Systems and methods for equipment maintenance
Disclosed are systems and methods for an industry-wide and predictive approach to maintenance of commercial equipment. In one embodiment, multiple instances of a frontend infrastructure can be deployed to various sites where one or more physical parameters of industrial equipment are monitored with wireless sensors and routed to a backend infrastructure. The backend infrastructure can process the sensor data received from the multiple sites and generate predictive maintenance notifications.
1. A method comprising:
receiving a profile of an equipment from an operator of the equipment, wherein the profile comprises one or more physical parameters of the equipment to be monitored a device identifier of the equipment to be monitored, a location of the equipment to be monitored, sensor weight preferences and normal ranges of the physical parameters;
monitoring, with one or more sensors, the one or more physical parameters of the equipment;
transmitting, by the one or more sensors, the physical parameters via a gateway to a sensor-side server;
transmitting, by the sensor-side server, sensor data including the physical parameter values and superfluous data to a backend server, the superfluous data including at least one or more of extra characters, encoding data, third-party application data and device identifiers unrelated to the equipment;
at the backend server, parsing the sensor data to remove the superfluous data from the sensor data and retaining the physical parameter values;
weighting the physical parameter values based on a location of the sensor that obtained the physical parameter values;
determining if the weighted physical parameter values are outside the normal range as set forth in the profile of the equipment and generating a notification;
determining one or more patterns in the physical parameter values over a period of time; and
generating a notification if the one or more patterns are indicative of an anomaly in operation of the equipment.
2. The method of claim 1 , wherein the one or more patterns indicative of an anomaly comprise the one or more parameter values approaching a range outside the normal range over a period of time, but not exceeding the normal range over the period of time.
3. The method of claim 1 , wherein the one or more patterns indicative of an anomaly are determined via one or more machine learning algorithms based on monitored parameter values of a plurality of equipment over a period of time.
4. The method of claim 1 further comprising:
detecting one or more equipment-wide patterns indicative of anomaly in operation, the one or more equipment-wide patterns shared among a plurality of same or similar equipment; and
generating a notification for some or all of the same or similar equipment.
5. The method of claim 1 further comprising:
generating a set of conditions based at least partly on the one or more patterns, wherein the set of conditions correlate with the anomaly;
generating an equipment repair profile corresponding to the anomaly, along with a mapping of the equipment to the equipment repair profile;
detecting the presence of the set of conditions in a second equipment; and
transmitting the equipment repair profile to an operator of the second equipment.
6. The method of claim 5 further comprising transmitting the equipment repair profile to other operators having equipment same or similar to the second equipment.
7. The method of claim 1 , further comprising:
generating equipment repair profiles of a plurality of equipment, corresponding to the anomaly detected based on the one or more patterns in the physical parameters;
based at least partly on the equipment repair profiles, detecting a set of conditions shared amongst the plurality of equipment having the detected anomaly;
generating a second repair profile corresponding to the set of conditions and the detected anomaly corresponding to the set of conditions;
detecting presence of the set of conditions in a second plurality of equipment; and
transmitting the second repair profile to one or more operators of the second plurality of equipment.
8. The method of claim 7 , wherein the set of conditions comprise one or more of; a brand of the equipment, or part identification of a previously repaired or replaced part, and a duration of time after which the part needed repair or replacement.
9. The method of claim 1 , wherein the physical parameters comprise one or more of temperature, vibration, electrical current and power drawn.
10. The method of claim 1 , further comprising: transmitting a sensor configuration signal from the backend server to the one or more sensors, wherein the sensor configuration signal is at least partly based on the equipment profile.
11. Non-transitory computer storage that stores executable program instructions that, when executed by one or more computing devices, configure the one or more computing devices to perform operations comprising:
receiving a profile of an equipment from an operator of the equipment, wherein the profile comprises one or more physical parameters of the equipment to be monitored, a device identifier of the equipment to be monitored, a location of the equipment to be monitored, sensor weight preferences and normal ranges of the physical parameters;
monitoring, with one or more sensors, the one or more physical parameters of the equipment;
transmitting, by the one or more sensors, the physical parameters via a gateway to a sensor-side server;
transmitting, by the sensor-side server, sensor data including the physical parameter values and superfluous data to a backend server, the superfluous data including at least one or more of extra characters, encoding data, third-party application data and device identifiers unrelated to the equipment;
at the backend server, parsing the sensor data to remove the superfluous data from the sensor data and retain the physical parameter values;
determining weighted physical parameter values by weighting the physical parameter values based on a location of the sensor that obtained the physical parameter values;
determining if the weighted physical parameter values are outside the normal range as set forth in the profile of the equipment and generating a notification;
determining one or more patterns in the physical parameter values over a period of time; and
generating a notification if the one or more patterns are indicative of an anomaly in operation of the equipment.
12. The non-transitory storage of claim 11 , wherein the one or more patterns indicative of an anomaly comprise the one or more parameter values approaching a range outside the normal range over a period of time, but not exceeding the normal range over the period of time.
13. The non-transitory storage of claim 11 , wherein the one or more patterns indicative of an anomaly are determined via one or more machine learning algorithms based on monitored parameter values of a plurality of equipment over a period of time.
14. The non-transitory storage of claim 11 , wherein the operations further comprise:
detecting one or more equipment-wide patterns indicative of anomaly in operation, the one or more equipment-wide patterns shared among a plurality of same or similar equipment; and
generating a notification for some or all of the same or similar equipment.
15. The non-transitory storage of claim 11 , wherein the operations further comprise:
generating a set of conditions based at least partly on the one or more patterns, wherein the set of conditions correlate with the anomaly;
generating an equipment repair profile corresponding to the anomaly, along with a mapping of the equipment to the equipment repair profile;
detecting the presence of the set of conditions in a second equipment; and
transmitting the equipment repair profile to an operator of the second equipment.
16. The non-transitory storage of claim 15 , wherein the operations further comprise: transmitting the equipment repair profile to other operators having equipment same or similar to the second equipment.
17. The non-transitory storage of claim 11 , wherein the operations further comprise:
generating equipment repair profiles of a plurality of equipment, corresponding to the anomaly detected based on the one or more patterns in the physical parameters;
based at least partly on the equipment repair profiles, detecting a set of conditions shared amongst the plurality of equipment having the detected anomaly;
generating a second repair profile corresponding to the set of conditions and the detected anomaly corresponding to the set of conditions;
detecting presence of the set of conditions in a second plurality of equipment; and
transmitting the second repair profile to one or more operators of the second plurality of equipment.
18. The non-transitory storage of claim 17 , wherein the set of conditions comprise one or more of a brand of the equipment, or part identification of a previously repaired or replaced part, and a duration of time after which the part needed repair or replacement.
19. The non-transitory storage of claim 11 , wherein the physical parameters comprise one or more of temperature, vibration, electrical current and power drawn.
20. The non-transitory storage of claim 11 , wherein the operations further comprise: transmitting a sensor configuration signal from the backend server to the one or more sensors, wherein the sensor configuration signal is at least partly based on the equipment profile.