Systems and methods for predictive irrigation system maintenance

Abstract

A machine learning based predictive maintenance system includes an irrigation system having a plurality of components and configured to irrigate a farming area. The maintenance system includes a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility. The sensor is configured to generate a signal indicative of a condition of at least one component of the plurality of components of the irrigation system based on network power quality. The maintenance system further includes a processor and a memory. The memory includes instructions, which when executed by the processor, cause the predictive maintenance system to receive the sensed signal, determine abnormal operation of the at least one component, and predict, by a machine learning model, a maintenance requirement of the at least one component based the determined abnormal operation. The network power quality includes a phase balance, an inrush current, a power factor, or combinations thereof.

What is claimed is: 
     
         1 . A machine learning based predictive maintenance system for an irrigation system, the machine learning based predictive maintenance system comprising:
 an irrigation system configured to irrigate a farming area and including a plurality of components;   a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility, the sensor is configured to generate a signal indicative of abnormal operation of at least one component of the plurality of components of the irrigation system based on network power quality, the network power quality including a phase balance, an inrush current, a power factor, or combinations thereof;   a processor; and   a memory, including instructions stored thereon, which when executed by the processor cause the predictive maintenance system to:
 receive the sensed signal; 
 determine abnormal operation of the at least one component; and 
 predict, by a machine learning model, a maintenance requirement of the at least one component based on the determined abnormal operation. 
   
     
     
         2 . The predictive maintenance system of  claim 1 , wherein the instructions, when executed by the processor, cause the predictive maintenance system to:
 display on a display the predicted maintenance requirement of the at least one component.   
     
     
         3 . The predictive maintenance system of  claim 1 , wherein the plurality of components of the irrigation system includes a pump, a pivot, a tower, an end tower, a corner tower, an air compressor, an endgun, or combinations thereof. 
     
     
         4 . The predictive maintenance system of  claim 3 , wherein the signal of abnormal operation includes an increase in energy required to move the irrigation system, a change in speed of the system, a change in sequence of a moving of the tower, an endgun turn frequency or combinations thereof. 
     
     
         5 . The predictive maintenance system of  claim 4 , wherein the sensor includes an encoder, a pressure sensor, a flow meter, a current sensor, a power sensor, a voltage sensor, or combinations thereof. 
     
     
         6 . The predictive maintenance system of  claim 1 , wherein the instructions, when executed by the processor, further cause the predictive maintenance system to:
 transmit an indication of the predicted maintenance requirement, to a user device for display.   
     
     
         7 . The predictive maintenance system of  claim 6 , wherein the instructions, when executed by the processor, further cause the predictive maintenance system to:
 display, on a display of the user device, the indication of the predicted maintenance requirement.   
     
     
         8 . The predictive maintenance system of  claim 1 , wherein the machine learning model is based on a deep learning network, a classical machine learning model, or combinations thereof. 
     
     
         9 . The predictive maintenance system of  claim 1 , wherein the instructions, when executed by the processor, further cause the predictive maintenance 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 National Oceanic and Atmospheric Administration weather. 
     
     
         10 . The predictive maintenance system of  claim 1 , wherein the prediction is based on comparing a power sensed by the sensor to an expected power based on at least one of a soil moisture directly measured, a soil moisture inferred from weather data from the field and/or regional weather stations, a topographical map, a soil map, a motor RPM, a gearbox ratio, a tower weight, a span weight, an operating condition of the at least one component, or combinations thereof. 
     
     
         11 . A computer-implemented method for predictive maintenance for an irrigation system, the computer-implemented method comprising:
 receiving a signal, sensed by a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility, indicative of a condition of at least one component of a plurality of components of an irrigation system based on network power quality, the network power quality including a phase balance, an inrush current, a power factor, or combinations thereof, the irrigation system configured to irrigate a farming area and including a plurality of components;   determining abnormal operation of the at least one component; and   predicting, by a machine learning model, a maintenance requirement of the at least one component based on the determined abnormal operation.   
     
     
         12 . The computer-implemented method of  claim 11 , further comprising:
 displaying on a display the predicted maintenance requirement of the at least one component.   
     
     
         13 . The computer-implemented method of  claim 11 , wherein the plurality of components of the irrigation system includes at least one of a pump, a pivot, a tower, an end tower, a corner tower, an air compressor, or an endgun. 
     
     
         14 . The computer-implemented method of  claim 13 , wherein the signal indicating the abnormal operation includes at least one of an increase in energy required to move the irrigation system, a change in speed of the system, a change in sequence of a moving of the tower, or an endgun turn frequency. 
     
     
         15 . The computer-implemented method of  claim 14 , further comprising:
 transmitting an indication of the predicted maintenance requirement, to a user device for display; and   displaying, on a display of the user device, the indication of the predicted maintenance requirement.   
     
     
         16 . The computer-implemented method of  claim 11 , wherein the sensor includes an encoder, a pressure sensor, a flow meter, a current sensor, a power sensor, a voltage sensor, or combinations thereof. 
     
     
         17 . The computer-implemented method of  claim 11 , wherein the machine learning model is based on a deep learning network, a classical machine learning model, or combinations thereof. 
     
     
         18 . The computer-implemented method of  claim 11 , wherein the prediction is based on comparing a power sensed by the sensor to an expected power based on at least one of a soil moisture directly measured, a soil moisture inferred from weather data from the field and/or regional weather stations, a topographical map, a soil map, a motor RPM, a gearbox ratio, a tower weight, a span weight, an operating condition of the at least one component, or combinations thereof. 
     
     
         19 . A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method for predictive maintenance for an irrigation system, the method comprising:
 receiving a signal, sensed by a sensor disposed at a center pivot of the irrigation system or at a main disconnect of a utility, indicative of a condition of at least one component of a plurality of components of an irrigation system based on network power quality, the network power quality including a phase balance, an inrush current, a power factor, or combinations thereof, the irrigation system configured to irrigate a farming area and including a plurality of components;   determining abnormal operation of the at least one component; and   predicting, by a machine learning model, a maintenance requirement of the at least one component based on the determined abnormal operation.   
     
     
         20 . The non-transitory computer-readable medium of  claim 19 , wherein the plurality of components of the irrigation system includes at least one of a pump, a pivot, a tower, an end tower, a corner tower, an air compressor, or an endgun.