METHODS AND SYSTEMS FOR SENSOR FUSION IN A PRODUCTION LINE ENVIRONMENT
Systems and methods for data collection in an industrial production system including a plurality of components are disclosed. An example system may include a sensor communication circuit structured to interpret a plurality of data values from a sensed parameter group, the sensed parameter group including a plurality of sensors including a vibration sensor and a temperature sensor, and the plurality of sensors operatively coupled to at least one of the plurality of components; a data analysis circuit structured to detect an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and a response circuit structured to modify a production-related operating parameter of the industrial production system in response to the detected operating condition.
1 . A system for data collection in an industrial production system including a plurality of components, the system for data collection comprising:
a sensor communication circuit structured to interpret a plurality of data values from a sensed parameter group, wherein the sensed parameter group includes a plurality of sensors including a vibration sensor and a temperature sensor, and wherein the plurality of sensors are operatively coupled to at least one of the plurality of components;
a data analysis circuit structured to detect an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and
a response circuit structured to modify a production-related operating parameter of the industrial production system in response to the detected operating condition.
2 . The system of claim 1 , wherein the sensed parameter group comprises a fused plurality of sensors including the vibration sensor and the temperature sensor.
3 . The system of claim 2 , further comprising:
a pattern recognition circuit structured to determine a recognized pattern value in response to the plurality of data values from the sensed parameter group comprising the fused plurality of sensors, wherein the recognized pattern value includes a secondary value comprising a component overtemperature value.
4 . The system of claim 1 , further comprising:
an input selection system that determines a fusion of the plurality of sensors including the vibration sensor and the temperature sensor based on learning from feedback to improve prediction of the operating condition.
5 . The system of claim 1 , wherein the data analysis circuit analyzes the data values including a variation in the temperature over time in fusion with the vibration pattern from the vibration sensor.
6 . The system of claim 1 , wherein the data analysis circuit omits an average temperature from a variation in the temperature over time to produce a resulting delta change in the temperature that is processed through a Fourier transform to produce a frequency spectrum, and wherein the data analysis circuit determines whether the frequency spectrum correlates to the operating condition.
7 . The system of claim 1 , further comprising:
a library, wherein the library stores a plurality of vibration fingerprints and associated operating conditions, and wherein the plurality of vibration fingerprints include the stored vibration fingerprint that matches the vibration pattern.
8 . The system of claim 7 , wherein each of the plurality of vibration fingerprints stored in the library includes at least one of a frequency, a spectra, a peak frequency location, a wave peak shape, a waveform shape, a wave envelope shape, phase information, or a phase shift.
9 . The system of claim 1 , wherein the data values from the temperature sensor and the data values from the vibration sensor are multiplexed into a data steam that combines the data values in a time series.
10 . The system of claim 1 , further comprising:
a peak detection circuit structured to verify consistency of timing of peak values between the data values from the vibration sensor and the data values from the temperature sensor.
11 . The system of claim 2 , wherein the fused plurality of sensors is self-organized.
12 . The system of claim 1 , further comprising:
an expert system seeded with the data values from the vibration sensor to determine if a change in a parameter of a machine of the industrial production system affects an intrinsic operation of the machine.
13 . A computer-implemented method for data collection in an industrial production system including a plurality of components, the method comprising:
interpreting a plurality of data values from a sensed parameter group, wherein the sensed parameter group includes a plurality of sensors including a vibration sensor and a temperature sensor, and wherein the plurality of sensors are operatively coupled to at least one of the plurality of components;
detecting an operating condition of the industrial production system based on detecting that the data values from the vibration sensor indicate a vibration pattern that matches a stored vibration fingerprint together with detecting that the data values from the temperature sensor indicate a change in a temperature; and
modifying a production-related operating parameter of the industrial production system in response to the detected operating condition.
14 . The computer-implemented method of claim 13 , wherein the sensed parameter group comprises a fused plurality of sensors including the vibration sensor and the temperature sensor.
15 . The computer-implemented method of claim 14 , further comprising:
determining a recognized pattern value in response to the plurality of data values from the sensed parameter group comprising the fused plurality of sensors, wherein the recognized pattern value includes a secondary value comprising a component overtemperature value.
16 . The computer-implemented method of claim 13 , further comprising:
determining a fusion of the plurality of sensors including the vibration sensor and the temperature sensor based on learning from feedback to improve prediction of the operating condition.
17 . The computer-implemented method of claim 13 , further comprising:
analyzing the data values including a variation in the temperature over time in fusion with the vibration pattern from the vibration sensor.
18 . The computer-implemented method of claim 13 , further comprising:
omitting an average temperature from a variation in the temperature over time to produce a resulting delta change in the temperature;
processing the resulting delta change in the temperature through a Fourier transform to produce a frequency spectrum; and
determining whether the frequency spectrum correlates to the operating condition.
19 . The computer-implemented method of claim 13 , wherein the stored vibration fingerprint is included in a library that stores a plurality of vibration fingerprints and associated operating conditions.
20 . The computer-implemented method of claim 19 , wherein each of the plurality of vibration fingerprints stored in the library includes at least one of a frequency, a spectra, a peak frequency location, a wave peak shape, a waveform shape, a wave envelope shape, phase information, or a phase shift.
21 . The computer-implemented method of claim 13 , further comprising:
multiplexing the data values from the temperature sensor and the data values from the vibration sensor into a data steam that combines the data values in a time series.
22 . The computer-implemented method of claim 13 , further comprising:
verifying consistency of timing of peak values between the data values from the vibration sensor and the data values from the temperature sensor.
23 . The computer-implemented method of claim 13 , further comprising:
detecting the operating condition further based on one or more additional parameters, wherein the one or more additional parameters includes at least one of a decreased flow rate, an increase in the temperature, a material in use, a duration of use, a power source, an installation, or an ambient sensed condition including at least one of an ambient noise or an ambient temperature.
24 . The computer-implemented method of claim 13 , further comprising:
seeding an expert system with the data values from the vibration sensor to determine if a change in a parameter of a machine of the industrial production system affects an intrinsic operation of the machine.
25 . The computer-implemented method of claim 24 , further comprising:
determining that the change in the parameter alters a vibration fingerprint of the machine such that a stored vibration fingerprint of the machine for a normal operation is no longer correct.