WO2022043976A1 - A system for optimizing power consumption of an industrial facility and a method thereof - Google Patents
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- WO2022043976A1 WO2022043976A1 PCT/IB2021/058546 IB2021058546W WO2022043976A1 WO 2022043976 A1 WO2022043976 A1 WO 2022043976A1 IB 2021058546 W IB2021058546 W IB 2021058546W WO 2022043976 A1 WO2022043976 A1 WO 2022043976A1
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- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000012545 processing Methods 0.000 claims abstract description 32
- 238000005457 optimization Methods 0.000 claims description 15
- 238000004891 communication Methods 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 4
- 238000013526 transfer learning Methods 0.000 claims description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 238000003723 Smelting Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000007727 cost benefit analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000010355 oscillation Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
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- 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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
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- G—PHYSICS
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P80/00—Climate change mitigation technologies for sector-wide applications
- Y02P80/10—Efficient use of energy, e.g. using compressed air or pressurized fluid as energy carrier
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Definitions
- the present disclosure relates to a system for optimizing power consumption of an industrial facility and a method thereof. More specifically it gives us insights into the cost saving by executing one or more industrial energy optimization processes in a specific manner.
- Patent Application US20150195136A1 - Optimizing network parameters based on a learned network performance model discloses a predictive model is constructed by mapping multiple network characteristics to multiple network performance metrics. Then, a network performance metric pertaining to a node in a network is predicted based on the constructed predictive model and one or more network characteristics relevant to the node. Also, a local parameter of the node is optimized based on the predicted network performance metric
- Figure 1 depicts a system (100) for optimizing power consumption of an industrial facility (101);
- Figure 2 illustrates method steps (200) for optimizing power consumption of an industrial facility (101);
- Figure 3 illustrates band pass filtering (Fig.3a) and clustering (Fig.3b) of the power patterns of a machine.
- Figure 1 depicts a system (100) for optimizing power consumption of an industrial facility (101).
- the system for optimizing power consumption of an industrial facility (101), the industrial facility (101) comprises a plurality of machines executing a plurality of processes.
- the system for optimizing power consumption comprises a plurality of energy metering devices (102), a processing module (103) and at least an output interface (104).
- At least one energy metering device (102) is associated with each of the plurality of machines.
- the plurality of energy metering devices (102) are in communication with the processing module (103).
- the plurality of energy metering devices (102) associated with each of the machines transmit electrical domain signature of each machine to the processing module (103).
- the electrical domain signature of each machine comprises the power profile of pattern collected from each of the plurality of machines.
- the processing module (103) comprises a data processing means (1031), Al module (1033) and at least a database (1032).
- the data processing means (1031) is high end processor capable of processing large quantities of data.
- Al module (1033) with reference to this invention can be explained as a component which runs a model.
- a model can be defined as reference or an inference set of data, which is use different forms of correlation matrices.
- This model in reference to this invention refers to an optimization model used to find correlation between the input parameters of the plurality of machines and a desired cost savings in execution of a process by the plurality of machines .
- correlations can be established between different types of data to arrive at some logical understanding of the data.
- the database (1032) comprises a repository of past case studies and trained optimization models. In other words it stores the information on the type of optimization model used corresponding to particular machine and process characteristics.
- the processing module (103) is in communication with the plurality of machines by one or more means of wired and wireless communication known to a person skilled in the art.
- the output interface (104) displays various cost saving predictions as analyzed by the processing module (103).
- each of the building blocks of the system for optimizing power consumption of an industrial facility (101) may be implemented in different architectural frameworks depending on the applications.
- all the building block of the system are implemented in hardware i.e. each building block may be hardcoded onto a microprocessor chip. This is particularly possible when the building blocks are physically distributed over a network, where each building block is on individual computer system across the network.
- the architectural framework of the system are implemented as a combination of hardware and software i.e. some building blocks are hardcoded onto a microprocessor chip while other building block are implemented in a software which may either reside in a microprocessor chip or on the cloud.
- FIG 2 illustrates method steps (200) for optimizing power consumption of an industrial facility (101).
- the industrial facility (101) comprises a plurality of machines executing a plurality of processes along with the system that comprises a plurality of energy metering devices (102), a processing module (103) and at least an output interface (104).
- the processing module (103) receives an electrical domain signature unique to each of the plurality of machines by means of the plurality of energy metering devices (102).
- the electrical domain signature comprises at least power patterns collected from each of the plurality of machines. This is illustrated through the graph in fig. 3a.
- step 202 the processing module (103) optimizes power supplied to each machine.
- This method step further has at least two sub steps.
- step 2021 there is band pass filtering of the electrical domain signature to clean up noise.
- the characteristics of the band pass filter used is gathered from the database (1032) as per an identified machines type (refer step 203). The characteristics include the bandwidth of the band pass filter. It refers to the frequencies allowed to pass and frequencies that are rejected which are outside that range.
- step 2022 there is clustering the electrical domain signature to differentiate between active and idle regions. Clustering algorithms such as K-means are used to differentiate between active and idle regions (as shown in fig. 3b) based on factors like extent of oscillations, power level and time of day and machine Meta model.
- the Meta model consists of nature of the machine (Motor load/thermal load/switching load/lighting load), minimum operating constraints, name plate details and sensed parameters (like energy/power/pressure/temperature).
- the processing module (103) optimizes machine operating parameters for the plurality of machines.
- This step further comprises at least two sub steps.
- the data processing means (1031) in the processing module (103) identifies a machine type based on machine metadata.
- Machine metadata used in machine type classification comprises at least power patter and the machine load type.
- Metadata further includes but is not limited to a load type, rating; based on power pattern and machine meta data , including the machine ID tags into Thermal, motor based, switching, lighting loads and the like.
- sub type classification that deciphers the sub classification of the load from type to sub type in order to accurately process the power pattern.
- step 2032 the Al module (1033) applies transfer learning for the identified machine type based from the database (1032). Once the type and sub type of the machines are identified, next step is to find if there are optimization potentials subject to constraints of the process that are executed by the plurality of machines.
- the constraints includes schedule flexibility, production output (number of parts to produce) and the like.
- the processing module (103) looks up the repository of past case studies in the database (1032), and applies an optimization model through transfer learning from the trained optimization models in the database (1032). For example, if the machine is identified as a compressor, then from repository of past case studies related to compressor like a suitable “pressure set point optimization model” is applied.
- step 203 the predicted cost savings in a process are displayed on the output interface (104).
- the process is executed by a plurality of machines based on optimization of power supplied (i.e. step 202) and optimization of machine operating parameters (i.e. step 203).
- the analysis by the processing module (103) (steps 202,203) are presented on a dashboard of the output interface (104) for visualization, (add example) [0019]
- This idea to develop a system for optimizing power consumption of an industrial facility (101) and a method thereof helps in easy visualization and calculation of the cost benefit analysis.
- the final output is given as a set of key performance indicators that are optimized so that the owner of an industrial facility (101) to achieve its energy efficiency goals, by implementing the metrics as displayed on the output interface (104). It is a top down approach that has a trickledown effect, for example savings of cost and resources at an industrial facility (101) like an Iron smelting plant level percolates below to each of the furnaces and then to the lowest level i.e. to each of the heaters. Similarly, it applies to other industrial processes in facilities.
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Abstract
The present disclosure proposes a system for optimizing power consumption of an industrial facility (101) and a method thereof. The industrial facility (101) comprising a plurality of machines executing a plurality of processes. The system is characterized by a plurality of energy metering devices (102) associated with each of the plurality of machines, a processing module (103) and at least an output interface (104). The processing module (103) comprises a data processing means (1031), Al module (1033) and at least a database (1032). The processing module (103) configured to optimize power supplied to each machine by means of a processing module (103); optimize machine operating parameters for the plurality of machines by means of the processing module (103); predict cost savings in a process.
Description
COMPLETE SPECIFICATION
1. Title of the Invention:
A system for optimizing power consumption of an industrial facility and a method thereof
Complete Specification:
The following specification describes and ascertains the nature of this invention and the manner in which it is to be performed.
Field of the invention
[0001] The present disclosure relates to a system for optimizing power consumption of an industrial facility and a method thereof. More specifically it gives us insights into the cost saving by executing one or more industrial energy optimization processes in a specific manner.
Background of the invention
[0002] The Fourth Industrial Revolution (or Industry 4.0) is transforming and traditional manufacturing and industrial practices, using modern technologies like the internet of things (loT) and artificial intelligence (Al). Such technologies are integrated in modern day industrial facilities for increased automation, improved communication and self-monitoring, and production of smart machines that can analyze and diagnose issues without the need for human intervention. However there is still a major bottleneck in adoption of such digitalization especially in backend support processes like facility/utilities management of an industrial or enterprise site is the visibility of return on investments
(ROI) or cost savings. This often leads to long decision processes and low conversion rates.
[0003] Patent Application US20150195136A1 - Optimizing network parameters based on a learned network performance model discloses a predictive model is constructed by mapping multiple network characteristics to multiple network performance metrics. Then, a network performance metric pertaining to a node in a network is predicted based on the constructed predictive model and one or more network characteristics relevant to the node. Also, a local parameter of the node is optimized based on the predicted network performance metric
Brief description of the accompanying drawings
[0004] An embodiment of the invention is described with reference to the following accompanying drawings:
[0005] Figure 1 depicts a system (100) for optimizing power consumption of an industrial facility (101);
[0006] Figure 2 illustrates method steps (200) for optimizing power consumption of an industrial facility (101); and
[0007] Figure 3 illustrates band pass filtering (Fig.3a) and clustering (Fig.3b) of the power patterns of a machine.
Detailed description of the drawings
[0008] Figure 1 depicts a system (100) for optimizing power consumption of an industrial facility (101). The system for optimizing power consumption of an industrial facility (101), the industrial facility (101) comprises a plurality of machines executing a plurality of processes. The system for optimizing power consumption comprises a plurality of energy metering devices (102), a processing module (103) and at least an output interface (104).
[0009] At least one energy metering device (102) is associated with each of the plurality of machines. The plurality of energy metering devices (102) are in communication with the processing module (103). The plurality of energy metering devices (102) associated with each of the machines transmit electrical domain signature of each machine to the processing module (103). The electrical domain signature of each machine comprises the power profile of pattern collected from each of the plurality of machines.
[0010] The processing module (103) comprises a data processing means (1031), Al module (1033) and at least a database (1032). The data processing means (1031) is high end processor capable of processing large quantities of data. Al module (1033) with reference to this invention can be explained as a component which runs a model. A model can be defined as reference or an inference set of data, which is use different forms of correlation matrices. This model in reference to this invention refers to an optimization model used to find correlation between the input parameters of the plurality of machines and a desired cost savings in execution of a process by the plurality of machines .Using these models and the data from these models, correlations can be established between different types of data to arrive at some logical understanding of the data. The database (1032) comprises a repository of past case studies and trained optimization models. In other words it stores the information on the type of optimization model used corresponding to particular machine and process characteristics.
[0011] The processing module (103) is in communication with the plurality of machines by one or more means of wired and wireless communication known to a person skilled in the art. The output interface (104) displays various cost saving predictions as analyzed by the processing module (103).
[0012] It must be understood that each of the building blocks of the system for optimizing power consumption of an industrial facility (101) may be implemented in different architectural frameworks depending on the applications. In one embodiment of the architectural framework all the building block of the system are implemented in hardware i.e. each building block may be hardcoded onto a microprocessor chip. This is
particularly possible when the building blocks are physically distributed over a network, where each building block is on individual computer system across the network. In another embodiment of the architectural framework of the system are implemented as a combination of hardware and software i.e. some building blocks are hardcoded onto a microprocessor chip while other building block are implemented in a software which may either reside in a microprocessor chip or on the cloud.
[0013] Figure 2 illustrates method steps (200) for optimizing power consumption of an industrial facility (101). A person skilled in the art will appreciate that the system for optimizing power consumption of an industrial facility (101) explained above in the preceding paragraphs is used carry out the method steps. The industrial facility (101) comprises a plurality of machines executing a plurality of processes along with the system that comprises a plurality of energy metering devices (102), a processing module (103) and at least an output interface (104).
[0014] In step 201, the processing module (103) receives an electrical domain signature unique to each of the plurality of machines by means of the plurality of energy metering devices (102). The electrical domain signature comprises at least power patterns collected from each of the plurality of machines. This is illustrated through the graph in fig. 3a.
[0015] In step 202, the processing module (103) optimizes power supplied to each machine. This method step further has at least two sub steps. In step 2021 there is band pass filtering of the electrical domain signature to clean up noise. The characteristics of the band pass filter used is gathered from the database (1032) as per an identified machines type (refer step 203). The characteristics include the bandwidth of the band pass filter. It refers to the frequencies allowed to pass and frequencies that are rejected which are outside that range. In step 2022 there is clustering the electrical domain signature to differentiate between active and idle regions. Clustering algorithms such as K-means are used to differentiate between active and idle regions (as shown in fig. 3b) based on factors like extent of oscillations, power level and time of day and machine Meta model. The Meta
model consists of nature of the machine (Motor load/thermal load/switching load/lighting load), minimum operating constraints, name plate details and sensed parameters (like energy/power/pressure/temperature).
[0016] In step 203, the processing module (103) optimizes machine operating parameters for the plurality of machines. This step further comprises at least two sub steps. In step 2031, the data processing means (1031) in the processing module (103) identifies a machine type based on machine metadata. Machine metadata used in machine type classification comprises at least power patter and the machine load type. Metadata further includes but is not limited to a load type, rating; based on power pattern and machine meta data , including the machine ID tags into Thermal, motor based, switching, lighting loads and the like. Further there is a sub type classification that deciphers the sub classification of the load from type to sub type in order to accurately process the power pattern.
[0017] In step 2032, the Al module (1033) applies transfer learning for the identified machine type based from the database (1032). Once the type and sub type of the machines are identified, next step is to find if there are optimization potentials subject to constraints of the process that are executed by the plurality of machines. The constraints includes schedule flexibility, production output (number of parts to produce) and the like. Based on these constraints the processing module (103) looks up the repository of past case studies in the database (1032), and applies an optimization model through transfer learning from the trained optimization models in the database (1032). For example, if the machine is identified as a compressor, then from repository of past case studies related to compressor like a suitable “pressure set point optimization model” is applied.
[0018] In step 203, the predicted cost savings in a process are displayed on the output interface (104). The process is executed by a plurality of machines based on optimization of power supplied (i.e. step 202) and optimization of machine operating parameters (i.e. step 203). The analysis by the processing module (103) (steps 202,203) are presented on a dashboard of the output interface (104) for visualization, (add example)
[0019] This idea to develop a system for optimizing power consumption of an industrial facility (101) and a method thereof helps in easy visualization and calculation of the cost benefit analysis. A person skilled in the art will appreciate that the final output is given as a set of key performance indicators that are optimized so that the owner of an industrial facility (101) to achieve its energy efficiency goals, by implementing the metrics as displayed on the output interface (104). It is a top down approach that has a trickledown effect, for example savings of cost and resources at an industrial facility (101) like an Iron smelting plant level percolates below to each of the furnaces and then to the lowest level i.e. to each of the heaters. Similarly, it applies to other industrial processes in facilities.
[0020] It must be understood that the embodiments explained in the above detailed description are only illustrative and do not limit the scope of this invention. Any modification a system (100) for optimizing power consumption of an industrial facility (101) and a method (200) thereof are envisaged and form a part of this invention. The scope of this invention is limited only by the claims.
Claims
1. A system for optimizing power consumption of an industrial facility (101), the industrial facility (101) comprising a plurality of machines executing a plurality of processes, characterized in that system: a plurality of energy metering devices (102) associated with each of the plurality of machines, the plurality of energy metering devices (102) in communication with a processing module (103); the processing module (103) comprising a data processing means (1031), Al module (1033) and at least a database (1032), the processing module (103) in communication with the plurality of machines, the processing module (103) configured to: optimize power supplied to each machine; optimize machine operating parameters for the plurality of machines; predict cost savings in a process; and at least a output interface (104) in communication with the processing module (103), the output interface (104) displaying said predictions.
2. The system for optimizing power consumption of an industrial facility (101) as claimed in claim 1, wherein the plurality of energy metering devices (102) associated with each of the machines transmit electrical domain signature of each machine to the processing module (103).
3. The system for of optimizing power consumption of an industrial facility (101) as claimed in claim 1, wherein the database (1032) comprises a repository of past case studies and optimization models.
8 The system for optimizing power consumption of an industrial facility (101) as claimed in claim 1, wherein optimizing power supplied to each machine further comprises: band pass filtering of the electrical domain signature to clean up noise; and clustering the electrical domain signature to differentiate between active and idle regions. The system for optimizing power consumption of an industrial facility (101) as claimed in claim 1, wherein optimizing machine operating parameters for the plurality of machines further comprises: identifying a machine type based on machine metadata; applying transfer learning for the identified machine type from a database (1032). A method of optimizing power consumption of an industrial facility (101), the industrial facility (101) comprising a plurality of machines executing a plurality of processes, the method comprising: receiving an electrical domain signature unique to each of the plurality of machines by means of a plurality of energy metering devices (102), wherein the electrical domain signature comprises at least power patterns collected from machines, characterized in that method: optimizing power supplied to each machine by means of a processing module (103), said optimization further comprises: band pass filtering of the electrical domain signature to clean up noise; and clustering the electrical domain signature to differentiate between active and idle regions; optimizing machine operating parameters for the plurality of machines by means of the processing module (103), said optimization further comprises: identifying a machine type based on machine metadata; applying transfer learning for the identified machine type from a database (1032); predicting cost savings in a process, the process executed by a plurality of machines based on optimization of power supplied and optimization of machine operating parameters, .
The method for optimizing power consumption of an industrial facility (101) as claimed in claim 6, wherein the characteristics of the band pass filter used is gathered from the database (1032). The method for optimizing power consumption of an industrial facility (101) as claimed in claim 6, wherein machine metadata comprises power pattern and at least machine load type.
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Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150195136A1 (en) | 2014-01-06 | 2015-07-09 | Cisco Technology, Inc. | Optimizing network parameters based on a learned network performance model |
US20190265971A1 (en) * | 2015-01-23 | 2019-08-29 | C3 Iot, Inc. | Systems and Methods for IoT Data Processing and Enterprise Applications |
US20200019155A1 (en) * | 2016-05-09 | 2020-01-16 | Strong Force Iot Portfolio 2016, Llc | Systems and methods for balancing remote motors |
US20200103894A1 (en) * | 2018-05-07 | 2020-04-02 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for computerized maintenance management system using the industrial internet of things |
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- 2021-09-20 JP JP2023513646A patent/JP2024513621A/en active Pending
- 2021-09-20 WO PCT/IB2021/058546 patent/WO2022043976A1/en active Application Filing
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150195136A1 (en) | 2014-01-06 | 2015-07-09 | Cisco Technology, Inc. | Optimizing network parameters based on a learned network performance model |
US20190265971A1 (en) * | 2015-01-23 | 2019-08-29 | C3 Iot, Inc. | Systems and Methods for IoT Data Processing and Enterprise Applications |
US20200019155A1 (en) * | 2016-05-09 | 2020-01-16 | Strong Force Iot Portfolio 2016, Llc | Systems and methods for balancing remote motors |
US20200103894A1 (en) * | 2018-05-07 | 2020-04-02 | Strong Force Iot Portfolio 2016, Llc | Methods and systems for data collection, learning, and streaming of machine signals for computerized maintenance management system using the industrial internet of things |
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