# US 2018/0321672 A1 · Methods and systems for a data marketplace in an industrial internet of things environment

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- Number: US 2018/0321672 A1
- Document ID: US20180321672A1
- Status: Published application
- Kind: A1
- Application number: 15/973,336
- Filed: 2018-05-07
- Published: 2018-11-08
- Earliest claimed: 2016-05-09
- Assignees: Strong Force IOT Portfolio 2016, LLC
- Applicants: StrongForce IoT Portfolio 2016, LLC
- Inventors: Charles Howard Cella; Gerald William Duffy, Jr.; Jeffrey P. McGuckin; Mehul Desai
- Web page: https://patentgrep.com/patent/US20180321672A1

## CPC

- G05B 19/4185: {characterised by the network communication}
- B62D 15/0215: {by measuring on the steering column}
- G01M 13/028: Acoustic or vibration analysis
- G01M 13/04: Bearings
- G01M 13/045: Acoustic or vibration analysis
- G05B 13/028: {using expert systems only}
- G05B 19/4183: {characterised by data acquisition, e.g. workpiece identification}
- G05B 19/4184: {characterised by fault tolerance, reliability of production system}
- G05B 19/41845: {characterised by system universality, reconfigurability, modularity}
- G05B 19/41865: {characterised by job scheduling, process planning, material flow}
- G05B 19/41875: {characterised by quality surveillance of production}
- 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 23/0229: {knowledge based, e.g. expert systems; genetic algorithms}
- G05B 23/024: {Quantitative history assessment, e.g. mathematical relationships between available data; Functions therefor; Principal component analysis [PCA]; Partial least square [PLS]; Statistical classifiers, e.g. Bayesian networks, linear regression or correlation analysis; Neural networks}
- G05B 23/0264: {Control of logging system, e.g. decision on which data to store; time-stamping measurements}
- 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)}
- G05B 23/0286: {Modifications to the monitored process, e.g. stopping operation or adapting control}
- G05B 23/0289: {Reconfiguration to prevent failure, e.g. usually as a reaction to incipient failure detection}
- G05B 23/0291: {Switching into safety or degraded mode, e.g. protection and supervision after failure}
- G05B 23/0294: {Optimizing process, e.g. process efficiency, product quality}
- G05B 23/0297: {Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results}
- G06F 3/0608: {Saving storage space on storage systems}
- G06F 3/0619: {in relation to data integrity, e.g. data losses, bit errors}
- G06F 3/0635: {by changing the path, e.g. traffic rerouting, path reconfiguration}
- G06F 3/067: {Distributed or networked storage systems, e.g. storage area networks [SAN], network attached storage [NAS]}
- G06F 16/2477: {Temporal data queries}
- G06F 18/2178: {based on feedback of a supervisor}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/02: Neural networks
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 3/047: Probabilistic or stochastic networks
- G06N 3/0499: Feedforward networks
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 5/046: Forward inferencing; Production systems
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- G06Q 10/04: Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202)
- G06Q 10/0639: Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q 30/02: Marketing; Price estimation or determination; Fundraising
- G06Q 30/0278: {Product appraisal}
- G06Q 30/06: Buying, selling or leasing transactions
- G06Q 50/00: Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism (healthcare informatics G16H)
- G06V 10/7784: {based on feedback from supervisors}
- G06V 10/82: using neural networks
- G16Z 99/00: Subject matter not provided for in other main groups of this subclass
- H02M 1/12: Arrangements for reducing harmonics from AC input or output
- H03M 1/12: Analogue/digital converters ({H03M1/001 – } H03M1/10 take precedence)
- H04B 17/23: Indication means, e.g. displays, alarms, audible means
- H04B 17/26: using historical data, averaging values or statistics
- H04B 17/29: Performance testing
- H04B 17/309: Measuring or estimating channel quality parameters
- H04B 17/318: Received signal strength
- H04B 17/345: Interference values ({signal-to-interference ratio [SIR] or carrier-to-interference ratio [CIR]} H04B17/336)
- H04L 1/0002: {by adapting the transmission rate}
- H04L 1/0041: {Arrangements at the transmitter end}
- H04L 1/18: Automatic repetition systems, e.g. Van Duuren systems
- H04L 1/1874: {Buffer management}
- H04L 67/1097: for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
- 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
- H04W 4/38: for collecting sensor information
- H04W 4/70: Services for machine-to-machine communication [M2M] or machine type communication [MTC]
- B62D 5/0463: {calculating assisting torque from the motor based on driver input}
- F01D 21/003: {Arrangements for testing or measuring (for measuring vibrations G01H)}
- F01D 21/12: responsive to temperature
- F01D 21/14: responsive to other specific conditions
- G05B 19/042: using digital processors (G05B19/05 takes precedence)
- G05B 23/02: Electric testing or monitoring
- G05B 23/0208: {characterized by the configuration of the monitoring system}
- G05B 2219/32287: Medical, chemical, biological laboratory
- G05B 2219/35001: Data input, data handling, programming, monitoring of nc
- G05B 2219/37337: Noise, acoustic emission, sound
- G05B 2219/37351: Detect vibration, ultrasound
- G05B 2219/37434: Measuring vibration of machine or workpiece or tool
- G05B 2219/37537: Virtual sensor
- G05B 2219/40115: Translate goal to task program, use of expert system
- G05B 2219/45004: Mining
- G05B 2219/45129: Boring, drilling
- G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)}
- G06F 18/21: Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F 18/217: {Validation; Performance evaluation; Active pattern learning techniques}
- G06F 18/25: Fusion techniques
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- H04B 17/40: of relay systems
- H04L 1/0009: {by adapting the channel coding (H04L1/1812 takes precedence)}
- H04L 5/0064: {Rate requirement of the data, e.g. scalable bandwidth, data priority}
- H04L 67/306: User profiles
- 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]
- Y02P 90/80: Management or planning
- Y04S 50/00: Market activities related to the operation of systems integrating technologies related to power network operation or related to communication or information technologies
- Y04S 50/12: Billing, invoicing, buying or selling transactions or other related activities, e.g. cost or usage evaluation
- Y10S 707/99939: Privileged access
- 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)
- B62D: MOTOR VEHICLES; TRAILERS (steering, or guiding on a desired track, of agricultural machines or implements A01B69/00; wheels, castors, axles, increasing wheel adhesion B60B; vehicle tyres, tyre inflation or tyre changing B60C; connections between vehicles of a train or the like B60D; vehicles for use on rail and road, amphibious or convertible vehicles B60F; suspension arrangements B60G; heating, cooling, ventilating or other air treating devices B60H; windows, windscreens, non-fixed roofs, doors or similar devices, protective coverings for vehicles not in use B60J; propulsion plant arrangements, auxiliary drives, transmissions, controls, instrumentation or dashboards B60K; electric equipment or propulsion of electrically-propelled vehicles B60L; power supply for electrically-propelled vehicles B60M; passenger accommodation not otherwise provided for B60N; adaptations for load transportation or to carry special loads or objects B60P; arrangement of signalling or lighting devices, the mounting or supporting thereof or circuits therefor, for vehicles in general B60Q; vehicles, vehicle fittings or vehicle parts not otherwise provided for B60R; servicing, cleaning, repairing, supporting, lifting, or manoeuvring, not otherwise provided for, B60S; brake arrangements, brake control systems or parts thereof B60T; air-cushion vehicles B60V; motor-cycles, accessories therefor B62J, B62K; testing of vehicles G01M)
- G01M: TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G16Z: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- H02M: APPARATUS FOR CONVERSION BETWEEN AC AND AC, BETWEEN AC AND DC, OR BETWEEN DC AND DC, AND FOR USE WITH MAINS OR SIMILAR POWER SUPPLY SYSTEMS; CONVERSION OF DC OR AC INPUT POWER INTO SURGE OUTPUT POWER; CONTROL OR REGULATION THEREOF (transformers H01F; dynamo-electric converters H02K47/00; controlling transformers, reactors or choke coils, control or regulation of electric motors, generators or dynamo-electric converters H02P)
- H03M: CODING; DECODING; CODE CONVERSION IN GENERAL (using fluidic means F15C4/00; optical analogue/digital converters G02F7/00; coding, decoding or code conversion, specially adapted for particular applications, see the relevant subclasses, e.g. G01D, G01R, G06F, G06T, G09G, G10L, G11B, G11C, H04B, H04L, H04M, H04N; ciphering or deciphering for cryptography or other purposes involving the need for secrecy G09C)
- H04B: TRANSMISSION
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)
- F01D: NON-POSITIVE DISPLACEMENT MACHINES OR ENGINES, e.g. STEAM TURBINES (machines or engines for liquids F03; non-positive displacement pumps F04D)
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y10S: TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS

## Abstract

A data collection system in an industrial environment includes a data collector coupled to a plurality of input channels, wherein a collector route determines a subset of the input channels for data collection, the collector route selected based on a data marketplace indicator; a data storage structured to store a plurality of collector routes and collected data that correspond to the input channels, each comprising a different data collection routine for the input channels; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each corresponding to at least one of the input channels; and a data analysis circuit structured to analyze the collected data from the input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data, wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.

## Claims

### Claim 1

A data collection system in an industrial environment, the system comprising:
a data collector communicatively coupled to a plurality of input channels, wherein a collector route determines a subset of the plurality of input channels for data collection, the collector route selected based on a data marketplace indicator;
a data storage structured to store a plurality of collector routes and collected data that correspond to the plurality of input channels, wherein the plurality of collector routes each comprise a different data collection routine for the plurality of input channels;
a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and
a data analysis circuit structured to analyze the collected data from the plurality of input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data,
wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.

### Claim 2 (depends on 1)

The system of claim 1, wherein the received data marketplace indicator is received from a self-organizing data marketplace for industrial Internet-of-things data that comprises at least in part data collected by the data collection system.

### Claim 3 (depends on 2)

The system of claim 2, wherein the self-organizing data marketplace is organized based on training a machine-learning self-organizing facility with a training set and feedback from measures of marketplace success with respect to stored collected data.

### Claim 4 (depends on 3)

The system of claim 3, wherein the machine-learning self-organizing facility learns to improve the measures of success based on determining user favored combinations of collected data through the selection of collection routines from the plurality of collection routines.

### Claim 5 (depends on 3)

The system of claim 3, wherein the machine-learning self-organizing facility is an expert system utilizing a neural network to classify the collected data for marketplace analysis.

### Claim 6 (depends on 2)

The system of claim 2, wherein the self-organizing data marketplace utilizes a self-organizing data pool comprising data collected by the data collection system.

### Claim 7 (depends on 6)

The system of claim 6, wherein the self-organizing data pool includes a data storage profile with a storage time definition for the collected data, each data storage profile corresponding to at least one of the detection values from data being collected.

### Claim 8 (depends on 2)

The system of claim 2, wherein a network data transport system interconnects the data collection system and distributed data process facilities of the self-organizing data marketplace.

### Claim 9 (depends on 2)

The system of claim 2, wherein the self-organizing data marketplace utilizes a self-organizing map that creates a topology for the stored collected data.

### Claim 10 (depends on 1)

The system of claim 1, wherein the data storage comprises local data acquisition calibration information, and the received data marketplace indictor is at least in part determined by a marketplace success measure based on a market usage of the stored local data acquisition calibration information.

### Claim 11 (depends on 1)

The system of claim 1, wherein the data storage comprises local data acquisition maintenance information, and the received data marketplace indictor is at least in part determined by a marketplace success measure based on a market usage of the stored local data acquisition maintenance information.

### Claim 12 (depends on 1)

The system of claim 1, wherein the data collector is one of a plurality of self-organized swarm of data collectors, wherein the plurality of self-organized swarm of data collectors organize among themselves to optimize data collection based at least in part on the received data marketplace indicator.

### Claim 13 (depends on 8)

The system of claim 8, wherein the plurality of self-organized swarm of data collectors coordinate with one another to optimize data collection based at least in part on the received data marketplace indicator.

### Claim 14 (depends on 1)

The system of claim 1, wherein the data collector receives a trigger signal based on the received data marketplace indicator, wherein the trigger signal sets up a data value trigger level for at least one of the plurality of input channels.

### Claim 15

A computer-implemented method for monitoring data collection in an industrial environment, the method comprising:
providing a data collector communicatively coupled to a plurality of input channels, wherein a collector route determines a subset of the plurality of input channels for data collection, the collector route selected based on a data marketplace indicator;
providing a data storage structured to store a plurality of collector routes and collected data that correspond to the plurality of input channels, wherein the plurality of collector routes each comprise a different data collection routine for the plurality of input channels;
providing a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and
providing a data analysis circuit structured to analyze the collected data from the plurality of input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data,
wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.

### Claim 16 (depends on 15)

The method of claim 15, wherein the received data marketplace indicator is received from a self-organizing data marketplace for industrial Internet-of-things data that comprises at least in part data collected by the data collection system.

### Claim 17 (depends on 16)

The method of claim 16, wherein the self-organizing data marketplace is organized based on training a machine-learning self-organizing facility with a training set and feedback from measures of marketplace success with respect to stored collected data.

### Claim 18

A monitoring apparatus for data collection in an industrial environment, the apparatus comprising:
a data collector component communicatively coupled to a plurality of input channels, wherein a collector route determines a subset of the plurality of input channels for data collection, the collector route selected based on a data marketplace indicator;
a data storage component structured to store a plurality of collector routes and collected data that correspond to the plurality of input channels, wherein the plurality of collector routes each comprise a different data collection routine for the plurality of input channels;
a data acquisition component structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and
a data analysis component structured to analyze the collected data from the plurality of input channels and evaluate a selected collection routine of the data collector based on the analyzed collected data,
wherein the selected collection routine is switched to a second collection routine based on a received data marketplace indicator.

### Claim 19 (depends on 18)

The apparatus of claim 18, wherein the received data marketplace indicator is received from a self-organizing data marketplace for industrial Internet-of-things data that comprises at least in part data collected by the data collection system.

### Claim 20 (depends on 19)

The apparatus of claim 19, wherein the self-organizing data marketplace is organized based on training a machine-learning self-organizing facility with a training set and feedback from measures of marketplace success with respect to stored collected data.