Methods and systems of industrial production line with self organizing data collectors and neural networks
Systems and methods for data collection in an industrial production line are disclosed. A systems may include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and a data acquisition and analysis circuit for receiving the collected data and analyzing the received collected data using a neural network to determine an occurrence of an anomalous condition of at least one component.
1. A data collection system in an industrial production line, the system comprising:
a plurality of data collectors comprising a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities or conditions of the data collector members of the swarm, wherein at least some of the data collector members are operatively coupled to at least one corresponding component of a plurality of components of the industrial production line, wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data, and wherein each of the plurality of input channels corresponds to a sensor located in a industrial production line environment;
a data acquisition and analysis circuit structured to receive the collected data via the plurality of input channels and structured to analyze the received collected data using a trained neural network to determine an occurrence of a fault condition of at least one component of the plurality of components of the industrial production line, wherein the trained neural network detects a value of interest in the collected data and determines the occurrence of the fault condition based on the value of interest, and wherein the value of interest includes a signature sensed by the sensor; and
a data response circuit structured to alter an operational parameter of the industrial production line based on the determined occurrence of the fault condition.
2. The system of claim 1 , wherein the trained neural network comprises a probabilistic neural network.
3. The system of claim 2 , wherein the probabilistic neural network determines the occurrence of an anomalous condition based on pattern recognition of the value of interest.
4. The system of claim 2 , wherein the probabilistic neural network acts to recognize a fault of the at least one component of the industrial production line.
5. The system of claim 1 , wherein the trained neural network comprises a time delay neural network.
6. The system of claim 5 , wherein the time delay neural network determines the occurrence of an anomalous condition based on pattern recognition of the value of interest.
7. The system of claim 5 , wherein the time delay neural network is trained with machine learning.
8. The system of claim 5 , wherein the analyzed collected data includes sound signals.
9. The system of claim 8 , wherein the at least one component comprises a rotating machine.
10. The system of claim 1 , wherein the trained neural network comprises a convolutional neural network.
11. The system of claim 10 , wherein the analyzed collected data comprises image data.
12. The system of claim 10 , wherein the analyzed collected data comprises video data.
13. A data collection system in an industrial production line, the system comprising:
a plurality of data collectors comprising a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities or conditions of the data collector members of the swarm, wherein at least some of the data collector members are operatively coupled to at least one corresponding component of a plurality of components of the industrial production line, wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data, and wherein each of the plurality of input channels corresponds to a sensor located in an environment;
a data acquisition and analysis circuit structured to receive the collected data via the plurality of input channels and structured to analyze the received collected data using a trained neural network to determine an occurrence of a fault condition of at least one component of the plurality of components of the industrial production line, wherein the trained neural network is at least one: a probabilistic neural network, a time delay neural network, or a convolutional neural network, wherein the trained neural network is trained with sensor data to detect patterns in the received collected data from the sensor, wherein the trained neural network analyzes the received collected data to detect a pattern that exceeds a threshold in the received collected data, and wherein the trained neural network determines the occurrence of the fault condition based on the detecting the pattern that exceeds the threshold; and
a data response circuit structured to alter an operational parameter of the industrial production line based on the determination of the occurrence of the fault condition.
14. The system of claim 13 , wherein enhancing data collection comprises optimizing data collection.
15. The system of claim 13 , wherein the swarm of self-organized data collector members organize to delegate functions related to data collection, data storage, data processing, and data publishing across the swarm.
16. The system of claim 13 , wherein the swarm of self-organized data collector members are organized in a peer to peer manner.
17. The system of claim 13 , wherein the swarm of self-organized data collector members are organized in a hierarchical manner.
18. The system of claim 13 , wherein the swarm of self-organized data collector members are organized based on a plurality of rules corresponding to a workflow of the industrial production line.
19. The system of claim 13 , wherein the swarm of self-organized data collector members are organized to serially collect at least one of sensor, instrumentation, or telematic data from each of a series of machines that execute an industrial process on the industrial production line.
20. The system of claim 19 , wherein the industrial process comprises a robotic manufacturing process.
21. The system of claim 13 , wherein the swarm of self-organized data collector members act in an adaptive manner.
22. A method for data collection in an industrial production line, the method comprising:
acquiring collected data with a plurality of data collectors comprising a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities or conditions of the data collector members of the swarm, wherein at least some of the data collector members are operatively coupled to at least one corresponding component of a plurality of components of the industrial production line, wherein the plurality of data collectors is coupled to a plurality of input channels, and wherein each of the plurality of input channels corresponds to a sensor located in an environment;
receiving the collected data via the plurality of input channels;
analyzing the received collected data using a trained neural network to detect a value of interest in the collected data, wherein the value of interest includes a signature sensed by the sensor;
determining an occurrence of a fault condition of at least one component of the plurality of components of the industrial production line based on detecting the value of interest, wherein the trained neural network is at least one of a probabilistic neural network, a time delay neural network, or a convolutional neural network; and
altering an operational parameter of the industrial production line based on the determining of the occurrence of the fault condition.
23. The method of claim 22 , wherein the probabilistic neural network determines the occurrence of an anomalous condition based on pattern recognition of the value of interest.
24. The method of claim 22 , wherein the probabilistic neural network acts to recognize a fault of the at least one component of the industrial production line.
25. The method of claim 22 , wherein the time delay neural network determines the occurrence of an anomalous condition based on pattern recognition of the value of interest.
26. A data collection system in an industrial production line, the system comprising:
a plurality of data collectors comprising a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities or conditions of the data collector members of the swarm, wherein at least some of the data collector members are operatively coupled to at least one corresponding component of a plurality of components of the industrial production line, and wherein the plurality of data collectors acquires collected data from on-board sensors via one or more input interfaces or ports;
a data acquisition and analysis circuit structured to receive the collected data via the input interfaces or ports and structured to analyze the received collected data using a trained neural network to determine an occurrence of a fault condition of at least one component of the plurality of components of the industrial production line, wherein the trained neural network detects a value of interest in the collected data and determines the occurrence of the fault condition based on the value of interest, and wherein the value of interest includes a signature sensed by one or more of the on-board sensors; and
a data response circuit structured to alter an operational parameter of the industrial production line based on the determined occurrence of the fault condition.
27. The system of claim 26 , wherein the fault condition is at least one of overheating, noise, grinding gears, locked gears, excessive vibration, wobbling, under-inflation, over-inflation, unexpected fan vibrations, misalignment of bearings, premature bearing failure due to contamination or loss of bearing lubricant, or metal fatigue.
28. The system of claim 26 , wherein the swarm of self-organized data collector members organize to delegate functions related to data collection, data storage, data processing, and data publishing across the swarm.