# US 10,481,572 B2 · Methods and systems for the industrial internet of things

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- Number: US 10,481,572 B2
- Document ID: US10481572B2
- Status: Granted
- Kind: B2
- Application number: 16/275,729
- Filed: 2019-02-14
- Published: 2019-06-20
- Granted: 2019-11-19
- Earliest claimed: 2016-05-09
- Assignees: Strong Force IOT Portfolio 2016, LLC
- Applicants: Strong Force IOT Portfolio 2016, LLC
- Inventors: Charles Howard Cella; Gerald William Duffy, Jr.; Jeffrey P. McGuckin
- Web page: https://patentgrep.com/patent/US10481572B2

## CPC

- G05B 19/0425: {Safety, monitoring}
- G01H 1/00: Measuring {characteristics of} vibrations in solids by using direct conduction to the detector (G01H9/00, G01H11/00 take precedence)
- G05B 11/32: with inputs from more than one sensing element; with outputs to more than one correcting element
- G05B 19/0423: {Input/output}
- 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]
- G05B 19/4183: {characterised by data acquisition, e.g. workpiece identification}
- G05B 19/4185: {characterised by the network communication}
- 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)}
- G06F 3/0488: using a touch-screen or digitiser, e.g. input of commands through traced gestures
- G06N 3/02: Neural networks
- G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks
- G06N 3/0499: Feedforward networks
- G06N 5/047: Pattern matching networks; Rete networks
- G06N 20/00: Machine learning
- H04L 67/10: in which an application is distributed across nodes in the network (software deployment G06F8/60; multiprogramming arrangements G06F9/46)
- 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
- H04L 67/125: involving control of end-device applications over a network
- 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
- H04W 4/38: for collecting sensor information
- H04W 84/18: Self-organising networks, e.g. ad-hoc networks or sensor networks
- G01M 13/028: Acoustic or vibration analysis
- G01M 13/045: Acoustic or vibration analysis
- G05B 19/042: using digital processors (G05B19/05 takes precedence)
- 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}
- G05B 23/0272: {Presentation of monitored results, e.g. selection of status reports to be displayed; Filtering information to the user}
- G05B 2219/23253: Expert system
- G05B 2219/23258: GUI graphical user interface, icon, function bloc editor, labview
- G05B 2219/24015: Monitoring
- G05B 2219/25174: Ethernet
- G05B 2219/25255: Neural network
- G05B 2219/25268: PLD programmable logic device
- G05B 2219/31156: Network structure, internet
- G05B 2219/31282: Data acquisition, BDE MDE
- G05B 2219/33333: Network multiprocessing
- H04Q 2209/20: using a distributed architecture
- H04Q 2209/40: using a wireless architecture
- H04W 84/20: Leader-follower arrangements
- Y02P 80/10: Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
- Y02P 90/02: Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G01H: MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04Q: SELECTING (switches, relays, selectors H01H; wireless communication networks H04W)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## 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

### Claim 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.

### Claim 2 (depends on 1)

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

### Claim 3 (depends on 1)

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

### Claim 4 (depends on 1)

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.

### Claim 5 (depends on 1)

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

### Claim 6 (depends on 1)

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

### Claim 7 (depends on 1)

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.

### Claim 8 (depends on 1)

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.

### Claim 9 (depends on 8)

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.

### Claim 10 (depends on 1)

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

### Claim 11 (depends on 9)

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.

### Claim 12 (depends on 1)

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.

### Claim 13 (depends on 1)

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.

### Claim 14 (depends on 1)

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.

### Claim 15 (depends on 1)

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

### Claim 16 (depends on 1)

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.

### Claim 17 (depends on 1)

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.

### Claim 18 (depends on 9)

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.

### Claim 19 (depends on 1)

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.

### Claim 20 (depends on 19)

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

### Claim 21 (depends on 19)

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

### Claim 22 (depends on 1)

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.

### Claim 23 (depends on 1)

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.

### Claim 24 (depends on 23)

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

### Claim 25 (depends on 24)

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.

### Claim 26 (depends on 24)

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.

### Claim 27 (depends on 1)

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.

### Claim 28 (depends on 27)

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.

### Claim 29 (depends on 27)

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.

### Claim 30 (depends on 27)

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

### Claim 31 (depends on 27)

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.