METHODS AND SYSTEMS FOR DATA COLLECTION, LEARNING, AND STREAMING OF MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS

• US 20200133255A1
• Filed: 11/15/2019
• Published: 04/30/2020
• Est. Priority Date: 05/07/2018
• Status: Active Application

• 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., Desai, Mehul
• Application Number:
  US16/684,651
• Publication Number:
  US 20200133255A1
• Time in Patent Office: Days
• CPC Class Codes: G01M 13/028 Acoustic or vibration analysis
• CPC Class Codes: G01M 13/045 Acoustic or vibration analysis
• CPC Class Codes: G05B 13/028 using expert systems only
• CPC Class Codes: G05B 19/406 characterised by monitoring or safety G05B19/19 takes precedence
• CPC Class Codes: G05B 19/4183 characterised by data acquisition, e.g. workpiece identification
• CPC Class Codes: G05B 19/4184 characterised by fault tolerance, reliability of production system
• CPC Class Codes: G05B 19/41845 characterised by system universality, reconfigurability, modularity
• CPC Class Codes: G05B 19/4185 characterised by the network communication
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: G05B 19/41875 characterised by quality surveillance of production
• CPC Class Codes: G05B 2219/32014 Augmented reality assists operator in maintenance, repair, programming, assembly, use of head mounted display with 2-D 3-D display and voice feedback, voice and gesture command
• CPC Class Codes: G05B 2219/32287 Medical, chemical, biological laboratory
• CPC Class Codes: G05B 2219/35001 Data input, data handling, programming, monitoring of nc
• CPC Class Codes: G05B 2219/37337 Noise, acoustic emission, sound
• CPC Class Codes: G05B 2219/37351 Detect vibration, ultrasound
• CPC Class Codes: G05B 2219/37434 Measuring vibration of machine or workpiece or tool
• CPC Class Codes: G05B 2219/40115 Translate goal to task program, use of expert system
• CPC Class Codes: G05B 2219/45004 Mining
• CPC Class Codes: G05B 2219/45129 Boring, drilling
• 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 23/0229 : knowledge based, e.g. expert systems; genetic algorithms
• CPC Class Codes: 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
• CPC Class Codes: G05B 23/0264 : Control of logging system, e.g. decision on which data to store; time-stamping measurements
• 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: G05B 23/0286 : Modifications to the monitored process, e.g. stopping operation or adapting control
• CPC Class Codes: G05B 23/0289 : Reconfiguration to prevent failure, e.g. usually as a reaction to incipient failure detection
• CPC Class Codes: G05B 23/0291 : Switching into safety or degraded mode, e.g. protection and supervision after failure
• CPC Class Codes: G05B 23/0294 : Optimizing process, e.g. process efficiency, product quality
• CPC Class Codes: G05B 23/0297 : Reconfiguration of monitoring system, e.g. use of virtual sensors; change monitoring method as a response to monitoring results
• CPC Class Codes: G06F 16/2379 : Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/24 : Querying
• CPC Class Codes: G06F 16/245 : Query processing
• CPC Class Codes: G06F 18/2178 : based on feedback of a supervisor
• CPC Class Codes: G06N 20/00 : Machine learning
• CPC Class Codes: G06N 3/006 : based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
• CPC Class Codes: G06N 3/02 : Neural networks
• CPC Class Codes: G06N 3/044 : Recurrent networks, e.g. Hopfield networks
• CPC Class Codes: G06N 3/045 : Combinations of networks
• CPC Class Codes: G06N 3/047 : Probabilistic or stochastic networks
• CPC Class Codes: G06N 3/08 : Learning methods
• CPC Class Codes: G06N 3/084 : Backpropagation, e.g. using gradient descent
• CPC Class Codes: G06N 3/088 : Non-supervised learning, e.g. competitive learning
• CPC Class Codes: G06N 3/126 : Evolutionary algorithms, e.g. genetic algorithms or genetic programming
• CPC Class Codes: G06N 5/046 : Forward inferencing; Production systems
• CPC Class Codes: G06N 7/01 : Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/20 : Administration of product repair or maintenance
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/20084 : Artificial neural networks [ANN]
• CPC Class Codes: G06T 2207/30164 : Workpiece; Machine component
• CPC Class Codes: G06T 2207/30168 : Image quality inspection
• CPC Class Codes: G06T 7/0004 : Industrial image inspection
• CPC Class Codes: H04B 17/309 : Measuring or estimating channel quality parameters
• CPC Class Codes: H04B 17/318 : Received signal strength
• CPC Class Codes: H04L 1/0002 : by adapting the transmission rate
• CPC Class Codes: H04L 1/0041 : Arrangements at the transmitter end
• CPC Class Codes: H04L 1/18 : Automatic repetition systems, e.g. Van Duuren systems
• CPC Class Codes: H04L 5/0064 : Rate requirement of the data, e.g. scalable bandwidth, data priority
• CPC Class Codes: 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]
• 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: 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]
• CPC Class Codes: Y02P 90/80 : Management or planning
• CPC Class Codes: Y04S 40/18 : Network protocols supporting networked applications, e.g. including control of end-device applications over a network

Abstract

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may predict a service event from vibration data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine signal a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.

Claims

1. A system for predicting a service event from vibration data, comprising:

an industrial machine comprising at least one vibration sensor disposed to capture vibration of a portion of the industrial machine;

a vibration analysis circuit in communication with the at least one vibration sensor and that generates at least one of a frequency, peak amplitude, and gravitational force of the captured vibration;

a multi-segment vibration frequency spectra structure that facilitates mapping the captured vibration to one vibration frequency segment of a multi-segment vibration frequency;

a severity unit algorithm that receives the frequency of the captured vibration and the corresponding vibration frequency segment and produces a severity value which is then mapped to one of a plurality of severity units defined for the corresponding vibration frequency segment; and

a signal generating circuit that receives the one of the plurality of severity units, and based thereon, signals a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.

2. The system of claim 1, wherein the multi-segment vibration frequency spectra structure facilitates a mapping of the detected vibrations to a first severity unit when the frequency of the captured vibration corresponds to a below a low-end knee threshold-range of the multi-segment vibration frequency spectra.

3. The system of claim 1, wherein the multi-segment vibration frequency spectra structure facilitates a mapping of the detected vibrations to a second severity unit when the frequency of the captured vibration corresponds to a mid-range of the multi-segment vibration frequency spectra.

4. The system of claim 1, wherein the multi-segment vibration frequency spectra structure facilitates a mapping of the detected vibrations to a third severity unit when the frequency of the captured vibration corresponds to an above a high-end knee threshold-range of the multi-segment vibration frequency spectra.

5. The system of claim 1, wherein the severity units indicate that the detected vibrations may lead to a failure of at least the portion of the industrial machine.

6. The system of claim 1, wherein a first segment of the multi-segment vibration frequency spectra is divided into a plurality of severity units based on the amplitude of the captured vibration.

7. The system of claim 1, wherein a second segment of the multi-segment vibration frequency spectra is divided into a plurality of severity units based on the gravitational force of the captured vibration.

8. The system of claim 1, wherein the severity unit is determined based on a peak displacement of the amplitude of the captured vibration for determined vibration frequencies within a first segment of the multi-segment vibration frequency spectra.

9. The system of claim 1, wherein the severity unit is determined based on gravitational force of the captured vibration for determined vibration frequencies within a second segment of the multi-segment vibration frequency spectra.

10. The system of claim 1, wherein the portion of the industrial machine is a moving part.

11. The system of claim 1, wherein the portion of the industrial machine is a structural member supporting a moving part.

12. The system of claim 1, wherein the portion of the industrial machine is a motor.

13. The system of claim 1, wherein the portion of the industrial machine is a drive shaft.