# US 2022/0197255 A1 · Methods and systems for the industrial internet of things

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- Number: US 2022/0197255 A1
- Document ID: US20220197255A1
- Status: Published application
- Kind: A1
- Application number: 17/692,708
- Filed: 2022-03-11
- Published: 2022-06-23
- Earliest claimed: 2016-05-09
- Assignees: Strong Force IOT Portfolio 2016, LLC
- Applicants: Strong Force IoT Portfolio 2016, LLC
- Inventors: Charles Howard Cella; Mehul Desai; Gerald William Duffy, JR.; Jeffrey P. McGuckin; Tracey Ho; John Segui; Steven Blumenthal; Chun Meng
- Web page: https://patentgrep.com/patent/US20220197255A1

## CPC

- G05B 19/4155: characterised by program execution, i.e. part program or machine function execution, e.g. selection of a program
- G05B 19/4183: {characterised by data acquisition, e.g. workpiece identification}
- 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}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- 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 3/086: using evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 3/09: Supervised learning
- G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming
- G06N 20/00: Machine learning
- G05B 2219/31156: Network structure, internet
- G05B 2219/31282: Data acquisition, BDE MDE
- G05B 2219/33039: Learn for different measurement types, create for each a neural net
- G06N 3/042: Knowledge-based neural networks; Logical representations of neural networks
- G06N 3/048: Activation functions
- G06N 3/049: Temporal neural networks, e.g. delay elements, oscillating neurons or pulsed inputs
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/088: Non-supervised learning, e.g. competitive learning
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00)
- G06N 20/10: using kernel methods, e.g. support vector machines [SVM]
- 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)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- Y02P: CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS

## Abstract

An example monitoring system for data collection includes a data collector including a plurality of sensor. The system includes a data storage to store a collector route template for the plurality of sensors with a sensor collection routine defining how the plurality of sensors. The system includes a data acquisition and analysis circuit to receive detection signals and evaluate the detection values with respect to a rule, and further, based on the evaluation of the detection values with respect to the rule, to modify the sensor collection routine.

## Claims

### Claim 1

A monitoring system for data collection, the system comprising:
a data collector including a plurality of sensors each outputting a respective detection signal;
a data storage structured to store a collector route template for the plurality of sensors, wherein the collector route template comprises a sensor collection routine for defining how the plurality of sensors are coupled to a plurality of input channels;
a data acquisition and analysis circuit structured to receive detection signals via the plurality of input channels, wherein each of the detection signals has a corresponding detection value, and to evaluate the plurality of detection values with respect to a rule,
wherein the data collector is configured to modify the sensor collection routine based on the evaluation of the plurality of detection values with respect to the rule.

### Claim 2 (depends on 1)

The system of claim 1, wherein the system is deployed in part locally on the data collector and in part on an information technology infrastructure component apart and remote from the collector.

### Claim 3 (depends on 1)

The system of claim 1, wherein each of the plurality of sensors is located in an industrial environment and senses a corresponding parameter.

### Claim 4 (depends on 1)

The system of claim 1, wherein the rule is based on an operational state of a machine with respect to which the plurality of sensors provides information.

### Claim 5 (depends on 1)

The system of claim 1, wherein the rule is based on an anticipated state of a machine with respect to which the plurality of sensors provides information.

### Claim 6 (depends on 1)

The system of claim 1, wherein the rule is based on a detected fault condition of a machine with respect to which the plurality of sensors provides information.

### Claim 7 (depends on 1)

The system of claim 1, wherein an evaluation of the plurality of detection values is based on operational mode routing collection schemes.

### Claim 8 (depends on 7)

The system of claim 7, wherein the operational mode is at least one of a normal operational mode, a peak operational mode, an idle operational mode, a maintenance operational mode, and a power savings operational mode.

### Claim 9 (depends on 7)

The system of claim 7, wherein the data collector modifies the sensor collection routine because the data analysis circuit determines a change in operating modes.

### Claim 10 (depends on 9)

The system of claim 9, wherein the change in operating modes comprises a change from an operational mode to an accelerated maintenance mode.

### Claim 11 (depends on 9)

The system of claim 9, wherein the change in operating modes comprises a change from an operational mode to a failure mode analysis mode.

### Claim 12 (depends on 9)

The system of claim 9, wherein the change in operating modes comprises a change from an operational mode to a power-savings mode.

### Claim 13 (depends on 9)

The system of claim 9, wherein the change in operating modes comprises a change from an operational mode to high-performance mode.

### Claim 14 (depends on 1)

The system of claim 1, wherein the data collector modifies the sensor collection routine based on a sensed change in a mode of operation.

### Claim 15 (depends on 14)

The system of claim 14, wherein the sensed change is a failure condition.

### Claim 16 (depends on 14)

The system of claim 14, wherein the sensed change is a performance condition.

### Claim 17 (depends on 14)

The system of claim 14, wherein the sensed change is a power condition.

### Claim 18 (depends on 14)

The system of claim 14, wherein the sensed change is a temperature condition.

### Claim 19 (depends on 14)

The system of claim 14, wherein the sensed change is a vibration condition.

### Claim 20 (depends on 1)

The system of claim 1, wherein evaluating the plurality of detection values with respect to a rule is based on a collection routine with respect to a collection parameter.

### Claim 21 (depends on 20)

The system of claim 20, wherein the parameter is network availability.

### Claim 22 (depends on 20)

The system of claim 20, wherein the parameter is sensor availability.

### Claim 23 (depends on 20)

The system of claim 20, wherein the parameter is a time-based collection routine.

### Claim 24 (depends on 20)

The system of claim 20, wherein the collection routine collects sensor data on a schedule.

### Claim 25 (depends on 20)

The system of claim 20, wherein the collection routing evaluates sensor data over time.