Methods and systems for the industrial internet of things

• US 10,416,639 B2
• Filed: 11/02/2018
• Issued: 09/17/2019
• 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): Feild, Joseph H
• Assistant Examiner(s): Point, Rufus C
• Application Number:
  US16/179,037
• Publication Number:
  US 20190079483A1
• Time in Patent Office: 319 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

The system generally includes a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor. The multiple outputs include a first and second output configured to be switchable between a condition in which the first output is configured to switch between delivery of 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 the second output. Each of multiple inputs is configured to be individually assigned to any of the multiple outputs. Unassigned outputs are configured to be switched off producing a high-impedance state. The local data collection system is configured to manage 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. The local data collection system is configured to create data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes.

Claims

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

a platform including a computing environment connected to a local data collection system having at least a first sensor signal and a second sensor signal obtained from at least the first machine in the industrial environment;

a first sensor in the local data collection system configured to be connected to the first machine;

a second sensor in the local data collection system; and

a crosspoint switch in the local data collection system having multiple inputs and multiple outputs including a first input connected to the first sensor and a second input connected to the second sensor, wherein the multiple outputs include a first output and a second output configured to be switchable between a condition in which the first output is configured to switch between delivery of 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 the second output, wherein the local data collection system is configured to manage 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 wherein the local data collection system is configured to create data acquisition routes based on hierarchical templates that each include the data collection bands related to machines associated with the data acquisition routes.

2. The system of claim 1 wherein the data collection bands define an overall waveform derived from a vibration envelope.

3. The system of claim 1 wherein the local data collection system includes a neural net expert system using intelligent management of the data collection bands.

4. The system of claim 1 wherein at least one of the hierarchical templates is associated with at least one of:

(i) multiple interconnected elements of the first machine, (ii) similar elements associated with at least the first machine and a second machine, and (iii) at least the first machine being proximate in location to a second machine.

5. The system of claim 1 wherein the local data collection system includes a graphical user interface system configured to manage the data collection bands.

6. The system of claim 5 wherein the graphical user interface system includes an expert system diagnostic tool.

7. The system of claim 1 wherein the platform includes cloud-based, machine pattern analysis of state information from multiple sensors to provide anticipated state information for the industrial environment.

8. The system of claim 1 wherein the platform is configured to provide self-organization of data pools based on at least one of utilization metrics and yield metrics.

9. The system of claim 1 wherein the platform includes a self-organized swarm of industrial data collectors.

10. The system of claim 1 wherein the local data collection system includes a wearable haptic user interface for an industrial sensor data collector with at least one of vibration, heat, electrical, and sound outputs.

11. The system of claim 1 wherein multiple inputs of the crosspoint switch include a third input connected to the second sensor and a fourth input connected to the second sensor, and wherein the first sensor signal is from a single-axis sensor at an unchanging location associated with the first machine.

12. The system of claim 11 wherein the second sensor is a three-axis sensor.

13. The system of claim 12 wherein the local data collection system is configured to record gap-free digital waveform data simultaneously from at least the first input, the second input, the third input, and the fourth input.

14. The system of claim 13 wherein the platform is configured to determine a change in relative phase based on the simultaneously recorded gap-free digital waveform data.

15. The system of claim 14 wherein the second sensor is configured to be movable to a plurality of positions associated with the first machine while obtaining the simultaneously recorded gap-free digital waveform data.

16. The system of claim 14 wherein multiple outputs of the crosspoint switch include a third output and fourth output, and wherein the second, third, and fourth outputs are assigned together to a sequence of tri-axial sensors each located at different positions associated with the machine.

17. The system of claim 14 wherein the platform is configured to determine an operating deflection shape based on the change in relative phase and the simultaneously recorded gap-free digital waveform data.

18. The system of claim 11 wherein the unchanging location is a position associated with rotating shaft of the first machine.

19. The system of claim 16 wherein tri-axial sensors in the sequence of the tri-axial sensors are at least one of:

(i) each located at different positions on the first machine but are each associated with different bearings in the machine, and (ii) each located at similar positions associated with similar bearings but are each associated with different machines.

20. The system of claim 14 wherein the local data collection system is configured to obtain the simultaneously recorded gap-free digital waveform data from the first machine while the first machine and a second machine are both in operation.

21. The system of claim 20 wherein the local data collection system is configured to characterize a contribution from the first machine and the second machine in the simultaneously recorded gap-free digital waveform data from the first machine.

22. The system of claim 14 wherein the simultaneously recorded gap-free digital waveform data has a duration that is in excess of one minute.