the present disclosure, the system may further include an analysis mechanism configured to perform the determinations.

[012] In a further aspect of the present disclosure, the instructions, when executed by the processor, may further induce the monitoring system to receive data from at least one of a weather station, a field soil moisture sensor , a terrain and soil map, a temperature sensor, or weather from the National Oceanic and Atmospheric Administration.

[013] In yet a further aspect of the present disclosure, the analysis mechanism may include a machine learning model, and wherein the machine learning model is based on a deep learning network, a classic machine learning model or combinations thereof.

[014] In another aspect of the present disclosure, the sensor may include an encoder, a pressure sensor, a flow meter, a magnetometer, a gyroscope, an accelerometer, a camera, a gesture sensor, a microphone, a rangefinder and laser, a reed switch, a magnetic switch, a GPS and/or an optical switch.

[015] In one aspect of the present disclosure, a computer-implemented method for monitoring an irrigation system including four end gun zones is disclosed. Each end gun zone includes a movable end gun operatively associated with a portion of the irrigation system. The method includes receiving an electrical signal generated by a sensor configured to couple to the movable end gun, wherein the electrical signal is indicative of movement and/or positioning of the movable end gun relative to the portion of the irrigation system over time. time, determine whether the trailing end gun, or one or more components thereof, requires maintenance based on the electrical signal, and determine when the trailing end gun is in an on and/or off activation state based on the electrical signal .

[016] In yet another aspect of the present disclosure, the method may further include determining an angular rate of the moving end gun and the time spent moving forward and/or backward based on the electrical signal.

[017] In a further aspect of the present disclosure, the method may further include generating a report based on the determinations.

[018] In yet a further aspect of the present disclosure, the method may further include determining whether the end gun pivots beyond a predetermined number of degrees without an end gun in an activated state and providing an indication to an user that a location has not been irrigated based on the determination.

[019] In another aspect of the present disclosure, wherein the portion of the irrigation system includes at least one of a side actuator, a water nozzle, or a pivot, and the movable end gun may be movably mounted on the pivot .

[020] In yet another aspect of the present disclosure, the movable end gun may be part of the same system, but separate from the irrigation system portion.

[021] In a further aspect of the present disclosure, the method may further include making determinations by an analysis mechanism.

[022] In yet a further aspect of the present disclosure, the method may further include receiving data from at least one of a weather station, a field soil moisture sensor, a terrain and soil map, a temperature, or climate from the National Oceanic and Atmospheric Administration.

[023] In yet another aspect of the present disclosure, the analysis engine includes a machine learning model, and wherein the machine learning model is based on a deep learning network, a classical machine learning model, or combinations thereof.

[024] In one aspect of the present disclosure, a non-transitory computer-readable medium stores instructions that, when executed by a processor, induces the processor to execute a method for monitoring an irrigation system. The irrigation system includes a plurality of end gun zones. Each end gun zone of the plurality of end gun zones includes a movable end gun operatively associated with a portion of the irrigation system. The method includes receiving an electrical signal generated by a sensor configured to couple to the moving end gun, wherein the electrical signal is indicative of movement and/or positioning of the moving end gun relative to the portion of the irrigation system as it passes. of time, determine whether the trailing end gun, one or more components thereof, require maintenance based on the electrical signal, and determine when the trailing end gun is in an on and/or off activation state based on the electrical signal .

[025] Other aspects, features and advantages will become apparent from the following description, drawings and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[026] The attached drawings, which are incorporated and constitute a part of this specification, illustrate aspects of the disclosure and, together with a general description of the disclosure provided below and the detailed description given below, serve, to explain the principles of this disclosure, in what:

[027] Figure 1 is a diagram illustrating a monitoring system;

[028] Figure 2 is a block diagram of a controller configured for use with the predictive maintenance system of Figure 1;

[029] Figure 3 is a diagram illustrating a machine learning model configured for use with the predictive maintenance system of Figure 1;

[030] Figure 4A illustrates a flowchart exemplifying a typical agricultural operation;

[031] Figure 4B illustrates an exemplary flowchart of an agricultural operation including a predictive maintenance system in accordance with the principles of this disclosure;

[032] Figure 5 illustrates a data science workflow with various models of the predictive maintenance system illustrated in Figure 1;

[033] Figures 6 to 8 are diagrams of the exemplary hardware interface and instrumentation of the predictive maintenance system of Figure 1;

[034] Figure 9 is a perspective view of a portion of an exemplary pivot of the predictive maintenance system of Figure 1;

[035] Figure 10 is a perspective view of a portion of the air compressor instrumentation of another pivot exemplifying the predictive maintenance system of Figure 1;

[036] Figure 11A is a perspective view of an end gun assembly of the predictive maintenance system in accordance with the principles of this disclosure;

[037] Figure 11B is a side view of the end gun assembly of Figure 11A;

[038] Figures 12A-12C are exemplary signals generated with the predictive maintenance system of Figure 11A;

[039] Figures 13A and 13B illustrate exemplary flowcharts of the predictive maintenance system including end gun monitoring in accordance with the principles of this disclosure;

[040] Figure 14 illustrates a data science workflow exemplifying the predictive maintenance systems of this disclosure;

[041] Figures 15 to 17 are illustrative flowcharts for test systems of the predictive maintenance systems of this disclosure;

[042] Figure 18 is an illustrative model for predicting tip gun performance using an inertial measurement unit with nine degrees of freedom;

[043] Figures 19 to 21 are high-level block diagrams for a monitoring system according to the disclosure;

[044] Figure 22 is a state machine diagram for the system of Figure 27 according to the disclosure;

[045] Figure 23 is a quadrant diagram of the end gun in a field being used to determine irrigation failure locations, in accordance with the disclosure;

[046] Figure 24 is an exemplary gyroscope signal output graph for the monitoring system of Figure 1, in accordance with the disclosure;

[047] Figure 25 is an exemplary compass signal output graph for the monitoring system of Figure 1, in accordance with the disclosure; It is

[048] Figure 26 is an exemplary accelerometer signal output graph for the monitoring system of Figure 1, in accordance with the disclosure.

DETAILED DESCRIPTION

[049] Aspects of the disclosed predictive maintenance systems will be described in detail with reference to the drawings, where similar numerical references designate identical or corresponding elements in each of the various views. Directional terms such as upper, lower, and the like are used simply for convenience of description and are not intended to limit the disclosure herein appended. Likewise, depending on the usage in question, the term “on” includes being in an open or activated position, while the term “off” includes being in a closed or inactive position.

[050] In the previous description, well-known functions or constructions are not described in detail to avoid obscuring the present disclosure in unnecessary details.

[051] Advantageously, the disclosed system predicts common unexpected downtime versus notification that occurred after the fact. The revealed system provides better perception than a team circulating to observe the operation (which can be subjective). Current technology only notifies failures after they have occurred, while the revealed system foresees a maintenance requirement before the failure occurs.

[052] With reference to Figures 1 and 7 to 9, a monitoring system 100 is provided for an irrigation system (for agriculture, mining, etc.). In general, the monitoring system 100 includes an irrigation system 106 and a controller 200 configured to execute instructions that control the operation of the pivot monitoring system 100. The irrigation system 106 may include a pump 10 (e.g., a compressor or booster pump, see Figure 11), a pivot 20, one or more turrets 30, an end turret 40, a corner turret 50, an air compressor 60, and an end gun 70 (also known as a gun large volume gun and/or movable nozzle). The pump 10 may include one or more current sensors and a wireless communication device 104 configured to remotely transmit data to the controller 200 (e.g., current data captured). Pivot 20 may include one or more sensors 102 and a wireless communication device 104 configured to remotely transmit data to controller 200. Each tower 30, corner tower 50, and end tower 40 may include one or more sensors 102 and a device wireless communication device 104 configured to transmit data remotely to controller 200. The wireless communication device may include, for example, 3G, LTE, 4G, 5G, Bluetooth, and/or Wi-Fi, etc. The sensors 102 may include at least one of a current sensor, a voltage sensor, and/or a power sensor configured to sense, for example, current, voltage, and/or power, respectively. In aspects, these sensors 102 can measure the transmission of electricity in a booster pump motor 10 when part of the system. Pump 10 may include transmission lines in the crosshead; a contactor; and components used to actuate the contactor, motor components including electrical components, mechanical components and pump components including impeller, inlet, outlet and/or piping. In aspects, the pump 10 may include a flow sensor (not shown) at the outlet of the booster pump.

[053] In aspects, one or more sensors 102 may include any suitable sensors such as, for example, an encoder (e.g., an angular encoder), pressure sensor, flow meter, etc., or combinations thereof. An angular encoder may be in the form of a position sensor that measures the angular position of a rotation axis.

[054] In aspects, one or more sensors may be connected (e.g., directly) and/or may be stand-alone components that may be connected via a wide area network (WAN). In aspects, one or more sensors may be aggregated in the cloud based on provisioning adjustments. In aspects, the one or more sensors may include, for example, a low power wide area network (LPWAN) technology that may be long range (LoRa).

[055] In aspects, the controller 200 may determine changes in the condition of at least one component based on comparing the generated signal to predetermined data.

[056] Controller 200 is configured to receive data from sensors 102 as well as from external data sources such as weather stations 82, field soil moisture sensors 86, terrain and soil maps 88, temperature sensors 89 , and/or National Oceanic and Atmospheric Administration (NOAA) weather 84 to perform and/or refine forecasts indicative of a condition of at least one component (e.g., a pivot 20, an end gun 70, a turret 30, etc. .) of the plurality of components of the irrigation system 106. This prediction allows the controller 200 to determine changes in the condition of at least one component and to predict maintenance requirements of at least one component based on predetermined data (e.g., historical data). . For example, the prediction may be based on comparing determined changes in the condition of at least one component of the irrigation system 106 to predetermined data. For example, the sensor 102 of a tower 30 may capture the typical current absorption of the tower 30. The captured current absorption may then be compared by the controller 200 to the historical and/or typical tower current absorption. The controller may determine that the current absorption drawn from this tower 30 is considerably greater than the historical current absorption by a predetermined number (e.g., about 30%) for a particular set of conditions (sunny day, dry ground, etc. ). Based on this determination, controller 200 may predict that tower 30 needs maintenance. Additionally, the specific type of maintenance may be able to be predicted. For example, if the motor current of a 30 tower is high, this may indicate a flat tire. The pivot monitoring system 100 may additionally or alternatively predict the number of hours typically taken to repair such an occurrence. In another example, the system may sense, through sensor 102, that the current in a pump 10 is low, and correspondingly predict that there is a failure in a pump 10. In one example, a terrain map identifies whether the pivot 20 is angled downward, thereby increasing the pressure in the end gun 70, which facilitates a determination of why the pressure is higher in that particular area.

[057] Data from external data sources can be used to improve model predictions. For example, by processing data such as increased power usage for 30 tower motors because the field is muddy from recent rain, this processed data can be used to improve model predictions. The pivot monitoring system 100 can display field maps for terrain, soil types, etc., which help the model explain variation in energy use. The predictions may be transmitted to a user device 120 by the controller 200 for display and/or further analysis.

[058] In aspects, the data and/or predictions may be processed by a data visualization system 110. Data visualization is the graphical representation of information and data. Using visual elements such as tables, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

[059] In aspects, the pivot monitoring system 100 can be implemented in the cloud. For example, Linux, which can run a Python script, for example, can be used to perform prediction.

[060] Figure 2 illustrates that the controller 200 includes a processor 220 connected to a computer-readable storage medium or a memory 230. The computer-readable storage medium or memory 230 may be a volatile type of memory, e.g. RAM, or a non-volatile type of memory, e.g. flash media, disk media, etc. In various aspects of the disclosure, the processor 220 may be another type of processor, such as a digital signal processor, a microprocessor, an ASIC, a graphics processing unit (GPU), a field programmable gate array (FPGA), or a central processing unit (CPU). In certain aspects of the disclosure, network inference can also be performed on systems that have implemented weights such as memristors, chemically, or other inference computations, as opposed to processors.

[061] In aspects of the disclosure, memory 230 may be a random access memory, a read-only memory, a magnetic disk memory, solid state memory, an optical floppy disk memory, and/or another type of memory. In some aspects of the disclosure, the memory 230 may be separate from the controller 200 and may communicate with the processor 220 via communication buses on a circuit board and/or via communication cables such as serial ATA cables or other types of pipes. . Memory 230 includes computer-readable instructions that are executable by processor 220 for operating controller 200. In other aspects of the disclosure, controller 200 may include a network interface 240 for communicating with other computers or to a server. A storage device 210 can be used to store data.

[062] The disclosed method may run on the controller 200 or on a user device, including, for example, on a mobile device, an IoT device, or a server system.

[063] In aspects, an analysis engine (e.g., a machine learning model, and/or classical analysis) may be configured to perform the determinations.

[064] Figure 3 illustrates a machine learning model 300 and data flow\storage\feedback of the pivot predictive maintenance system. The machine learning model 300 may be hosted on pivot 20 or in the cloud (e.g., a remote server). The machine learning model 300 may include one or more convolutional neural networks (CNN).

[065] In machine learning, a convolutional neural network (CNN) is a class of artificial neural network (ANN), most commonly applied to analyze a set of visual images. The convolutional aspect of a CNN refers to applying matrix processing operations to localized portions of an image, and the results of these operations (which can involve dozens of different parallel and serial calculations) are sets of many features that are used to train neural networks. . A CNN typically includes convolution layers, activation function layers, and pooling layers (typically max pooling) to reduce dimensionality without losing too many features. Additional information can be included in the operations that generate these resources. Providing unique information that produces features that provide neural network information can ultimately be used to provide an aggregated way to differentiate between data fed into neural networks. In aspects, the machine learning model 300 may include a combination of one or more deep learning networks (e.g., a CNN), and classical machine learning models (e.g., an SVM, a decision tree, etc. .). For example, machine learning model 300 may include two deep learning networks.

[066] In aspects, two labeling methods for the training data can be used, one based on a connection to a computer maintenance system (CMMS) and one based on user input. In aspects, the user may be prompted to label data, or may provide the data manually (e.g., at the time of service events).

[067] As noted previously, Figure 4A illustrates a flowchart exemplifying a typical agricultural operation 400a. In general, in step 410, pre-season maintenance is performed on the irrigation equipment. Next, in step 420, the irrigation equipment is used in season. In step 440, if it is determined that the equipment has broken down, it is sent for repair in step 430. At the end of the season (step 450), post-season maintenance is performed (step 460).

[068] Figure 4B illustrates an exemplary flowchart 400b of an agricultural operation that includes a monitoring system 100 in accordance with the principles of this disclosure. In general, in step 410, pre-season maintenance is performed on the irrigation equipment. Next, the monitoring system 100 predicts whether maintenance is required for a particular piece of irrigation equipment. If maintenance is anticipated in step 415, then in step 430 the equipment is examined and repaired. Next, in step 420, the irrigation equipment is used in season. In step 440, if it is determined that the equipment has broken down, the equipment is sent for repair in step 430. At the end of the season (step 450), post-season maintenance is performed (step 460).

[069] Figure 5 illustrates a data science workflow with various models of the predictive maintenance system illustrated in Figure 4B.

[070] The five models include an end gun prediction model 502, a turret drive prediction model 504, a sequencing prediction model 506, an air compression prediction model 508, and an electrical prediction model. 510. Models can be implemented through logic and/or machine learning.

[071] With reference to Figures 5 and 18, an end gun prediction model 502 is shown. The end gun prediction model can count the number of times it takes the end gun 70 (Figure 1) to move from left to right and back. The expected time to go from left to right may be based on pressure, bearing condition, traction, etc., or combinations thereof.

[072] The end gun prediction model 502 may consider expected horsepower based on soil moisture directly measured or inferred from field weather data or regional weather stations, topographic maps, soil maps, engine RPM, gearbox ratio, turret weight, crossmember weight, operating condition, etc., or combinations thereof. The end gun 70 includes instrumentation that can measure each cycle using a proximity switch, encoder, capacitance, and/or imaging system. Aspects of the monitoring system 100 may be mounted or dismounted to the irrigation system 106, for example, a moisture sensor that records when the sensor unit is wetted remotely by water being distributed to the field. If an electronic gun is used, the power usage and duty cycle may be used. In aspects, the one or more sensors may include any suitable sensors such as, for example, an encoder (e.g., angular), pressure sensor, flow meter, magnetometer, gyroscope, accelerometer, camera, gesture sensor, microphone, rangefinder, laser, reed/magnetic/optical switch, etc., or combinations thereof. The end gun prediction model 502 may also include as inputs pump pressure, end gun model number, end gun nozzle diameter, drive arm spring configuration, diffuser type , a flow measurement, a drive arm spring factor K, a drive arm balance, a drive arm bearing condition, a base bearing condition, a base seal condition, an arm alignment drive, and/or a mounting base rigidity (Figure 18). The nozzle type can be inferred from a measured flow and measured pressure. In aspects, the butt gun prediction model 502 (Figure 18) can predict a drive arm impact frequency, a drive arm impact acceleration magnitude, a forward angular rate, a rear angular rate, a of fore and aft heading change, a time per pass, and/or a time to turn a reversing lever. Model outputs can be used to further predict abnormal operation.

[073] Abnormal operation of the end gun may also be based on the movement and/or positioning of the movable end gun 70 relative to the pivot 20 (and/or another portion of the irrigation system, such as a side actuator, a water splash, etc.) over time. For example, the pivot monitoring system 100 may monitor the frequency of the drive arm using an accelerometer and/or gyroscope, and/or the change of direction of the end gun 70 may be determined by a magnetometer. End gun 70 can typically be “on” for about fifteen degrees of rotation from the time it is turned on until the time it is stopped. The sensor 102 may sense that the end gun 70 was on for about three degrees of rotation and the controller may determine that this was an abnormal operation and that the end gun 70 may need maintenance. In aspects, the logic for determining abnormal operation may be based on a sliding window for seconds, minutes, hours, days, and/or years. In aspects, there is an end gun 70 in displacement without a cross member known as water nozzles. The technology revealed also applies to water nozzles in lateral movement irrigation systems. In aspects, a movable end gun 70 may be disposed in the water nozzle. In some examples, a nozzle of water moves over tires, in other examples, the end gun 70 moves rotationally by the drive arm, or a gear energized by water flow. In one example, a water nozzle may be powered by another vehicle such as a tractor or truck. In another example, the water nozzle may be drawn by a flexible water pipe along a path through a coil. In yet another example, the end gun 70 can be directly mounted on a truck to keep dust in a mine, and/or apply waste water to a field. In another example, the end gun 70 may not be pivot-mounted, but preferably mounted on a boom, and/or a regular last drive unit in the pivot-style irrigation system.

[074] Monitoring output parameters such as timing, flow, and/or pressure from the end gun 70 can also help infer the health of the air compressor. In aspects, abnormal operation may further be determined by the water pressure and/or volume from the end gun 70.

[075] For example, if a user (e.g., a farmer) is applying too much pressure to the end gun 70, water and fertilizer can be thrown into the crop, leading to dry rings. The pressure sensor could pick up that the end gun pressure was dropping to about 40 psi from a normal 71 psi. The 502 End Gun Prediction Model can predict that the system is operating abnormally based on pressure measurement over time. The pressure may have initially been high, and then dropped about 10 psi over the next hour. The farmer may have been operating at too high a pressure because the booster pump was dropping and restarting frequently. Restarting the pump is quite detrimental to the health of the 106 irrigation system, as the pump can wear out the electrical components much faster than its rated life.

Electrical instrumentation:

[076] The system can also monitor contactors, switching rings, motor windings, electrical connections and/or wiring faults. Monitoring electrical transients or power metrics like THD, Power Factor, current balance can help infer the health of the electrical system.

[077] Monitoring the component temperatures listed above can also help infer the health of the electrical system.

[078] Referring to Figures 11A, 11B and 12A-12C, the movable end gun 70 supports an electronics housing 1110 that supports at least one sensor 1120 including an accelerometer, a gyroscope, a microphone, a pressure sensor, a flow sensor and/or a magnetometer, a power supply or battery 1130, a circuit 1140 (e.g., a controller), and/or a solar panel 1150 that may be electrically coupled together. In aspects, the sensor 1120 may be mounted suspended from, below, and/or to the side of the end gun 70. In aspects, the sensor 1120 may include a water quality sensor that measures, for example, iron, calcium, salts and /or organic material.

[079] In aspects, the magnetometer can determine the typical heading and/or displacement of an end gun 70 (see Figures 15 and 16). For example, a typical offset of a 70 butt gun can range from about 100 to about 150 degrees in rotation. If the 1210 drive arm return spring changes because of poor adjustment, or due to a tree branch pulling it, the heading accuracy can be at least about 10 degrees. In other cases, 70 end guns never change direction, or may shift outward 100 to 150 degrees.

[080] The moving end gun 70 may further support an encoder assembly 1160 having an encoder 1162 and an encoder disk 1164 that is coupled to the electronics housing 1110. A pressure sensor 1170 is also coupled to the electronics housing 1110 to measure fluid flow pressure through end gun 70 (Figure 15). Pressure can indicate the volume of water dispensed. Furthermore, a reed switch 1180 or other magnetic switch may be coupled to the movable end gun 70 and disposed in proximity to a magnet 1190 supported on pivot 20 (Figure 1). As can be appreciated, any of the disclosed electronic components can be electrically coupled to circuit 140 through a wired or wireless connection (see Figures 13A and 13B). Notably, one or more of the accelerometer, gyroscope, magnetometer, encoder assembly, and/or any other suitable sensors is configured to generate an electrical signal indicative of motion and/or positioning (e.g., acceleration, speed, distance, location , etc.) of the moving end gun 70 relative to the pivot 20 over time (seconds, minutes, hours, days, years, etc.). The controller 200 is configured to receive the electrical signal and determine whether the trailing end gun requires maintenance based on the electrical signal. The controller 200 may send a signal and/or alert indicating the health of the end gun and/or whether maintenance is required based on predetermined data or thresholds that may be part of a database, program, and/or stored in memory ( e.g. supported in-circuit, in the cloud, on a network, server, etc.).

[081] When there is a mechanical problem with the end gun, the angular rate may decrease. Additionally, the ratio of time forward to time backward may become less balanced and the time spent moving forward will become much longer than the speed of return.

[082] Figures 12A and 12B are exemplary signals generated during a left-to-right passage of the end gun with the predictive maintenance system of Figure 11A. In aspects, the butt gun prediction model 502 may use ratios of factors such as total passage period (Tc), forward angular rate (T1), backward angular rate (T2), number of forward turns (n). , number of backward turns (m), forward angular rate, and/or backward angular rate, to indicate reduced health of the butt gun. For example, an end gun in perfect health might have a ratio of forward angular rate to backward angular rate of 1. Although this ratio may begin to deviate from 1 as the health of the end gun decreases. In another example, a slope of the gyroscope signal over time during forward motion or backward motion may be proportional to the angular acceleration. This slope can be used by the end gun prediction model 502 to predict abnormal operation of the end gun. In aspects, when there is a mechanical problem, the angular rate may decrease. Additionally, the ratio of time forward to time backward may become less balanced and the time spent forward will become much longer than the return speed.

[083] With reference to Figures 13A, 13B and 14, the disclosed predictive maintenance systems, which may be in the form of a smart end gun for end gun predictive maintenance, may operate using any suitable number or type of approaches. analytics and/or logic such as control mapping, machine learning (“ML”) anomaly detection, parameter threshold alarms, etc. For example, the predictive maintenance system may use a signal that fails to satisfy a given threshold related to free movement of the end gun 70 such as a peak rotational speed during a drive arm impact, or the time it takes to complete a Left-Right-Left Cycle to predict abnormal behavior.

[084] In aspects, a geolocation report can be used as an input to the ML 300 model. For example, a GPS, can be used to determine a wet zone versus a dry zone, and train as a “digital twin” to the as the irrigation system moves around the field. The altitude of the 70 end gun (relative to the pivot) is also useful in predicting the expected pressure. Pressure signal analysis can be used as an input to the ML 300 model.

[085] Figure 15 shows a flowchart for testing systems of the predictive maintenance systems of this disclosure.

[086] In aspects, the disclosed predictive maintenance systems may be a separate system that can be selectively attached or retrofitted to an end gun 70, or in some aspects, the predictive maintenance system may be built directly into an end gun. 70.

[087] Figure 17 shows a logic diagram for the revealed technology. The predictive maintenance system can look at various motion acceptance criteria such as forward/backward angular rate, forward/backward motion ratio, angular range, time to actuate the sensing lever, acceleration in x/y/directions. z/front/back, and/or change of direction forward and backward. These movements are proportional to water pressure. In aspects, the slope of the accelerometer and/or gyroscope signal over time, and/or the waveforms from the gyroscope and/or accelerometer over time can be used to determine abnormal operation of the pistol. far end.

[088] Furthermore, the disclosed structure may include any mechanical, electrical and/or chemical components suitable for operating the disclosed pivot predictive maintenance system or components thereof. For example, these electrical components may include, for example, any set of electrical and/or electromechanical, and/or electrochemical circuits, which may include or be coupled to one or more printed circuit boards. According to the usage in question, the term “controller” includes “processor,” “digital processing device” and the like, and are used to indicate a microprocessor or central processing unit (CPU). The CPU is the set of electronic circuits in a computer that carries out the instructions of a computer program, carrying out the control of basic arithmetic logic, logic, control and input/output (I/O) operations specified by the instructions, and, by way of non-limiting examples, include server computers. In some aspects, the controller includes an operating system configured to carry out executable instructions. Those skilled in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. In some aspects, the operating system is powered by cloud computing.

[089] In some aspects, the term “controller” may be used to indicate a device that controls the transfer of data from a computer or computing device to a peripheral or separate device and vice versa, and/or a mechanical device and/or electromechanical (e.g., a lever, a rotary knob, etc.) that mechanically operates and/or actuates a peripheral or separate device.

[090] In aspects, the controller includes a storage and/or memory device. Storage and/or memory device is one or more physical devices used to store data or programs on a temporary or permanent basis. In some aspects, the controller includes volatile memory and requires power to maintain stored information. In many respects, the controller includes non-volatile memory and retains stored information when not powered. In some aspects, non-volatile memory includes flash memory. In certain aspects, non-volatile memory includes dynamic random access memory (DRAM). In some aspects, non-volatile memory includes ferroelectric random access memory (FRAM). In many aspects, non-volatile memory includes phase change random access memory (PRAM). In certain aspects, the controller is a storage device that includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tape drives, optical disk drives, and storage based in cloud computing. In various aspects, the storage and/or memory device is a combination of devices such as those disclosed herein.

[091] In some aspects, the controller includes a display for sending visual information to a user. In many ways, the monitor is a cathode ray tube (CRT). In many ways, the monitor is a liquid crystal display (LCD). In certain aspects, the display screen is a thin film transistor liquid crystal display (TFT-LCD). In aspects, the display is an organic light-emitting diode (OLED) display. In certain aspects, in an OLED display there is a passive matrix OLED (PMOLED) or active matrix OLED (AMOLED) display. In aspects, monitor is a plasma monitor. In certain respects, the monitor is a video projector. In many aspects, the display is interactive (e.g., having a touch screen or a sensor such as a camera, a 3D sensor, a LiDAR, a radar, etc.) that can detect user interactions/gestures/responses, and similar. In some aspects, the monitor is a combination of devices such as those disclosed herein.

[092] The controller may include or be coupled to a server and/or a network. As used in question, the term “server” includes “computer server,” “central server,” “main server,” and similar terms to indicate a computer or device on a network that manages the system, components thereof, and/or or resources thereof. As used in question, the term “network” may include any network technology including, for example, a cellular data network, a wired network, a fiber optic network, a satellite network, and/or a wireless network. IEEE 802.11a/b/g/n/ac wire, among others.

[093] In several aspects, the controller can be coupled to a mesh network. As per the usage in question, a “mesh network” is a network topology where each network node delays data to the network. All mesh nodes cooperate in distributing data across the network. They can be applied to wired and wireless networks. Wireless mesh networks can be considered a type of “wireless ad hoc” network. Therefore, wireless mesh networks are closely related to mobile ad hoc networks (MANETs). Although MANETs are not restricted to a specific mesh network topology, wireless ad hoc networks or MANETs can take any form of network topology. Mesh networks can relay messages using a flooding technique or a routing technique. With routing, the message is propagated along a trajectory, jumping from node to node until it reaches its destination. To ensure that all of its trajectories are available, the network must allow continuous connections and must reconfigure itself around broken trajectories, using self-healing algorithms such as Shortest Path Bridging. Self-healing allows a routing-based network to operate when a node breaks or when a connection becomes unreliable. As a result, the network is typically quite reliable, as there is more than one path between a source and a destination in the network. This concept can also apply to wired networks and software interaction. A mesh network whose nodes are all connected together is a fully connected network.

[094] In some aspects, the controller may include one or more modules. According to the usage in question, the term “module” and similar terms are used to indicate a hardware component independent of the central server, which successively includes software modules. In software, a module is a part of a program. Programs are composed of one or more independently developed modules that are not combined until the program is linked. A single module may contain one or several routines, or program sections that perform a particular task.

[095] Depending on the use in question, the controller includes software modules for managing various aspects and functions of the disclosed system or components thereof.

[096] The disclosed structure may also utilize one or more controllers to receive various information and transform the received information into information to generate an output. The controller may include any type of computing device, computing circuit, or any type of processor or processing circuit capable of executing a series of instructions that are stored in memory. The controller may include multiple processors and/or multi-core central processing units (CPUs) and may include any type of processor, such as a microprocessor, digital signal processor, microcontroller, programmable logic device (PLD), gate array field programmable (FPGA), or similar. The controller may also include a memory for storing data and/or instructions that, when executed by one or more processors, induce the one or more processors to perform one or more methods and/or algorithms.

[097] Any of the methods, programs, algorithms or codes described in this document can be converted, or expressed, into a programming language or computer program. The terms “programming language” and “computer program,” as used in question, include any language used to specify instructions to a computer, and include (without limitation) the following languages and their derivatives: Assembler, Basic, Batch files , BCPL, C, C+, C++, Delphi, Fortran, Java, JavaScript, machine code, operating system command languages, Pascal, Perl, PL1, scripting languages, Visual Basic, metalanguages that specify programs, and all languages first, second, third, fourth, fifth generation or later computer. Also included are a database and other data schemas, and any other metalanguages. No distinction is made between languages that are interpreted, compiled, or use compiled and interpreted approaches. No distinction is made between compiled and source versions of a program. Therefore, a reference to a program, where the programming language could exist in more than one state (such as source, compiled, object or linked) is a reference to any and all states. Reference to a program can encompass the actual instructions and/or the intent of those instructions.

[098] The machine learning (“ML”) model may be the most efficient for complex failures. However, basic logic can be used for simpler failure modes. Similarly, signs of abnormal operation may come from increases in the energy required to move the irrigation system, changes in system speed, or changes in the sequence of tower movement, tip gun curve frequency, or quality metrics. of power such as phase balance, starting current, power factor, THD. Since these vary with a complex inference space, ML can assist in predicting abnormal operation and simplifying user input and especially in the matter by providing a simple labeling method.

[099] In aspects, the abnormal operation can be predicted by generating, based on the first received set of sensor signals, a data structure that is formatted as being processed through one or more layers of a machine learning model. . The data structure can have one or more structuring data in fields. Abnormal operation may be further predicted by processing data including the data structure, through each one or more layers of the machine learning model that have been trained to predict a probability that a particular piece of equipment may require maintenance; and generating, by an output layer of the machine learning model, an output data structure. The output data structure may include one or more field structure data indicating a probability that a particular piece of equipment may require maintenance. The abnormal operation requirement may further be anticipated by processing the output data structure to determine whether data organized by one or more fields of the output data structure satisfy a predetermined threshold, wherein the output data structure includes one or more field structuring data indicating a probability that a particular piece of equipment may require maintenance; and generate the prediction based on the output data of the machine learning model. The forecast includes abnormal operation. Training may include supervised learning.

[0100] The machine learning model can be trained based on observing where the end gun 70 turns on, the speed at which the end gun 70 completes a Left-Right-Left Cycle, peak gyroscope speed, coordinates of GPS end gun 70 turns on, snaps on a given GPS location, and uses it as a “digital twin” to set a baseline operation to compare to when in service. In aspects, the pressure transient data when the end gun 70 turns on and off can be used as an input to the machine learning model for training. Pressure transient data can be used to identify valve operation problems that can lead to the valve sticking open or closed. In aspects, irrigated acres of a field can be automatically mapped to replace or supplement the current practice of “flagging,” whereby an irrigation crew member drives around the field with a laser rangefinder and places flags to create a map of the field. which can be used to plant and manage the field. By observing the pressure at the pivot end 20 and the gun end 70, the machine learning model may be able to identify/predict potential problems in the water supply, well, well motors, crossheads, VFDs, filters, pumps. reinforcement, and/or other pivot components. Transient pressure measurements can be captured using relatively high time resolution (<100ms). In aspects, a user can configure the on and/or off zones of the end gun 70. In aspects, end gun kinematic parameters as well as pressure transients for end gun valve pressure and/or sequencing pressure corner arm patterns can be used as an input to the machine learning model. This reference observation can be used to enable site and/or well pressure-based analyzes to improve system sensitivity and accuracy.

[0101] In aspects, the controller 200 may receive the electrical signal generated from a sensor 102 that monitors a valve 902 (Figure 9). Valve 902 is configured to supply water for irrigation. Sensor 102 may capture, for example, but not limited to, pressure transient data. Sensor 102 may capture pressure transient data over time, and/or generate a resulting waveform over time. The controller 200 may determine whether the valve 902, one or more components thereof, require maintenance based on the electrical signal and determine when the valve 902 requires maintenance (e.g., valve operation problems may lead to the valve sticking open or closed ) based on the electrical signal. The determination can be carried out by the machine learning model and/or by a classical algorithm. The controller 200 may provide an alert to the user of the determination that the valve 902 requires maintenance.

[0102] Although an irrigation system is used as an example, the disclosed systems and methods may be advantageously used in other environments, such as, without limitation, dust management in a mine, and/or lawn irrigation in a stadium to be covered.

[0103] In one aspect of the present disclosure, the disclosed algorithms can be trained using supervised learning. Supervised learning is the machine learning task of learning a function that maps an input to an output based on input-output exemplar pairs. The ML model infers a function from labeled training data consisting of a set of training examples. In supervised learning, each example is a pair that includes an input object (typically a vector) and a desired output value (also called the supervised signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used to map new examples. In various embodiments, the algorithm may correctly determine class labels for unseen instances. This requires the learning algorithm to generalize from the training data to situations not “reasonably” seen.

[0104] In various embodiments, the neural network may be trained using training data, which may include, for example, different ground conditions or different component characteristics (e.g., current, voltage, pressures, cycle, etc.). The algorithm can analyze this training data and produce an inferred function that can allow the algorithm to identify component failures or changes in health, based on the generalizations that the algorithm has developed from the training data. In various embodiments, the training may include at least one of supervised training, unsupervised training, and/or reinforcement training.

[0105] In some aspects, a user may begin a training session while watching an operation to simplify setup on each unique end gun and pivot combination since pressures and flows may be different. When the end gun is considered to be operating normally, the user can open a training window which will then be used to calibrate or train the analysis for future anomaly detections. For example, Linux (R), which can run a Python (R) script, for example, can be used to perform prediction. In aspects, analysis can also be performed on the sensor using platforms such as Tensor Flow (R) lite.

[0106] In various embodiments, the neural network may include, for example, a three-layer temporal convolutional network with residual connections, where each layer may include three parallel convolutions, where the number of kernels and dilations increases from bottom to top, and where the number of convolutional filters increases from bottom to top. It is contemplated that a greater or lesser number of layers may be used. It is contemplated that a greater or lesser number of cores and dilations may also be used.

[0107] In aspects, the disclosed monitoring systems may be a separate system that may be selectively attached or retrofitted to an end gun, or, in some aspects, the monitoring system may be built directly into an end gun.

[0108] As seen in Figures 19 to 21, a condition-based monitoring (CBM) system in the form of an end gun testing system is also provided for testing end guns to determine the health of the end guns.

[0109] In general, the system includes a first cloud server (e.g., a Heartland Farm cloud) that includes an interface to the system, a device cloud (e.g., a Particle cloud) configured for communication between devices, and the system, and a firmware server, which is configured to push firmware updates to system components. System devices may include a cellular-enabled microcontroller (e.g., a Particle Boron) and a CBM module. The cellular-enabled microcontroller includes a cellular receiver/transmitter, a wireless receiver/transmitter (e.g., Bluetooth and/or WIFI), power management functions, firmware update functions, surveillance functions, integrated circuits for power management. power (PMIC), power-on self-test (POST) functions, a universal asynchronous receiver/transmitter (UART), and a general purpose IO (GPIO). The cellular-enabled microcontroller communicates with the condition-based monitor module (CBM module) that is configured to process signals from sensors. Sensor signals can be sampled by the CBM module at a rate of 1125KHz, for example. When the CBM module determines that one or more operations are complete, the CBM module notifies the system via the cellular-enabled microcontroller.

[0110] Referring to Figure 20, a high-level block diagram of the CBM module is shown. The CBM module performs functions including signal processing, sensor calibration, power management, end gun operational reporting, end gun health alerts, end gun characterization, surveillance, reset loader, and a state. In general, the CBM module includes a microcontroller (e.g., an STM32 microcontroller), a regulator, one or more UARTs, analog and/or digital inputs and outputs, a programming header (e.g., SWD and/or JTAG) , status LED (e.g. blue status LED, red error LED), flash memory, an impact sensor, an inertial measurement unit (IMU). Application firmware and a reset loader firmware run on the microcontroller. The CBM module can be powered via an external power supply and/or a battery. An IMU is an electronic device that measures and reports a specific body force, angular rate, and sometimes body orientation, using a combination of accelerometers, gyroscopes, and/or magnetometers.

[0111] Signal processing functions include, for example, logic to: detect when the end gun starts and/or stops, determine the number of degrees (degrees forward and backward) that the end gun has traveled based on on an onboard compass, detect the average peak forward and/or backward angular rate based on an onboard gyroscope, determine the average forward and backward time, determine a ratio of forward and backward time, determine the time to actuate the reverse lever for the end gun (e.g., notify the system if the unit is becoming suspended and is taking too long to reverse direction), forward and/or reverse drive arm frequency based on the accelerometer and/or gyroscope.

[0112] In aspects, a triangulated cell phone location of the CBM module can be used to determine the tip gun's geolocation, speed, positioning, minimum and maximum observed location, and other relevant information. Cell phone location data comes from a variety of sources, including cell phone towers, Global Positioning System (GPS) signals, and Bluetooth beacons. For example, the cell location of the CBM module can be determined using cell site location information since cellular devices connect their users to telecommunications and internet networks through cell towers with known locations.

[0113] The CBM module may generate an operational report based on the processed sensor signals and may upload the report to the system via the cellular-enabled microcontroller. The operational report may include, for example, total degrees traveled, number of passes, average passing degrees traveled, and/or an average drive arm period.

[0114] Referring to Figure 21, a diagram of the firmware is shown. Firmware may include, for example, peripheral units (e.g., SPI, I2C, UART, and/or QUADSPI), device units, and applications.

[0115] Referring to Figure 22, a state machine for the CBM system is shown. A state machine is a behavioral model that includes a finite number of states. Based on the current state and a given input, the state machine performs state transitions. The state machine may include at least the following states: initiation (e.g., powered on), reboot load, initialization, application recovery, idle, monitoring, sensor calibration, end gun characterization, failure, hardware test, monitor, and/or suspension.

[0116] When the CBM module is initially powered on, the state machine initiates the start state. Next, the CBM module enters the reset load state. In the reboot load state, the reboot loader firmware loads the application firmware into working memory. The boot loader firmware may include the ability to update the firmware, determine whether the application is ok or not (e.g., corrupt and/or erroneous application), or load the application firmware. The boot loader firmware can determine whether the application firmware is compatible with the hardware.

[0117] When in the reset load state, if the CBM module receives a predetermined command (e.g., 0xAAFFAAFF”), the CBM module may enter a flash state (e.g., a firmware update mode). In this state, the CBM module can receive firmware updates and then restart after updating the application firmware.

[0118] If the application firmware is ok (e.g., application not corrupted and/or wrong), and a flash command is not received, then the application firmware is loaded and the initialization state is entered. In the initialization state, the CBM module adjusts the sensors and communicates with the memory.

[0119] If initialization is ok, an idle state begins. In the idle state, generally, the CBM module reads sensor data and processes the sensor data to generate the operational report. While in the idle state, the CBM module can poll/interrogate the IMU to obtain the latest heading and/or temperature of the device. In the idle state, if the gyroscope is active for more than about 10 degrees per second, for about a minute, for example, the CBM module enters the monitoring state, where the various sensors are monitored.

[0120] While in the monitoring state, if the gyroscope (e.g., the z-axis gyroscope) is less than about 5 degrees/second for about a minute, for example, the CBM module exits the monitoring state and enters the idle state again. Upon transition to the monitoring state, the CBM module will issue the end gun start event. Upon exiting the monitoring state, the CBM module will issue the end gun stop event. Upon exit from the monitoring state, the CBM module can send the operational report to the first cloud server. In the monitoring state, the CBM module may determine, based on sensors, that one or more of the end guns have failed and may report the failure to the first cloud server.

[0121] The CBM module may include a command line interface (CLI), which allows a user computing device (e.g., a mobile device, a tablet, a desktop computer, and/or a laptop) connected to the CBM module through the UART (e.g., by RS485 and/or Bluetooth) to send commands to the CBM module to enter various states from the idle state. For example, a “CAL” CLI command induces the CBM module to enter the sensor calibration state. In a sensor calibration state, the system may allow calibration of sensors and sensor data. Additionally, if a TBS sensor failure is detected during the monitoring state, the CBM module may enter a self-healing pass and enter the idle state again. Failures can be based on a total number of degrees traveled by the end gun, a number of passes, an average degree of passage traveled, an average drive arm period, an average peak gyro output per drive arm impact, an average peak accelerometer magnitude per drive arm impact, an average front and rear time ratio, a total time in the butt gun state, a number of failures reported during operation, a triangulated cell phone location, a bearing minimum, and/or a maximum heading. For example, a “HWtest” CLI command induces the CBM module to enter a hardware test state. In the hardware test state, the CBM module tests the attached hardware. For example, a “press enter twice” CLI command induces the CBM module to enter the monitoring state. For example, a “CHAR” CLI command induces the CBM module to enter the end gun characterization state. In the end gun characterization state, the CBM module allows you to characterize and classify the characterization of the attached hardware. For example, a CLI command “SLP” induces the CBM module to enter the sleep state.

[0122] Figure 23 is an end gun zone diagram (e.g., four quadrants) used to determine irrigation failure location. In aspects, one or more end guns may be located in the four quadrants of a field that also uses a center pivot irrigation system. Although four quadrants are used as an example, any number of end gun zones are contemplated to be within the scope of this disclosure. The end gun can be used to irrigate corners (e.g. quadrants) of a field that are overlooked by the center pivot irrigation system. Center pivot irrigation systems often take up to two days to make a complete circle around the field. If there is equipment failure and the user does not realize that a corner has not been irrigated on a hot summer day, for example, crops in that corner may die. Correspondingly, methods and systems for detecting missed corners in real time of development help to avoid these situations by detecting a corner (e.g. quadrant) that has not been irrigated and reporting this fact to the user. In addition to not being irrigated, sometimes the 70 end gun is “on” when it should be “off,” which can lead to danger for homes and drivers in the area. In aspects, each quadrant may include two end guns equipped with a CBM module, for example. The CBM module may include a compass (e.g., a magnetometer) configured to determine a minimum and maximum observed heading of the two end guns (e.g., a first end gun and a second end gun). For example, if the first end gun has a minimum observed bearing of southwest and the second end gun has a maximum observed bearing of northeast, then controller 200 may determine that the first and second end guns are located in the quadrant 4. The first end gun for a quadrant may have a relationship to the first end gun for the next or previous quadrant, where they are offset in orientation by about 90 degrees. For example, the first end gun of quadrant 4 may have a minimum observable position of southwest and the first end gun of quadrant 1 may have a minimum observable position of northwest. In aspects, the CBM module may include a GPS, and/or use cellular location triangulation to adjust a geofence to determine which quadrant a butt gun is located. For example, based on GPS coordinates, controller 200 may determine that an end gun is in quadrant 2. The method may monitor “on” activation events to determine whether an end gun has been activated to irrigate the field. The controller 200 may determine, based on the quadrant and whether a power-on event has been detected, that the determined quadrant has not been irrigated. In aspects, the user may receive a report or an indication (e.g., a text message, email, etc.) that the given quadrant has not been irrigated. For example, controller 200 may detect a trigger event connected to an end gun. Controller 200 may also detect that a specific end gun was situated in quadrant 1. Controller 200 would determine that quadrant 1 was irrigated. Next, controller 200 may detect for the next end gun that there was not an activated trigger event, and based on the GPS location of the end gun it was in quadrant 2. Controller 200 would determine that quadrant 2 may not have been activated. irrigated and would generate a report to alert the user so that the user can check the end gun for possible equipment failures. In end gun zones, for example, end gun 70 may be “on” at a random location around 360 degrees, not operated in another corner, and/or in some cases, “on” all the time.

[0123] It is contemplated that the moving end gun may be operatively coupled to the pivot, for example, as part of the system but separate from the pivot. For example, on some less capitalized farms, a pivot may not have an end gun, but instead the end gun may be placed in a fixed location in the corner of the field. The end gun may include stationary gun irrigators that are placed in position on a tripod or quadpod.

[0124] Referring to Figure 24, an exemplary gyroscope signal output graph for the monitoring system of Figure 1 is shown. For example, if an end gun requires maintenance, gyroscope data can provide indications such as the forward/back time ratio being greater for an end gun that does not require maintenance.

[0125] Referring to Figure 25 an exemplary compass signal output graph for the monitoring system of Figure 1 is shown. For example, if the end gun has difficulty turning a reversing mechanism, there may be a tilt change in the output data from the compass (i.e., the magnetometer).

[0126] Referring to Figure 26, an exemplary accelerometer signal output graph for the monitoring system of Figure 1 is shown. For example, if a tip gun requires maintenance, accelerometer data can provide indications such as the forward/backward time ratio being greater than for a tip gun that does not require maintenance.

[0127] As can be appreciated, the fixing of any of the components of the disclosed apparatus can be carried out using known fixing techniques such as welding, crimping, gluing, clamping, etc.

[0128] Individuals skilled in the art will understand that the structures and methods specifically described in the present document and illustrated in the attached figures are non-limiting exemplary aspects, and that the description, disclosure, and figures should be construed merely as exemplary of particular aspects. Therefore, it should be understood that this disclosure is not limited to the precise aspects described, and that various other changes and modifications may be made by an individual skilled in the art without departing from the scope or spirit of the disclosure. Additionally, it is envisioned that the elements and features illustrated or described in connection with one exemplary aspect may be combined with the elements and features of another without departing from the scope of this disclosure, and that such modifications and variations are also intended to be included within the scope of this revelation. In fact, any combination of any of the disclosed elements and features is within the scope of this disclosure. Correspondingly, the subject matter of this revelation should not be limited to what has been particularly shown and described.

Claims (16)

1. Monitoring system (100) for a pivot irrigation system (106), a movable end gun (70) operatively associated with a portion of the pivot irrigation system (106), CHARACTERIZED by the fact that the monitoring system (100) monitoring (100) comprises: a sensor (102, 1120) configured to couple to the movable end gun (70), and configured to generate an electrical signal indicative of movement and/or positioning of the movable end gun (70) relative to portion of the pivot irrigation system (106) over time; a processor (220); and a memory (230), including instructions stored therein, which when executed by the processor (220) induce the system (100) to: receive the generated electrical signal; determining whether the moving end gun (70), or one or more components thereof, requires maintenance based on the electrical signal; determining when the trailing end gun (70) is in an on and/or off activation state based on the electrical signal, determining an angular rate of the trailing end gun (70) and time taken to move forward and/or backwards based on electrical signal; determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state; and providing an indication to a user that a location has not been irrigated based on the determination. 2. Monitoring system (100), according to claim 1, CHARACTERIZED by the fact that the instructions, when executed, further induce the system (100) to generate a report based on: determining whether the mobile end gun ( 70), or one or more components thereof, requires maintenance based on the electrical signal; determining when the trailing end gun (70) is in an on and/or off activation state based on the electrical signal, determining an angular rate of the trailing end gun (70) and time taken to move forward and/or backwards based on electrical signal; and determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state. 3. Monitoring system (100), according to claim 1, CHARACTERIZED by the fact that the irrigation system portion (106) includes at least one of a side actuator, a water nozzle or a pivot (20), and wherein the movable end gun (70) is movably mounted on the pivot (20). 4. Monitoring system (100), according to claim 1, CHARACTERIZED by the fact that the mobile end gun (70) is part of the same system, but separate from the irrigation system portion (106). 5. Monitoring system (100), according to claim 1, CHARACTERIZED by the fact that it further comprises an analysis mechanism configured to perform: determining whether the moving end gun (70), or one or more components thereof, requires maintenance based on electrical signal; determining when the trailing end gun (70) is in an on and/or off activation state based on the electrical signal, determining an angular rate of the trailing end gun (70) and time taken to move forward and/or backwards based on electrical signal; and determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state. 6. Monitoring system (100), according to claim 5, CHARACTERIZED by the fact that the instructions, when executed by the processor (220), further induce the monitoring system (100) to receive data from at least one between a weather station (82), a field soil moisture sensor (86), a terrain and soil map (88), a temperature sensor (89), or National Oceanic and Atmospheric Administration weather (84). 7. Monitoring system (100), according to claim 5, CHARACTERIZED by the fact that the analysis mechanism includes a machine learning model (300), and on which the machine learning model (300) is based in a deep learning network, a classic machine learning model, or combinations thereof. 8. Monitoring system (100), according to claim 1, CHARACTERIZED by the fact that the sensor (1120) includes at least one of an encoder (1162), a pressure sensor (1170), a flow meter, a magnetometer, a gyroscope, an accelerometer, a camera, a gesture sensor, a microphone, a laser rangefinder, a reed switch (1180), a magnetic switch, a GPS, or an optical switch. 9. Computer-implemented method for monitoring a pivot irrigation system (106) that includes four end gun zones, each end gun zone of the four end gun zones including a movable end gun ( 70) operationally associated with a portion of the pivot irrigation system (106), CHARACTERIZED by the fact that the method comprises: receiving an electrical signal generated by a sensor (102, 1120) configured to couple to the movable end gun (70 ), wherein the electrical signal is indicative of movement and/or positioning of the movable end gun (70) relative to the portion of the pivot irrigation system (106) over time; determining whether the moving end gun (70), or one or more components thereof, requires maintenance based on the electrical signal; determining when the moving end gun (70) is in an on and/or off activation state based on the electrical signal; determining an angular rate of the moving end gun (70) and time taken to move forward and/or backward based on the electrical signal; determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state; and providing an indication to a user that a location has not been irrigated based on the determination. 10. Computer-implemented method according to claim 9, CHARACTERIZED by the fact that it further comprises generating a report based on: determining whether the moving end gun (70), or one or more components thereof, requires maintenance with based on electrical signal; determining when the trailing end gun (70) is in an on and/or off activation state based on the electrical signal, determining an angular rate of the trailing end gun (70) and time taken to move forward and/or backwards based on electrical signal; and determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state. 11. Computer-implemented method according to claim 9, CHARACTERIZED by the fact that the portion of the irrigation system (106) includes at least one of a side actuator, a water nozzle or a pivot (20), and in that the movable end gun (70) is movably mounted on the pivot (20). 12. Computer-implemented method according to claim 9, CHARACTERIZED by the fact that the mobile end gun (70) is part of the same system, but separate from the irrigation system portion (106). 13. Computer-implemented method according to claim 9, CHARACTERIZED by the fact that it further comprises performing, by an analysis mechanism: determining whether the moving end gun (70), or one or more components thereof, requires maintenance based on the electrical signal; determining when the trailing end gun (70) is in an on and/or off activation state based on the electrical signal, determining an angular rate of the trailing end gun (70) and time taken to move forward and/or backwards based on electrical signal; and determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state. 14. Computer-implemented method according to claim 13, CHARACTERIZED by the fact that it further comprises receiving data from at least one of a weather station (82), a field soil moisture sensor (86), a terrain and soil map (88), a temperature sensor (89), or weather from the National Oceanic and Atmospheric Administration (84). 15. Computer-implemented method according to claim 13, CHARACTERIZED by the fact that the analysis engine includes a machine learning model (300), and wherein the machine learning model (300) is based on a deep learning network, a classic machine learning model, or combinations thereof. 16. Non-transitory computer-readable media CHARACTERIZED by the fact that it stores instructions that, when executed by a processor (220), induce the processor (220) to execute a method for monitoring a pivot irrigation system (106) including a plurality of end gun zones, each end gun zone of the plurality of end gun zones including a movable end gun (70) operatively associated with a portion of the pivot irrigation system (106), being which the method comprises: receiving an electrical signal generated by a sensor (102, 1120) configured to couple to the movable end gun (70), wherein the electrical signal is indicative of movement and/or positioning of the movable end gun ( 70) in relation to the portion of the pivot irrigation system (106) over time; determining whether the moving end gun (70), or one or more components thereof, requires maintenance based on the electrical signal; determining when the moving end gun (70) is in an on and/or off activation state based on the electrical signal; determining an angular rate of the moving end gun (70) and time taken to move forward and/or backward based on the electrical signal; determining whether the end gun (70) pivots more than a predetermined number of degrees without an end gun (70) in the activated state; and providing an indication to a user that a location has not been irrigated based on the determination.