Patent Application
20160004794
It can be difficult for an owner of an industrial plant to determine whether the benefits of a contemplated change will, over time, offset the cost of the change. For example, the owner of a power plant that produces electricity may be unsure the cost of a new turbine would result in a sufficient increase in power output to justify that cost. Note that many different factors, such as the type of power plant, the power plant's location, and/or the age of the power plant, may have an impact on such decisions. Moreover, a power plant owner, or a person advising the power plant owner, might only have incomplete information about the operation of the power plant, making such determinations an even more time consuming and error prone task.
It would therefore be desirable to provide systems and methods to supplement incomplete industrial plant information in an automatic and accurate manner.
In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of embodiments. However, it will be understood by those of ordinary skill in the art that the embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components and circuits have not been described in detail so as not to obscure the embodiments.
As used herein, devices, including those associated with the system 100 and any other device described herein, may exchange information via any communication network which may be one or more of a Local Area Network (LAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a proprietary network, a Public Switched Telephone Network (PSTN), a Wireless Application Protocol (WAP) network, a Bluetooth network, a wireless LAN network, and/or an Internet Protocol (IP) network such as the Internet, an intranet, or an extranet. Note that any devices described herein may communicate via one or more such communication networks.
The model building platform 110 may receive various types of information, such as a power plant name, plant location, plant age, etc., from the historic industrial plant parameters database 120. The historic industrial plant parameters database 120 may be locally stored or reside remote from the model building platform 110. Although a single model building platform and model execution platform 150 are shown in
The system 100 may generate supplemented industrial plant data based on received incomplete industrial plant information in an automatic and accurate manner in accordance with any of the embodiments described herein. For example,
At S210, a model building platform may receive a set of historic industrial plant parameters associated with operation of a plurality of industrial plants over a period of time. As used herein, the phrase “industrial plant” may refer to, for example, a power plant that produces electricity. The set of historic industrial plant parameters might be associated with, for example, economic information (e.g., revenue, costs, or profit), regulatory information, configuration information, and/or operational information.
At S220, the model building platform may automatically create a generative model (e.g., a stochastic generative model) based on relationships detected within the set of historic industrial plant parameters. According to some embodiments, prior to the automatic creation of the generative model, the historic industrial plat parameters may be pre-processed to create normalized data. Note that the automatic creation of the generative model may be associated with a machine deep learning process and a validation test set.
At S230, a model execution platform may receive incomplete industrial plant information associated with a particular industrial plant. At S240, supplemented industrial plant data may be automatically generated based on the received incomplete industrial plant information and the generative model. According to some embodiments, prior to the automatic generation of the supplemented industrial plant information, the historic industrial plant parameters may be pre-processed to create normalized data. Note that the model execution platform may use a Gibbs sampling Markov Chain Monte Carlo (“MCMC”) algorithm to obtain an observation approximated from a specified multivariate probability distribution. According to some embodiments, the incomplete industrial plant information and the supplemented industrial plant data comprise complete industrial plant operational information. Moreover, the supplemented industrial plant data may include likelihood information (e.g., associated with the likely accuracy of the supplemented data). According to some embodiments, an indication of the supplemented industrial plant data may then be output (e.g., to a display, printed report, or web page).
The embodiments described herein may be implemented using any number of different hardware configurations. For example,
The processor 310 also communicates with a storage device 330. The storage device 330 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 330 stores a program 312 and/or a model building engine 314 for controlling the processor 310. The processor 310 performs instructions of the programs 312, 314, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 310 may access historic industrial plant database 400 storing information about a number of different industrial plants over a period of time. The processor 310 may then automatically create a generative model based on relationships detected within the set of historic industrial plant parameters. The processor 310 may, for example, store generative model parameters, such as weighing factors, in a generative model parameters database 500.
The programs 312, 314 may be stored in a compressed, uncompiled and/or encrypted format. The programs 312, 314 may furthermore include other program elements, such as an operating system, clipboard application a database management system, and/or device drivers used by the processor 310 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the model building platform 300 from another device; or (ii) a software application or module within the model building platform 300 from another software application, module, or any other source.
In some embodiments (such as shown in
Referring to
The industrial plant identifier 402 may be, for example, unique alphanumeric codes identifying industrial plants, such as power plants that produce electricity. Note that a single plant might be associated with multiple entries (e.g., representing an output of a power plant over different periods of time). The location 404 might indicate a country or state where the power plant is location, and the type 406 might indicate how each plant produces electricity (e.g., via gas or coal). The age 408 may indicate how long the plant has been in operation, and configuration information 410 might be associated with any operational characteristic of the power plant (e.g., a number of turbines, a maintenance schedule, a pre-stored configuration profile, etc.). Note that some of the information in the historic industrial plant database 400 may be missing (e.g., the age 408 of the power plant “P_104” is unknown and therefore blank).
Note that the information in the example of
Referring to
The generative model identifier 502 may be, for example, unique alphanumeric codes identifying a generative model and the parameters 504, 506, 508 may be associated with weighing values, rules, and/or any other information that may be used to define the model. As used herein, the phrase “generative model” may refer to a model for randomly generating observable data, such as when fed some parameters. It may specify a joint probability distribution over observation and/or label sequences. The generative model may be associated with machine learning, modeling data directly (such as when modeling observations drawn from a probability density function), and/or as an intermediate step to forming a conditional probability density function. A conditional distribution might be formed for example, from a generative model through Bayes' rule.
The information in the generative model parameters database 500 may then be used by a model execution platform. For example,
The processor 610 also communicates with a storage device 630. The storage device 630 may comprise any appropriate information storage device, including combinations of magnetic storage devices (e.g., a hard disk drive), optical storage devices, mobile telephones, and/or semiconductor memory devices. The storage device 630 stores a program 612 and/or a model execution engine 614 for controlling the processor 610. The processor 610 performs instructions of the programs 612, 614, and thereby operates in accordance with any of the embodiments described herein. For example, the processor 610 may access an incomplete information database 700 storing information about one or more industrial plants. The processor 610 may then execute a generative model and store supplemented data in a supplemented data database 800.
The programs 612, 614 may be stored in a compressed, uncompiled and/or encrypted format. The programs 612, 614 may furthermore include other program elements, such as an operating system, clipboard application a database management system, and/or device drivers used by the processor 610 to interface with peripheral devices.
As used herein, information may be “received” by or “transmitted” to, for example: (i) the model execution platform 600 from another device; or (ii) a software application or module within the model execution platform 600 from another software application, module, or any other source.
In some embodiments (such as shown in
Referring to
The industrial plant identifier 702 may be, for example, unique alphanumeric codes identifying industrial plants, such as power plants that produce electricity. The location 704 might indicate a country or state where the power plant is location, and the type 706 might indicate how each plant produces electricity (e.g., via gas or coal). The age 708 may indicate how long the plant has been in operation, and configuration information 710 might be associated with any operational characteristic of the power plant (e.g., a number of turbines, a maintenance schedule, a pre-stored configuration profile, etc.). Note that the information in the database is “incomplete” (e.g., the configuration information 710 for “P_123” and the type 704 for “P_356” are unknown and therefore blank).
Referring to
Note that the information in the database 800 is “supplemented” such that the blank values from the incomplete information database have been filled in. For example, the configuration information 810 for “P_123” and the type 804 for “P_356” have been determined by the generative model and stored into the database 800.
Such a system 900, including algorithms and enabling software, may estimate the power plant operational characteristics given the incomplete plant information. The system 900 may be “big data” based and may be a part of an industrial internet initiative. The system 900 may use a large number of power plant examples and deep learning techniques to extract and describe the relationships between various power plant economic, regulatory, configuration, and/or operational information. These relationships may be captured in a stochastic generative model which may be used to impute the missing information about a particular plant.
The complete information from the system 900 may be used to assess configuration and/or modification needs for a power plant and ultimately improve the plant's performance given the economic environment in which it operates. According to some embodiments, the system 900 may be extended to other applications where missing data impedes operations, such as automated maintenance record correction and completion and/or user preference inference.
According to some embodiments, the system 900 includes two process steps. The first step is training a deep learning module. That may include accessing a database containing historical data used to train the model. Note that the information in the database might not be complete, that is, the system 900 may learn from incomplete plant economic, configuration, and/or operational information. According to some embodiments, continuous, numeric categorical, and string categorical data is pre-processed and normalize to facilitate processing. A deep learning algorithm may process the historical data and capture the relationships observed in the data. Once the model is generated, it may be validated using a validation test set.
The second process step associated with the system 900 is model execution. That may include, given a new incomplete record, a generative model may be sampled using Gibbs sampling. The output may provide the maximum likelihood, along with other statistical parameters such as the standard deviation. This may let a user assess the output's precision. According to some embodiments, the system 900 may also be used to compare the current performance of a power plant to an average of power plants with the same or similar configurations and/or economic environments. This may help a user assess if the plant should be operated or configured differently. Note that, according to some embodiments, the system 900 may be used to study the effect of individual customization and/or modifications on a specific plant such that a quantifiable benefit may be derived and used to recommend highly effective changes.
According to some embodiments, the system 900 may help a user obtain a complete picture of operational needs to help a power plant operate optimally given the plant configuration and economic environment. Moreover, the system 900 may provide an ability to gain insight into user behavior and identify value-drivers using deep learning techniques. In addition, the system 900 may provide statistical information that can be used to guide future data collection efforts. For example, the system 900 may be associated with a means to identify observations for which eliminating uncertainty may provide a measurable benefit in terms of assessing plan operations.
The following illustrates various additional embodiments of the invention. These do not constitute a definition of all possible embodiments, and those skilled in the art will understand that the present invention is applicable to many other embodiments. Further, although the following embodiments are briefly described for clarity, those skilled in the art will understand how to make any changes, if necessary, to the above-described apparatus and methods to accommodate these and other embodiments and applications.
Although specific hardware and data configurations have been described herein, note that any number of other configurations may be provided in accordance with embodiments of the present invention (e.g., some of the information associated with the databases described herein may be combined or stored in external systems).
The present invention has been described in terms of several embodiments solely for the purpose of illustration. Persons skilled in the art will recognize from this description that the invention is not limited to the embodiments described, but may be practiced with modifications and alterations limited only by the spirit and scope of the appended claims.
