Patent ID: 12198081

DETAILED DESCRIPTION′

This disclosure relates to a work request processor (e.g., a tool) operating on a computing platform that can provide a work order (e.g., a task or a job at a scheduled time) for a maintenance of equipment at a power generation system such as a nuclear reactor, or a nuclear power plant, more generally. The work request processor can provide a work order that is more effective in contrast to manual techniques that rely on human interaction and processing. Thus, the work request processor can improve an accuracy of maintenance tasks.

A work request interface operates on the computing platform and communicates with the work request processor. The work request interface receives work request data that includes work requests. The work requests identify the equipment (e.g., make, module, type, etc.), work description (e.g., description of a problem with the equipment and the operations needed to fix the equipment), a system in which the equipment is located, and other relevant information (e.g., work order title, workers name, work location, etc.) The work request interface provides the work request data (including the work requests) to a work request processing pipeline configured to standardize the data, process text (e.g., using natural language processing (NLP) techniques), and encode equipment information and categorical information. The work request processing pipeline can determine operations for a corresponding work order that needs to be performed on the equipment identified in the corresponding work request. The work request processing pipeline can provide a standardized work request for a given work order to a work order generator executing on the computing platform.

The work order generator includes trained machine learning models for processing relevant data from the pipelined work request data for the generation of a set of work orders for the equipment. Data outputted by the trained machine learning modules (as described below) can be provided to a work order data aggregator that can be programmed to combine relevant outputted machine learning data to provide the work order for the equipment. The work order generator includes an outage module that can predict whether equipment identified in a given work request will need to be completed during a plant outage in order to execute the operations needed to fix the equipment. To make this prediction for the work order, the work order generator provides the outage model with the standardized work request that includes a description of the work, the location of work, equipment information, the system in which the equipment operates, and several aggregations of a number of equipment types or groups that have had outage work done previously. The outage model can output outage data to indicate whether the equipment for the given work request can be completed during the plant outage.

The work order generator also includes a priority model that is another machine learning generated model. The priority model predicts the priority of the work request based on an array of categorical variables, for example, the system code, equipment class, functional importance determination and single point vulnerability. The priority module can be programmed to provide priority data indicating a priority of the work further based on the work order request.

The work order generator further includes a unit condition model that can determine a reactor stage (alternatively referred to as a reactor phase) for a reactor of the power generation system (e.g. a nuclear power plant) for maintenance of the equipment identified in the given work request. The unit condition model can output unit condition data identifying the reactor stage based on the outage data and the priority data, as well as a system operability status. The work order generator further includes a nuclear applicable mode module that uses several categorical properties of work order tasks. The nuclear applicable mode module can determine a nuclear applicability of the work request based on the priority data, the unit condition data and the outage data. The nuclear applicable mode module can provide nuclear applicability data characterizing the nuclear applicability of the work request.

The work order generator module further includes a package type model that can predict a complexity and/or risk for implementing maintenance of the equipment. The package type model can generate instructions commensurate with the complexity and the package type model can output package data. The work order generator further includes a discipline module that can predict one or more disciplines (e.g., electrical department, chemical department, etc.) for implementing the maintenance of the equipment characterized in the work order request. Such disciplines reflect a nature and/or type of work needed for the work order request, such that a technician with the correct skills (e.g., electrical skills, knowledge of chemistry, mechanical skills, etc.) will be assigned a corresponding work order. The discipline module can provide discipline data identifying each discipline for equipment maintenance based on the work order request and the craft skills needed to perform the work.

Based on the determinations of the outage model, the priority model, the nuclear applicable model, the discipline model, the unit condition model and the package type model, the work order generator can generate a set of work orders for work requests in the work request data, including a work request for the given work order. The set of work orders can include one or more work orders that specify operations to provide maintenance to the equipment identified in the given work order. For instance, suppose that the set of work orders were related to fixing a leaking valve, wherein the valve was unreachable. In this case, the set of work orders can include, for example, operations related to setting up scaffolding, operating on the identified equipment and removing the scaffolding. Accordingly, in this example, there might be three different work orders, that may be completed by different operators (or teams of operators) in a specific sequence.

FIG.1illustrates a system100for generating work orders for a nuclear power generation system104. The nuclear power generation system104includes equipment106for generating electrical power and for providing the electrical power to external consumers. The nuclear power generation system104includes a nuclear reactor108(e.g., an atomic pile) that generates steam that is provided to a steam turbine112. The steam from the nuclear reactor108causes the steam turbines to generate electricity that is provided to a power distribution interface116. The power distribution interface116can be representative of electrical equipment (e.g., transformers and/or electrical towers) that are employed to transmit electricity to external systems, such as end-user premises. The equipment106of the nuclear power generation system104can be integrated with one or more of the nuclear reactor108, the steam turbine112or the power distribution interface116(or integrated with another part of the nuclear power generation system104). The equipment106represents, for example, valves, gears, motors, nuclear fuel, etc.

The equipment106of the nuclear power generation system104includes actuators120for controlling operations of the equipment of the nuclear power generation system104. The equipment106of the nuclear power generation system104also includes sensors124for monitoring operations of the equipment of the nuclear power generation system104. As some examples, the actuators120can be implemented as motors, solenoids, etc. that can change state to modify a condition of the nuclear power generation system104. For instance, if a given actuator120is a solenoid coupled to a valve, actuation of the solenoid could, for example, cause the valve to open or close. The sensors124can be representative of any equipment that reports a state of another instance of the equipment106. As some examples, the sensor124can be temperature sensors, pressure sensors, photo-eyes, cameras, etc.

The nuclear power generation system104has operational cycles and operational states. For example, the nuclear reactor108can be online, offline (e.g., shutdown) as well as in one of a plurality of nuclear reactor stages (phases of operation). In some examples, there are six (6) or more stages of the nuclear reactor108that corresponds to a temperature and pressure of the nuclear reactor108. For example, in a first stage, the nuclear reactor108is fully operational. In a second stage, the nuclear reactor108operates in a lower energy state and a lower pressure than the first stage. In a third stage, the nuclear reactor108operates in a lower energy state and a lower pressure than the second stage. This trend of lowering energy and pressure continues to the sixth stage (e.g., which has a lower energy and pressure than a fifth stage), which is presumed to be a minimal power and minimal pressure needed to operate the nuclear reactor108. For an energy state and pressure lower than the sixth stage, it is presumed that the nuclear reactor108is offline.

Operators and/or maintenance crews of the nuclear power generation system104periodically and/or asynchronously inspect the nuclear power generation system104. More particularly, the operators and/or maintenance crew inspect the equipment106of the nuclear power generation system104to ensure proper operation. For instance, the operators and/or maintenance crew inspect the sensors124and the actuators120to ensure that the items are operating correctly. Additionally, the operators and/or maintenance crews inspect the nuclear reactor108, the steam turbine112and the power distribution interface116to ensure that these systems are operating correctly.

The operators and/or maintenance crews employ an end-user device128to generate work requests132. The end-user device128can be, for example, computing devices, such as portable computing devices (e.g., tablet computers, smart phones, laptop computers, etc.). Alternatively, the end-user device128can be desktop computers. Although only a single end-user device128is illustrated in the present example, in other examples, there are multiple end-user devices. The end-user device128communicates on a network136. The network136can be implemented, for example, as a public network (e.g., the Internet) a private network (e.g., a utility network) or a combination thereof.

The work requests132are generated in response to user input. In some examples, the work requests132are generated by filling digital forms output by the end-user device128. In some examples, the digital forms are provided on a web page accessed by the end-user device128. In other examples, the digital forms are provided on application software executing on the end-user device128. In any such situation, the user input provided to generate the work requests132describes equipment106of the nuclear power generation system104that may need service based on observations of the user of the end-user device128(e.g., an operator and/or maintenance crew member of the nuclear power generation system104).

Each work request132includes a title, a unique identifier for one or more instances of equipment, a description of the issue observed and a recommended action. More generally, in various examples, each work request132can include data identifying the equipment (e.g., make, module, type, etc.), work description (e.g., description of a problem with the equipment and the operations needed to fix the equipment), a system in which the equipment is located, and other relevant information (e.g., work order title, workers name, work location, etc.) In general, the recommended action describes changing a state of the corresponding equipment from a first state (e.g., damaged) to a second state (e.g., functioning properly). Additionally, in some examples each work request132can be associated with one or more images that attempt to illustrate the issue corresponding to the work request132.

As a first example (hereinafter, “the first example”), suppose that a work request132identifies a specific valve based on a readout of a corresponding sensor124(e.g., a pressure sensor). Thus, in the first example, suppose that the pressure sensor indicated that the particular valve is leaking. In the first example, the work request132could have a title of “leaking pressure valve”. The work request132in the first example generated can provide a unique identifier for the pressure sensor (e.g., an index number, such as “pressure valve 195-12-1”). The work requests132in the first example further includes a description of the observed issue, which could be “Pressure sensor for valve indicates a loss of pressure of about 4 pascal (Pa).” In the first example, the recommended action for the work request132could be “Replace valve”.

As a second example, (hereinafter, “the second example”), suppose that a hairline crevice is identified in a cover of the steam turbine112. In the second example, suppose that the hairline crevice is identified with direct visual inspection. In the second example, the associated work request132could be assigned a title of, “Indication in Steam Turbine”. Additionally, in the second example, the associated work request could uniquely identify the particular steam turbine112and the region of the hairline crevice. Thus, in the second example, the work request132could have an equipment identifier of “outer cover of steam turbine2”. In the second example, the work request could have a description of “Indication of about 0.5 meters extending in a vertical direction on left side of the steam turbine”. Further, in the second example, the recommended action could be, “repair indication”.

The system100includes a server140that communicates on the network136. The server140includes a non-transitory memory144for storing data and machine-readable instructions. The non-transitory memory144can be implemented as a non-transitory machine readable medium, such as volatile memory (e.g., random access memory), nonvolatile memory (e.g., a hard disk drive, a solid state drive, flash memory, etc.) or a combination thereof. The server140also includes a processing unit148for accessing the memory144and executing the machine-readable instructions. The server140can communicate with the network136through a network interface152, such as a network interface card or other device.

The server140provides a computing platform. In some examples, the server140could be implemented in a computing cloud. In such a situation, features of the server140, such as the processing unit148, the network interface152, and the memory144could be representative of a single instance of hardware or multiple instances of hardware with applications executing across the multiple of instances (i.e., distributed) of hardware (e.g., computers, routers, memory, processors, or a combination thereof). Alternatively, the server140could be implemented on a single dedicated server.

The memory144includes a work order processor156that is programmed/configured to process the work requests132and to generate work orders corresponding to the work requests132(or some subset thereof). More specifically, the work order processor156includes a work request interface160that receives the work requests132from the end-user devices128embedded in work request data. The work request interface160provides work request data characterizing the work requests132to a work request processing pipeline164, a module of the work order processor156.

In response to the work request data, the work request processing pipeline164is programmed/configured to standardize the work request data, process text of the work requests132embedded in the work request data (e.g., using natural language processing (NLP) techniques), and encode equipment information and categorical information. For example, in some situations, pictures of the equipment106identified in a corresponding work order can be added by the work request processing pipeline164.

Additionally, the work request processing pipeline164identifies operations needed to change the equipment106of a corresponding work request132from the first state (e.g., a state needing maintenance) to the second state (e.g., a fully operational state). In some situations, the work request processing pipeline164adds operations for a work order to form a sequence of operations that are needed to change the equipment106from the first state to the second state. The work request processing pipeline164determines the operations needed for each given work request132using a machine learning model (e.g., generated with random forest techniques) that employs similarity between the current work request132and previously generated work orders to produce a list of operations for a work order that likely need to be completed for the work request132. The machine learning module can employ collaborative filtering techniques to analyze the tasks in the previously generated work orders. In some examples, the work request processing pipeline164updates fields of the work requests132included in the work order data with data from external sources. For instance, in examples where the equipment106is inaccessible to a ground level, the work request processing pipeline164can add operations related to adding and/or removing scaffolding to access corresponding equipment. Accordingly, the work request processing pipeline164can add tasks for the work order, such as the building and removing of scaffolding even when the description or recommended action of the work request132does not mention scaffolding, the model for the work request processing pipeline would take features of the work request such as the equipment, system, location, and others and compare those to previous work orders, some of which would include a scaffolding task.

The work request processing pipeline164can provide a standardized work request and the tasks for the work order for each received work request132to a work order generator168of the work order processor156. The work order generator168includes K number of trained machine learning models172for processing relevant data from the standardized work requests provided from the work request processing pipeline164for the generation of a set of work orders for the equipment, where K is an integer greater than or equal to one. Data outputted by the K number of trained machine learning models172can be provided to a work order data aggregator176module of the work order processor156that can be programmed to combine relevant outputted machine learning data to provide a work order for the equipment106of the nuclear power generation system104. In some examples, the K number of trained machine learning models172are generated with random forest machine learning techniques. In other examples, other types of machine learning algorithms are employable to generate the K number of trained machine learning models172or some subset thereof.

FIG.2illustrates an example of a work order generator200that is employable to implement the work order generator168ofFIG.1. In the example illustrated, the work order generator200receives a standardized work request204that could be provided as a portion of work order data from a work request processing pipeline (e.g., the work request processing pipeline164ofFIG.1). The standardized work request204is processed by machine learning trained models of the work order generator200, which are employable to implement the K number of trained machine learning models172ofFIG.1.

More specifically, the work order generator200includes an outage model208(e.g., a software module) that can predict whether equipment identified in the standardized work request204will need to be completed during a plant outage of a nuclear power plant (e.g., the nuclear power generation system104ofFIG.1) in order to execute the operations needed to fix the equipment. To predict the outage indicator of the work order, the outage model208processes the description of the work, the location of work, equipment information, the system in which the equipment operates, and several aggregations of a number of equipment types or groups that have had outage work done previously. The outage model208can output outage data to indicate whether the equipment for the standardized work request204can be completed during the plant outage, which can entail a shutdown of a nuclear reactor (e.g., the nuclear reactor108ofFIG.1) and/or a steam turbine (e.g., the steam turbine112ofFIG.1).

The work order generator200includes a plurality of other machine learning trained models that are implemented as software modules. These machine learning trained models include a multi-media processor210, a priority model212, a unit condition model216, a package type model220, a nuclear applicable mode model224, a discipline model228and a work order data aggregator232.

The multi-media processor210can process images, audio and/or videos associated with the standardized work request204to determine multi-media data. The multi-media data identifies equipment present in the images, audio and/or videos. Additionally, in some examples, through processing of the images, the audio and/or the video, the multi-media processor210can provide sufficient information to prepare a recommendation for correcting an issue in the standardized work request204. For instance, suppose that the standardized work request204included an image of a valve. In this example, the multi-media processor210could process an image of the valve to determine a serial number and/or model of the valve (in which the serial number could be present in the image of the valve). Additionally, in this example, suppose that the multi-media processor210has historical data indicating that the particular model of the valve needs frequent cleaning. Thus, in this example, the multi-media data can identify the serial number and model of the valve, as well as a recommendation for cleaning the valve.

The priority model212predicts the priority of the standardized work request204based on the multi-media data and on an array of categorical variables, for example, the system code, equipment class, functional importance determination and single point vulnerability. The priority model212can be programmed to provide priority data indicating a priority of the work characterized in the work order request204.

The unit condition model216can receive the outage data and the priority data to determine a reactor stage needed for a reactor (e.g., the nuclear reactor108ofFIG.1) of the power generation system (e.g. a nuclear power plant) for maintenance of the equipment identified in the given work request. More generally, the unit condition model216determines when equipment for the standardized work request204can be worked on based on outage considerations as well as the status that the power generation system (of which the equipment for the standardized work request204is a component) should be in for work to be executed. To make this determination, the unit condition model216can analyze a history of work performed on this equipment. The unit condition model216can output unit condition data identifying the reactor stage based on the outage data and the priority data.

The nuclear applicable mode model224uses several categorical properties of operations for the standardized work request204. The nuclear applicable mode model224can determine a nuclear applicability of the work request based on the unit condition data, the outage data and the priority data. The nuclear applicable mode model224can provide nuclear applicability data characterizing the nuclear applicability of the standardized work request204. The nuclear applicable mode model224employs a modified random forest classifier that considers only valid modes for the nuclear reactor based on the outage data provided by the outage model208. The nuclear applicable mode model224is configured to select the valid mode with the greatest predicted probability for successful execution of the operations in the standardized work request204.

The package type model220receives the unit condition data, the multi-media data and the nuclear applicability data. The package type model220implements a random forest classifier with estimators (e.g.,120estimators) and balanced weights. The input data includes values from equipment name, location, description, and detailed description, combined with encoded categorical data. The package type model220employs this information to determine a complexity and/or risk associated with executing the tasks for the standardized work request204, and employs data characterizing the complexity and/or risk in package data. The package type model220also determines a solution and the level of instructions needed for a worker to address problems identified in the work request. For example, the package type model220can determine if few (or no) instructions are needed to execute the tasks for the work request or if a particular set of tasks would require complex instructions. The package type model220provides instructions in the package data for executing the operations corresponding to the work request commensurate with the determined complexity, and the package type model220outputs the package data.

The discipline model228receives the standardized work request204. The discipline model228analyzes the standardized work request204to predict one or more disciplines (e.g., electrical department, chemical department, etc.) for implementing the maintenance of the equipment identified in the work order. These disciplines reflect a nature and/or type of work needed for the standardized work order request204. Accordingly, considering the nature and/or type of work needed for the standardized work request204ensures (or at least improves the chances) that a technician with the correct skills (e.g., electrical skills, knowledge of chemistry, mechanical skills, etc.) will be assigned a corresponding work order. The discipline model228provides discipline data identifying each discipline for equipment maintenance.

The work order data aggregator232receives the outage data from the outage model208, the priority data from the priority model212, the unit condition data from the unit condition model216, the package data from the package type model220, the nuclear applicability data from the nuclear applicable mode model224and the discipline data from the discipline model228. Based on this data, the work order data aggregator232can generate a set of work orders236for work requests in the work request data, including a set of work orders236for the given work request. The set of work orders236can include one or more work orders that specify operations to provide maintenance to the equipment identified in the given work order236. For instance, suppose that the set of work orders236were related to fixing a leaking valve, such as the first example, wherein the valve was unreachable. In this case, the set of work orders can include, for example, operations related to setting up scaffolding, operating on the identified equipment and removing the scaffolding. Accordingly, in this example, there might be three different work orders that may be completed by different operators (or teams of operators).

Referring back toFIG.1, the work order data aggregator176provides a set of work orders178generated by the work order processor156to a scheduler180through the network136. The scheduler180can be representative of application software executing on a computing platform. Moreover, althoughFIG.1illustrates the scheduler180and the work order processor156as being implemented on separate computing platforms, in other examples, the scheduler180and the work order processor156can be integrated to operate on the same computing platform, such as the server140.

The work orders178generated by the work order processor156include an identification of equipment106, a priority of the corresponding work order178and a unit condition required (e.g., offline/shutdown) and a package type that are determined by the work order generator168. In some examples, multiple work orders178are associated with the same work request132. For instance, there could be three work orders generated for the replacement of a valve where scaffolding is needed, namely, a first work order to install the scaffolding, a second work order to execute maintenance on the equipment106and a third work order to remove the scaffolding.

The scheduler180can screen the work orders178. In some examples, some work orders178can be removed if it is determined that an uncompleted work order has already been issued for the same instance of equipment106. Each work order178is assigned to a given service crew184of G number of service crews184, where G is an integer greater than or equal to one. For instance, in some examples, the scheduler180determines the particular service crews184needed for a set of work orders178and the scheduling needed to execute each work order178in the set of work orders178. Stated differently, the scheduler180can be programmed/configured to identify specific service crews184trained in the particular operations needed to complete the corresponding work order178. For instance, in the first example, a particular service crew184skilled in valve replacement could be assigned a work order178corresponding to the work request132generated in the first example. Further, in some examples, the scheduler180can be configured to bundle multiple work orders178from different sets of work orders178(collected over time) to curtail component wear.

Additionally, the scheduler180is programmed/configured to distribute the work orders178at a time proximal to the time that each work order178can be executed. For example, suppose that a particular work order178indicates that the nuclear reactor108needs to be in an outage in order to be completed, and the next outage is schedule for 35 days in the future. In this case, the scheduler180could delay distribution of the particular work order178until a time near the outage until about 5 days prior to the outage.

Upon receipt of the work orders, the G number of service crews184can be deployed to complete the work orders. By utilizing the system100, the work orders178are correctly and timely prioritized to ensure efficient operation of the nuclear power generation system104. In particular, the work order generator168ensures that the proper skillset is identified to complete each work order178, thereby curtailing the deployment of the wrong service crew for maintenance. Further, as described, in situations where an outage is required, but the priority for executing a particular work order178is low, the completion of the work order can be delayed until a next scheduled outage of the equipment106(e.g., the nuclear reactor108).

Furthermore, the G number of service crews184can employ the end-user device128to provide feedback188characterizing observations made during execution of operations for a particular work order178. The feedback188can be positive (e.g., the particular work order178accurately described the situation and the work needed) or negative (e.g., mistakes were made during generation of the particular work order178). This feedback188can be provided to the work request processing pipeline164and/or the work order generator168. The work request processing pipeline164and/or the work order generator168can thus employ the feedback188to update the machine learning models in a reinforcement learning operation.

By employing the system100, accurate and timely work orders178are generated for the work requests132. In particular, work orders178are generated and efficiently distributed to the correct service crews184. Over time, conventional inefficiencies associated with untimely work orders and/or wrongly assigned work orders (based on the skill of the assigned service crews184) are curtailed. In this manner, the work request processor168and the scheduler180can operate in concert to tune equipment maintenance and improve an overall equipment life cycle of the equipment106, to curtail downtime at the nuclear power generation system104and improve safety at the nuclear power generation system104.

In view of the foregoing structural and functional features described above, an example method will be better appreciated with reference toFIG.3. While, for purposes of simplicity of explanation, the example method ofFIG.3is shown and described as executing serially, it is to be understood and appreciated that the present examples are not limited by the illustrated order, as some actions could in other examples occur in different orders, multiple times and/or concurrently from that shown and described herein. Moreover, it is not necessary that all described actions be performed to implement a method.

FIG.3illustrates a flow chart of a method300for generating work orders for a power generation system, such as the nuclear power generation system104ofFIG.1. The method300can be implemented by a computing platform, such as the server140ofFIG.1.

At310, a work request interface (e.g., the work request interface160ofFIG.1) executing on the computing platform can receive work request data for the power generation system. The work request data includes a work request comprising data characterizing equipment of the power generation system and a first state of the equipment. At315, a work request pipeline (e.g., the work request processing pipeline164ofFIG.1) executing on the computing platform can process the work request to modify at least one field in the work request to provide a standardized work request. The standardized work request includes data characterizing operations needed to change the state of the equipment from the first state to a second state.

At320, a work order generator (e.g., the work order generator168ofFIG.1) executing on the one or more computing platforms receives the standardized work request. At325, the work order generator determines a priority of the standardized work request. At330, the work order generator determines a mode of operation of the power generation system needed to change the state of the equipment from the first state to the second state. At335, the work order generator generates a set of work orders for the work request. The set of work orders identify the equipment, the determined priority, the operations needed to change the equipment from the first state to the second state and a skill set needed for each work order of the set of work orders.

At340, a scheduler operating on the computing platform selects and deploys a service crew (or multiple service crews) to execute the operations needed to change the equipment from the first state to the second state based on the skill set needed for each work order of the set of work orders.

What have been described above are examples. It is, of course, not possible to describe every conceivable combination of components or methodologies, but one of ordinary skill in the art will recognize that many further combinations and permutations are possible. Accordingly, the disclosure is intended to embrace all such alterations, modifications, and variations that fall within the scope of this application, including the appended claims. As used herein, the term “includes” means includes but not limited to, the term “including” means including but not limited to. The term “based on” means based at least in part on. Additionally, where the disclosure or claims recite “a,” “an,” “a first,” or “another” element, or the equivalent thereof, it should be interpreted to include one or more than one such element, neither requiring nor excluding two or more such elements.