METHODS AND SYSTEMS FOR SAMPLING AND STORING MACHINE SIGNALS FOR ANALYTICS AND MAINTENANCE USING THE INDUSTRIAL INTERNET OF THINGS

• US 20200133256A1
• Filed: 11/15/2019
• Published: 04/30/2020
• Est. Priority Date: 05/07/2018
• 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., Desai, Mehul
• Application Number:
  US16/684,668
• Publication Number:
  US 20200133256A1
• 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

In 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 perform a method of sampling a signal at a streaming sample rate to produce a plurality of samples of the signal. Portions of the plurality of samples can be allocated to first and second signal analysis circuits based on signal analysis sampling rates less than the streaming sample rate, and the samples and the outputs of the signal analysis circuits can be stored. The system can include a sensor detecting a condition of an industrial machine to output a signal, which can be sampled at a streaming sample rate that is at least twice a dominant frequency of the signal.

Claims

1. A method comprising:

sampling a signal at a streaming sample rate, thereby producing a plurality of samples of the signal;

allocating, with a signal routing circuit, a first portion of the plurality of samples of the signal to a first signal analysis circuit, the portion selected based on a first signal analysis sampling rate that is less than the streaming sample rate;

allocating, with a signal routing circuit, a second portion of the plurality of samples of the signal to a second signal analysis circuit, the portion selected based on a second signal analysis sampling rate that is less than the streaming sample rate; and

storing the plurality of samples of the signal, an output of the first signal analysis circuit, and an output of the second signal analysis circuit, wherein the allocated first portion in the stored plurality of samples and the allocated second portion in the stored plurality of samples are tagged with indicia that references the corresponding stored signal analysis output.

2. The method of claim 1, wherein allocating with the signal routing circuit comprises integrating a plurality of samples based on a ratio of the signal analysis sampling rate and the streaming sample rate.

3. The method of claim 1, wherein allocating with the signal routing circuit comprises selecting samples of the signal based on a ratio of the signal analysis sampling rate and the streaming sample rate.

4. The method of claim 1, wherein the streaming sample rate is at least twice as fast as a dominant frequency of the signal.

5. The method of claim 1, wherein a ratio of the signal analysis sampling rate to the streaming sample rate determines a number of supplemental binary bits of data of the output of the first and second signal analysis circuits.

6. The method of claim 5, wherein the number of supplemental binary bits comprises one when the streaming sample rate is at least twice and less than four times the signal analysis sampling rate.

7. The method of claim 5, wherein the number of supplemental binary bits comprise two when the streaming sample rate is at least four times and less than eight times the signal analysis sampling rate.

8. A system comprising:

a sensor detecting a condition of an industrial machine, the sensor producing a signal that varies over time and substantially corresponds with the condition;

an analog to digital converter that receives the signal and samples the signal at a streaming sample rate that is at least twice a dominant frequency of the signal, the sampled signal being output from the analog to digital converter as a sequence of data values; and

at least one digital signal router that receives the sequence of data value and a sub-sampling rate, wherein the sub-sampling rate is lower than the streaming sample rate, and produces at least one sub-sampled output sequence of data comprising select samples from the sequence of samples based on at least one of the sub-sampling rate and a ratio of the streaming sample rate and the sub-sampling rate.

9. The system of claim 8, further comprising a data storage facility that receives the sequence of data values and an analyzed set of data values derived from the sub-sampled output sequence, wherein the analyzed set of data values are stored in association with the sequence of data values such that data values in the sequence of data values that correspond to the sub-sampled output sequence are tagged with indicia that references the corresponding analyzed set of data values.

10. The system of claim 8, wherein producing the at least one sub-sampled output sequence comprises integrating a plurality of samples in the sequence of data values based on a ratio of the sub-sampling rate and the streaming sample rate.

11. The system of claim 8, wherein producing the at least one sub-sampled output sequence comprises selecting samples of the signal based on a ratio of the sub-sampling rate and the streaming sample rate.

12. The system of claim 8, wherein the streaming sample rate is at least twice as fast as a dominant frequency of the signal.

13. The system of claim 8, wherein the ratio of the sub-sampling rate to the streaming sample rate determines a number of supplemental binary bits in the sub-sampled output sequence.

14. The system of claim 13, wherein the number of supplemental binary bits comprises one when the streaming sample rate is at least twice and less than four times the sub-sampling rate.

15. The system of claim 14, wherein the number of supplemental binary bits comprise two when the streaming sample rate is at least four times and less than eight times the sub-sampling rate.