Methods and systems for the industrial internet of things

• US 10,481,572 B2
• Filed: 02/14/2019
• Issued: 11/19/2019
• Est. Priority Date: 05/09/2016
• Status: Active Grant

• Current Assignee: Strong Force IoT Portfolio 2016, LLC (Strong Force Intellectual Capital, LLC)
• Original Assignee: Strong Force IoT Portfolio 2016, LLC (Strong Force Intellectual Capital, LLC)
• Inventors: Cella, Charles Howard, Duffy, Jr., Gerald William, McGuckin, Jeffrey P.
• Primary Examiner(s): Rivera Vargas, Manuel A
• Application Number:
  US16/275,729
• Publication Number:
  US 20190187656A1
• Time in Patent Office: 278 Days
• CPC Class Codes: G01H 1/00 Measuring characteristics of vibrations in solids by using direct conduction to the detector G01H9/00, G01H11/00 take precedence
• CPC Class Codes: G01M 13/028 Acoustic or vibration analysis
• CPC Class Codes: G01M 13/045 Acoustic or vibration analysis
• CPC Class Codes: G05B 11/32 with inputs from more than one sensing element; with outputs to more than one correcting element
• CPC Class Codes: G05B 19/042 using digital processors G05B19/05 takes precedence
• CPC Class Codes: G05B 19/0423 Input/output
• CPC Class Codes: G05B 19/0425 Safety, monitoring
• CPC Class Codes: G05B 19/418 Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
• CPC Class Codes: G05B 19/4183 characterised by data acquisition, e.g. workpiece identification
• CPC Class Codes: G05B 19/4185 characterised by the network communication
• CPC Class Codes: G05B 2219/23253 Expert system
• CPC Class Codes: G05B 2219/23258 GUI graphical user interface, icon, function bloc editor, labview
• CPC Class Codes: G05B 2219/24015 Monitoring
• CPC Class Codes: G05B 2219/25174 Ethernet
• CPC Class Codes: G05B 2219/25255 Neural network
• CPC Class Codes: G05B 2219/25268 PLD programmable logic device
• CPC Class Codes: G05B 2219/31156 Network structure, internet
• CPC Class Codes: G05B 2219/31282 Data acquisition, BDE MDE
• CPC Class Codes: G05B 2219/33333 Network multiprocessing
• CPC Class Codes: G05B 23/0221 Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
• CPC Class Codes: G05B 23/0272 : Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user
• CPC Class Codes: G05B 23/0283 : Predictive maintenance, e.g. involving the monitoring of a system and, based on the monitoring results, taking decisions on the maintenance schedule of the monitored system; Estimating remaining useful life [RUL] preventive maintenance, i.e. planning maintenance according to the available resources without monitoring the system G06Q10/06
• CPC Class Codes: G06F 3/0488 : using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/02 : Neural networks
• CPC Class Codes: G06N 3/042 : Knowledge-based neural networks; Logical representations of neural networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 5/047 : Pattern matching networks; Rete networks
• CPC Class Codes: H04L 67/10 : in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04L 67/125 : involving control of end-device applications over a network
• CPC Class Codes: H04Q 2209/20 : using a distributed architecture
• CPC Class Codes: H04Q 2209/40 : using a wireless architecture
• CPC Class Codes: H04Q 9/00 : Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
• CPC Class Codes: H04W 4/38 : for collecting sensor information
• CPC Class Codes: H04W 84/18 : Self-organising networks, e.g. ad-hoc networks or sensor networks
• CPC Class Codes: H04W 84/20 : Master-slave selection or change arrangements
• CPC Class Codes: Y02P 80/10 : Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
• CPC Class Codes: Y02P 90/02 : Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

Methods for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment include obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine and connecting a first input of a crosspoint switch to a first sensor and a second input of the crosspoint switch to a second sensor. The local data collection system manages data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope. The local data collection system includes a neural net expert system using intelligent management of the data collection bands.

Claims

1. A method for data collection, processing, and utilization of signals with a platform monitoring at least a first element in a first machine in an industrial environment, the method comprising:

obtaining, automatically with a computing environment, at least a first sensor signal and a second sensor signal with a local data collection system that monitors at least the first machine;

connecting a first input of a crosspoint switch of the local data collection system to a first sensor and a second input of the crosspoint switch to a second sensor in the local data collection system;

switching between a condition in which a first output of the crosspoint switch alternates between delivery of at least the first sensor signal and the second sensor signal and a condition in which there is simultaneous delivery of the first sensor signal from the first output and the second sensor signal from a second output of the crosspoint switch; and

planning data acquisition routes based on hierarchical templates associated with at least the first element in the first machine in the industrial environment,

wherein the local data collection system manages data collection bands that define a specific frequency band and at least one of a group of spectral peaks, a true-peak level, a crest factor derived from a time waveform, and an overall waveform derived from a vibration envelope, and wherein the local data collection system includes a neural net expert system using intelligent management of the data collection bands.

2. The method of claim 1 wherein the first sensor signal and the second sensor signal are continuous vibration data from the industrial environment.

3. The method of claim 1 wherein the second sensor in the local data collection system is connected to the first machine.

4. The method of claim 1 wherein the second sensor in the local data collection system is connected to a second machine in the industrial environment.

5. The method of claim 1 further comprising comparing, automatically with the computing environment, relative phases of the first and second sensor signals.

6. The method of claim 1 wherein the first sensor is a single-axis sensor and the second sensor is a three-axis sensor.

7. The method of claim 1 wherein at least the first input of the crosspoint switch includes internet protocol front-end signal conditioning for improved signal-to-noise ratio.

8. The method of claim 1 further comprising continuously monitoring at least a third input of the crosspoint switch with an alarm having a pre-determined trigger condition when the third input is unassigned to any of multiple outputs on the crosspoint switch.

9. The method of claim 8 wherein the local data collection system includes distributed complex programmable hardware device (CPLD) chips each dedicated to a data bus for logic control of the multiple multiplexing units and the multiple data acquisition units that receive the multiple data streams from the multiple machines in the industrial environment.

10. The method of claim 1 wherein the local data collection system provides high-amperage input capability using solid state relays.

11. The method of claim 9 further comprising powering down at least one of an analog sensor channel and a component board of the local data collection system.

12. The method of claim 1 wherein the local data collection system includes an external voltage reference for an A/D zero reference that is independent of a voltage of the first sensor and the second sensor.

13. The method of claim 1 wherein the local data collection system includes a phase-lock loop band-pass tracking filter that obtain slow-speed RPMs and phase information.

14. The method of claim 1 further comprising digitally deriving phase using on-board timers relative to at least one trigger channel and at least one of multiple inputs on the crosspoint switch.

15. The method of claim 1 further comprising auto-scaling with a peak-detector using a separate analog-to-digital converter for peak detection.

16. The method of claim 1 further comprising routing at least one trigger channel that is one of raw and buffered into at least one of multiple inputs on the crosspoint switch.

17. The method of claim 1 further comprising increasing input oversampling rates with at least one delta-sigma analog-to-digital converter to reduce sampling rate outputs and to minimize anti-aliasing filter requirements.

18. The method of claim 9 wherein the distributed CPLD chips each dedicated to the data bus for logic control of the multiple multiplexing units and the multiple data acquisition units includes as high-frequency crystal clock reference divided by at least one of the distributed CPLD chips for at least one delta-sigma analog-to-digital converter to achieve lower sampling rates without digital resampling.

19. The method of claim 1 further comprising obtaining long blocks of data at a single relatively high-sampling rate with the local data collection system as opposed to multiple sets of data taken at different sampling rates.

20. The method of claim 19 wherein the single relatively high-sampling rate corresponds to a maximum frequency of about forty kilohertz.

21. The method of claim 19 wherein the long blocks of data are for a duration that is in excess of one minute.

22. The method of claim 1 wherein the local data collection system includes multiple multiplexing units and multiple data acquisition units receiving multiple data streams from multiple machines in the industrial environment.

23. The method of claim 1 wherein the local data collection system creates data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes.

24. The method of claim 23 wherein at least one of the hierarchical templates is associated with multiple interconnected elements of the first machine.

25. The method of claim 24 wherein at least one of the hierarchical templates is associated with similar elements associated with at least the first machine and a second machine.

26. The method of claim 24 wherein at least one of the hierarchical templates is associated with at least the first machine being proximate in location to a second machine.

27. The method of claim 1 further comprising controlling a graphical user interface system of the local data collection system to manage the data collection bands, wherein the graphical user interface system includes an expert system diagnostic tool.

28. The method of claim 27 wherein the computing environment of the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment.

29. The method of claim 27 wherein the computing environment of the platform provides self-organization of data pools based on at least one of utilization metrics and yield metrics.

30. The method of claim 27 wherein the computing environment of the platform includes a self-organized swarm of industrial data collectors.

31. The method of claim 27 wherein each of multiple inputs of the crosspoint switch is individually assignable to any of multiple outputs of the crosspoint switch.