Mitigating asset damage via asset data analysis and processing

An AI-based asset maintenance system accesses a variety of data sources related to an entity to analyze data regarding one or more damage mechanisms corresponding to the entity thereby identifying and implementing corrective actions that mitigate the effects of the damage mechanisms within the entity. The accessed data is stored using a parameterized data model that represents the entity. A trained parameter model identifies the most significant operating parameters for a given component of the entity for the damage mechanism affecting the component. A projection model is used to perform ‘what-if’ analysis of the most significant operating parameters for determining the instances of minimum and maximum degradation due to the damage mechanism. Corrective actions for mitigating the degradation due to the damage mechanism can be determined based on analysis of the operating parameters and other attributes corresponding to the best and worst case degradation scenarios.

BACKGROUND

In an establishment such as a manufacturing plant or a refinery unit wherein numerous machines interface to execute complex processes, maintenance and reliability necessitate exchange of data between various data sources including structured and unstructured data. Various maintenance procedures such as risk-based inspections (RBIs) or condition-based inspections can be implemented to maintain the equipment in good working condition. A risk-based inspection is a methodology used to examine equipment such as pressure vessels, heat exchangers, piping and the like in industrial setups. RBI requires an assessment of the probability of failure (PoF) associated with each equipment included in a particular processing unit. RBI can be used to prioritize inspection-related activities so that the true state of the equipment can be determined. Additional risk or damage mitigating activities can be identified via the RBI assessment.

International engineering standards and practices that relate to risk-based inspection can include, for example, American Petroleum Institute (API) RP (Recommended Practice) 580 and 581 etc. API RP 580 sets out the minimum guidelines for implementing an effective, credible RBI program. API RP 581 details the procedures and methodology of RBI.

DETAILED DESCRIPTION

According to one or more examples described herein, an AI-based asset maintenance system that accesses information regarding one or more of active damage mechanisms or potential damage mechanisms acting within an entity from a variety of data sources of the entity, processes the information and identifies corrective actions that can mitigate the effect of or prevent degradation due to the damage mechanisms is disclosed. The entity can include industrial sites which may be scattered at different geographical locations throughout the globe. The asset maintenance system accesses data related to the damage mechanisms from historical data sources which can include machine logs, activity records, reports generated by the asset maintenance system and the like. The damage mechanism or degradation mechanism can include one or more physical processes or chemical procedures that actively cause or potentially cause wear and tear of the hardware such as machinery and other equipment within an industrial setup. The asset maintenance system provides for a centralized storage or a single master data source for processing of various pieces of data corresponding to the damage mechanisms within the entity.

The historical data enables the asset maintenance system to assess the degradation of the equipment as it occurs or a potential degradation of the assets due to the damage mechanisms. In an example, the historical data can include time series data wherein certain asset attributes which can be indicative of the asset degradation are recorded over time. Based on the time series data, the rate of occurrence of the damage due to one or more damage mechanisms can be estimated via calculating the rate of change in certain asset attributes. In an example, the time series data can include current data which pertains to the latest attribute values and values of the operational parameters or values which have the most recent date/time stamps. The asset maintenance system can poll the various data sources of the entity periodically to collect the data. Alternately or additionally, the data sources can be configured to push the data to the asset maintenance system.

The data thus received is further processed by the asset maintenance system using a data model that provides a common taxonomy and a consistent format for the data. The entity, in accordance with an example, can be represented by the data model that includes various nodes connected via hierarchical relationships. The nodes represent the different elements of the entity so that each element of the entity is represented by a respective node. The elements which contain other elements are represented as parent nodes in higher levels of the hierarchy. The elements which are contained in other elements can be represented as child nodes in the lower levels of the hierarchy. For example, assets of an entity can further include one or more elements or components. The various properties and characteristics of the elements can be represented as attributes of the respective nodes.

The asset maintenance system includes a rules engine that stores rules which enable identifying those assets that are most vulnerable to the various damage mechanisms. In an example, the assets or components contained therein can be sub-divided into various classes based on the type of equipment. The extent to which a particular asset type or class is affected by a damage mechanism can vary from one class to another. Moreover, environmental factors of the individual components within a component class also influence the effects of the damage mechanisms. The rules within the rules engine are framed based on such considerations. The rules can include those rules which correspond to particular international protocols such as API 580 or API 581 in addition to custom rules that may be added by users of the asset maintenance system. For example, rules for individual elements which are customized per the environmental factors of those elements may be added by the users to the rules engine. The rules may represent the engineering principles that govern the assets so that when used in conjunction with statistical methods such as correlations, the rules enable predicting the damage to the assets due to a particular damage mechanism.

The rules are used to analyze various asset or component classes that are vulnerable to a damage mechanism. In an example, a vulnerable asset class can currently experience some degradation or wear and tear due to the damage mechanism. In an example, other potential damage mechanisms may indicate a likelihood of future degradation for some of the asset classes. Certain component attributes may be analyzed for identifying different damage mechanisms. For example, when analyzing the component classes for corrosion, measured thickness and corrosion rate may be considered to identify a component class that is most vulnerable to corrosion. In an example, an initial corrosion output can be obtained from the current data for each of the assets wherein the initial corrosion output also includes a respective projected thickness for each of the assets at a future time point based on the corrosion rate. A most applicable damage sub-mechanism can also be identified. For example, among the various types of corrosion mechanisms such as embrittlement, amine corrosion, amine cracking, atmospheric corrosion etc. sub-mechanisms, a most influential or a most applicable corrosion sub-mechanism can be extracted for a component class, based on the historical data and the component attributes.

The key performance factors for the component class such as operating parameters and other characteristics that characterize or are indicative of the degradation due to the most influential damage sub-mechanism are analyzed. The most significant operating parameters are identified using a trained parameter model. The data pattern of the most significant operating parameters and other characteristics for the most influential damage sub-mechanism is analyzed using a projection model to determine instances of maximum and minimum degradation. The projection model can be based on statistical methods such as but not limited to regression, gradient boost, random forest and the like. The maximum and minimum degradation instances enable identifying corrective actions to mitigate the effects of the damage mechanism. Various graphical user interfaces (GUIs) are also implemented to enable user interactions with the asset maintenance system. Some of the GUIs can include reporting and dashboards to facilitate insights regarding data compliance, completeness, integrity and accuracy.

The AI-based asset maintenance system described herein enables obtaining ‘a single version of the truth’ by providing for a master data source for data corresponding to various damage mechanisms such as corrosion which act within an entity. The asset maintenance system provides for a flexible asset hierarchy with well-defined data attributes. Data elements which pertain to different equipment and which would otherwise be stored in disparate data sources are brought together into the master data store which stores the data in a common format. As the data is now stored in a common format within the master data source, analysis of such data can produce insights that would not otherwise be possible if the data was stored in the disparate data sources in different data formats or different nomenclatures. The asset maintenance system is configured to comply with standards such as API 581 and facilitates seamless import and export of damage mechanism information across various systems of the entity or enables advanced search on the asset characteristics. The import and export data features enable the asset maintenance system to interface with other maintenance and reliability systems. Moreover, the loadable data which can be produced by the asset maintenance system can automate and improve productivity in the data injection activities. Also, the corrective actions that are identified and implemented by the asset maintenance system enable extending the longevity of machinery by monitoring and countering the effects of damage mechanisms even prior to their occurrences.

FIG. 1is a block diagram of an AI-based asset maintenance system100in accordance with embodiments disclosed herein. The asset maintenance system100is configured to monitor assets within an entity190or an organization via analyzing and processing data regarding the assets to identify damage mechanisms and to proactively initiate actions to counter the damage mechanisms thereby protecting the assets and prolonging the lifetime of the assets. The entity190as disclosed herein can include an establishment with sites192,194etc. at disparate geographical locations wherein various assets are maintained for its operations. The assets110maintained by the entity190at sites192,194can include process equipment like large machinery used for manufacturing, chemical processing, mechanisms used to transport goods such as pipelines, conveyor belts, containers with/without vehicles, computer systems and communication networks including hardware and software used for controlling and monitoring the aforementioned assets and the like. Accordingly, the asset maintenance system100can receive data from a variety of data sources120based on the type of assets110that are being monitored. In an example, the asset maintenance system100can be configured to monitor damage mechanisms within static assets such as boilers, pipelines and the like which have little or no moveable parts. The data sources120can include structured and unstructured data such as but not limited to machine logs, relational databases, engineering documents and other proprietary, non-proprietary or public data sources which may be associated with different pieces of machinery within the entity190. The assets110and consequently the data sources120may be located at disparate geographical locations spread throughout the globe and the asset maintenance system100can be connected to the assets110via communication networks such as the internet. In an example, the assets110can form an Internet of Things (IoT) network which may be partly or wholly monitored and controlled by the asset maintenance system100.

The asset maintenance system100includes a data collector102that is configured to connect to the data sources120and collect data for analysis and processing. In an example, the data collector102can be configured to collect data related to various damage mechanisms or degradation processes including physical and chemical processes that cause routine wear and tear to the assets110within the entity190such as but not limited to, rusting, corrosion, friction, heating, cooling, high/low pressure and the like. It can be appreciated that each of the damage mechanisms can affect specific pieces of machinery or a given asset in a particular manner and measurement of certain characteristics or attributes of the assets can help in identification, analysis and mitigation of degradation that can occur due to the damage mechanism. The asset maintenance system100can access historical data122related to the damage mechanisms to assess degradation due to of exposure of the assets110to the various damage mechanisms. In addition, the asset maintenance system100can also be configured to receive current data124that is indicative of the current condition of the assets110. The current data124can be indicative of the current conditions or the current attributes of the various elements within the assets110.

As data of various formats, of various types and various versions is received from the data sources120, a ‘single version of the truth’ or a single ‘master data source’ is required for accurate analysis and processing of the damage mechanism data so that correct solutions for mitigating asset damage can be identified. As similar processes occurring at different geographic locations can give rise to variations in the possible DMs, aggregating data from different sites192,194can be helpful in identifying newer trends than would otherwise be possible if the data analysis was isolated to each individual site. Also analyzing DMs in view of the environmental conditions present at each geographic location can be helpful in identifying similar situations as they occur in other geographic locations at different times. Accordingly, the analysis of data aggregated from different sites enables in identifying solutions for such DMs to be determined. The asset maintenance system100therefore includes a data model104which models the entity190, the assets110within the entity190that are included and the various characteristics of the assets110as in a hierarchical arrangement as a network of nodes wherein each node within the network represents a particular element of the entity190. The elements represented by the nodes can include the disparate geographical locations or sites of the entity, the units within each site, the various assets110in each unit, the components within the assets110, the damage mechanisms acting within the entity190, the measurements taken for the various assets/components thereof, the locations of the assets, the inspection procedures within the assets, the measurements obtained from the assets and the like. Details of the data model104will be discussed further infra. Each of the nodes of the data model104enables storing the current data124as attributes of the corresponding nodes. In an example, the current data124can include time series data related to measurements of an attribute of an asset or a component over a period of time. The series of attribute values obtained from the time series data enable monitoring effect of one or more damage mechanisms on the corresponding component. By the way of illustration and not limitation, the current data124can include a series of thickness measurements of a component and related temperature measurements of the component. The asset maintenance system100therefore provides a centralized data management for information from the various data sources scattered across the globe. The data thus processed by the data model104can be stored to a data repository130as processed information126for further analysis

A data analyzer106can include a rules engine162which employs the processed information126for enabling execution of one or more of Quality Assurance (QA) or Quality Control (QC) activities. In an example, the rules engine162enables the asset maintenance system100to operate per one or more of industry specific standardized rules such as American Petroleum Institute (API) 581, 580.1 and customized rules that may be particular to specific components or specific data sets. Administrative users of the asset maintenance system100can develop the customized rules based on input from various engineers/technicians administering the assets110of the entity190in an example. The rules1622enable identifying those assets that are most vulnerable to or more prone to be affected by the various damage mechanisms.

In an example, the assets110may be sub-divided into classes based on the type of equipment. Various classes of assets or components thereof are represented by the nodes of the data model104. For example, certain material or chemical processing tanks may form an asset class. Similarly, a pipeline network may form an asset class of which the pipelines and the valves may form different component classes within the pipeline asset class. Therefore, it can be appreciated that the extent of damage due to a damage mechanism can vary from one asset/component class to another asset class. Therefore, a given asset class or component class may have a particular damage mechanism as a most applicable damage mechanism that causes the most wear and tear to that particular class of components. The asset maintenance system100can be configured to identify the applicable degradation mechanism(s) at various levels of detail based on the rules1622. For example, the asset maintenance system100can identify a particular corrosion mechanism as the most applicable corrosion mechanism for a given component class from the various corrosion mechanisms such as corrosion under insulation (CUI), caustic corrosion, sulfuric acid corrosion, CO2 corrosion, soil corrosion, and the like that may affect a particular pipeline within a refinery.

Other environmental factors unique to a particular asset such as, the geographic location and weather conditions at the geographical location or placement of the asset within the given unit, the usage level of the asset may also counter or exacerbate the effects of the damage mechanism. For example, a pipeline that may be constantly transmitting fluid can be subject to greater corrosion as compared to another pipeline within the same unit. Similarly, one unit may have higher capacity or greater demand as compared to other units. As a result, the machinery of the unit with greater demand is subject to higher damage and hence requires a more extensive maintenance as compared to another unit with lesser demand. Furthermore, the geographical location and hence the environmental conditions of a unit affects the condition of the machinery within the unit. Hence, different machines within a unit or the same asset class within different units in different geographical locations may be subject to different levels of degradation via the same damage mechanisms due to secondary factors such as location and/or demand, usage and the like.

The rules1622can include rules customized to take into account the various environmental factors described above. For example, an asset at a geographical location with a higher temperature may have its corrosion proportionately multiplied by a factor. The rules1622enable the data analyzer106in identifying key factors affecting a given component class for a given damage mechanism. These can include operating parameters of the asset or the component class and attributes of the assets or components. Referring to corrosion damage mechanism within a refinery as an example, operating parameters of containers and conduits employed in holding and transporting the fluids and attributes of such equipment can be analyzed per the rules1622. The rules1622can specify, for each of the damage mechanism, the attributes of the components and the operating parameters to be analyzed in order to identify a most vulnerable component112for that damage mechanism.

The data analyzer106can further identify one or more of most significant operating parameters. A trained parameter model142such as but not limited to a co-relational model, can be used for identifying the most significant operating parameters as detailed herein. The parameter model142can be trained on historical data122, for identifying the most significant operating parameters for a given component for the damage mechanism affecting the component. The training can involve one or more of supervised or unsupervised learning. In an example, statistical correlational strengths between the damage mechanism and the operating parameters can be indicative of significance of the operating parameters. Furthermore the statistical correlations can be vetted by engineering principles represented by the rules1622for the identification of the most significant operating parameters.

Based at least on the most significant operating parameters identified by the parameter model142, actions that can be implemented for minimizing or preventing damage from the damage mechanisms are identified by a damage minimizer108. In some examples, data patterns including combinations of the operating parameters and other attributes or characteristics of the components can be used to perform ‘what-if’ analysis employing regression analysis so that anomaly event scenarios with the best and worst performing instances can be determined for the most applicable damage mechanism for the most affected asset. The conditions associated with the best and the worst performing instances of the operating parameters and other characteristics can be identified from the historical data122. In an example, an AI based projection model144can be employed to perform ‘what-if’ analyses for various conditions encountered by the assets110. Various models based on algorithms such as but not limited to regression, gradient boost model, random forest and the like can be employed for the projection model144. The projection model144can project the time series data from the current data124associated with the most applicable damage mechanism to determine the consequences of continuance of the present conditions as determined from the historical data122. More particularly, the conditions involving combinations of the attributes and the operation parameters during the best performing instances which represent minimal degradation due to the most significant damage mechanism can be determined.

Corrective actions to protect the component from the damage mechanism can involve establishing conditions of the best performing instances within the assets110in an example. Accordingly, the actions thus identified can be communicated via various channels to the responsible personnel. A damage minimizer108can be configured to automatically effect the changes within the assets110in an example. Tools such as, Application Programming Interfaces (API) of third party systems involved in control and operations of the entities can be employed to automatically execute the identified actions. Furthermore, the rules1622can also include information that enables performing quality control activities per industry standards. For example, when the processed information126pertains to corrosion mechanisms, the rules1622can implement Quality Assurance and Control activities with respect to API 580 and/or 581 methodology on the asset corrosion data.

The asset maintenance system100includes various GUIs140that enable user interactions. In an example, one of the GUIs can enable user control of one or more of the data collector102, the data analyzer106and the damage minimizer108in order to allow users to manage and approve attribute values associated with the nodes in the asset hierarchy as represented by the data model104. The GUIs140can also involve data import/export GUIs1422that enable importing data from and exporting data to the data repository130via user-defined data collection templates. The GUIs140also enable, via search interfaces1424, advanced searches on asset characteristics. For example, processed information126can include simple attributes or values calculated from various element attributes for each individual element within the asset. The individual asset attributes from the multiple sites192,194of the entity190can be retrieved via the search interfaces1424at the click of a button. The asset maintenance system100also includes reporting capabilities that generates one or more of one-time or periodic reports and delivers them through various channels such as email, file share locations etc. In an example, the asset maintenance system100can be built on AZURE cloud with MICROSOFT applications stack, Hypertext Markup Language (HTML) 5.0 and JQuery. It can be appreciated that other technological platforms can also be used to build the asset maintenance system100in accordance with examples disclosed herein.

FIG. 2illustrates a block diagram of the data model104representing the entity190in accordance with examples disclosed herein. It can be appreciated that the data model104can be a generic parameterized data model which can be used to represent hierarchical relationships that may exist between different elements of an entity or an organization. The hierarchical network200in particular, represents the entity190wherein each node210represents a site, a process, a component, a data source such as a document, a database, a report, an attribute and the like. The nodes which are at a higher level in the hierarchical network200carry a parent-child relationship with the associated nodes in the levels below. The parameterized nature of the data model104enables customizing the data model104for various domains which require asset monitoring and management. The data model104in this example, represents a particular site192of the entity190. As mentioned earlier, the entity190can have multiple sites at192,194at different geographical locations. The data model104is configured to support multiple sites within the entity190in one database. Therefore, the data model104can also have other hierarchical networks associated with other sites such as the hierarchical network200. Of course, the nodes and interrelationships between the nodes can be different from the hierarchical network200. The information regarding each site can be stored as values within the node tables that are detailed further herein.

Each site192of the hierarchical network200can have multiple units202wherein each of the units include assets206and systems204. In an example, assets206can refer to the physical entities such as the assets110which include machinery and hardware within the entity190whereas systems204refer to logical representations of the assets110within the hierarchical network200. Therefore, asset nodes206represent assets while the systems are represented by the system nodes204and the components are represented by the component nodes208. As an example, each of the assets are further made up of components which can be independently monitored and analyzed. Each of the component nodes208can be further associated with child nodes including nodes that represent damage mechanisms212and condition monitoring location (CML) groups214. CMLs can be designated locations within the components where measurement of component attributes are conducted to monitor the presence and rate of damage/degradation due to the various damage mechanisms. For example, if the damage mechanism pertains to corrosion, then CMLs can be particular points within the component nodes208such as pressure vessels or piping where thickness measurement inspections (TMIs) are conducted. Various CMLs associated with a given component can form a CML group under that component. Each of the CML groups214further includes inspections222, CMLs218and the CMLs218further include CML measurements216. Representation of the various elements of the entity190enables capturing the properties of such elements in terms of attributes and characteristics of the nodes. As a result, the information from the different hardware and software systems having various data formats from multiple locations can be stored in a uniform data format which enables advanced search such as via the search interfaces1424.

FIG. 3illustrates a block diagram300of the data model104in accordance with examples disclosed herein. The data model104serves to abstract the elements within the organization or the entity190for storage to a centralized database. The data model104is premised upon commonality within the properties or attributes of the nodes of the hierarchical network200. These properties can include primary properties which are common between the nodes and auxiliary properties which are unique to certain nodes. Additionally, the hierarchical network200represents the parent-child relationships between the different nodes and the inter-relationships between nodes at the same hierarchical level. The data model104can be flexible and can be extended to accommodate newer entities and properties.

In an example, the data repository130can be a database which stores processed information126regarding the entity190. The database can be normalized to the third normal form (3NF) in one example. Nodes corresponding to units, assets, CML groups, CMLs, CML measurements, inspections, systems etc. may have common attributes such as name, id, type, description, operating state, legacy id, etc. A node representing a unit may have one or more auxiliary properties which may not be included in other node types. For example the child nodes of a certain component class may each have an auxiliary property which may not be included in parent node.

The data model104provides for a central node table and related tables which store primary information (such as attributes) and auxiliary information such as, characteristics, associated document links, lookups etc. The related tables include a node type table320, a node attribute table330, a node characteristic table340, a node relation table350, a node type relation table360, a node document table370, a node component type table380and a node component relation table390. Similarly lookup tables for the node family, the node characteristics, the node data source and the like may also be included in the data model104. The parameter values of the elements modeled by the data model104are thus stored in a variety of tables and the interrelationships or links between the tables represent the hierarchical relationships between the elements of the entity being modeled.

Each node table310within the database that stores information regarding nodes within the hierarchical network200. The node table310can include a node id302which may be of alpha-numeric data type and uniquely identifies a node, a node name304of string data type and a node type ID306which conveys the type of node represented by the node ‘ID’. Information regarding the node type can be stored in the node type table320which can specify if the node is an asset, a system, a component etc. The attributes of the nodes are stored in the node attribute table330while characteristics of the node are stored in the node characteristics table340. The relationships of the node such as a parent ID of the node are found in the node relations table350while the information regarding different relationships such as parent or child is stored in the node type relations table360. Information sources regarding the nodes such as the various data sources120which can include engineering documents and the like from which attribute, characteristics and other node data is extracted are specified in the node document table370.

FIG. 4shows a block diagram of the data analyzer106in accordance with examples disclosed herein. In addition to the rules engine162, the data analyzer106also includes a damage mechanism (DM) identifier402and a component analyzer404. Different damage mechanisms may affect different attributes of the components. The rules1622can specify particular component attributes that are to be examined or processed to determine the extent of degradation caused by the various damage mechanisms. In an example, the rules1622can include methodologies to obtain calculated values from various component attributes that enable determining the effects of various damage mechanisms. As mentioned above, various types of component or component classes within the assets110may be affected differently by different damage mechanisms. The DM identifier402employs the rules1622pertaining to each of the damage mechanisms to analyze the various component classes that makeup the assets110. Accordingly, a most applicable damage mechanism/sub-mechanism can therefore be identified by for each class of components. The most applicable damage mechanism can be configured within the rules162for each of the elements within the assets110in an example. However, at various times, different damage mechanism may take precedence or have greater potential to cause damage in response to the environmental conditions. Therefore, the most applicable damage mechanism can be obtained by comparing a percentage of deviation of a characteristic attribute from the optimal value of that attribute in an example. When the effects of corrosion are determined, various component types that are likely to be affected by different types of corrosion can be analyzed based on measured thicknesses and corrosion rates. Among the different types of corrosion, a most applicable corrosion mechanism or the corrosion mechanism that actively causes or can potentially cause the most degradation for each component class can be identified by the DM identifier402.

In addition to particular damage mechanisms, the rules1622may also specify which of the environmental factors affect each of the component classes and how the environmental factors affect the component classes. The values of particular environmental factors for the component classes in the entity190can be obtained, for example, from the processed information126. Therefore, the damage mechanism, in an example, can be further analyzed in view of the environmental factors. In certain cases, the environmental factors may enhance or reinforce the effects of the damage mechanisms. In some other cases, the environmental factors may mitigate or counter the effects of the damage mechanisms. Hence, the identification of the most applicable damage mechanism for a given component class within the entity can further take into account the environmental factors present at the site192for the component class.

The information regarding the applicable damage mechanisms for each component class and the environmental factors associated with the various components within the entity190is received by the component analyzer404. The component analyzer404can be configured to determine the most applicable damage mechanism for each component taking into account the environmental factors associated with that particular component. Furthermore, the component analyzer404can further analyze certain key performance factors for each damage mechanism for a given component based on the most applicable damage mechanism for that component. In an example, the component analyzer404can employ the trained parameter model142for identifying at least one most significant operating parameters and other component attributes that can affect the most applicable damage mechanism for that specific component. In an example, the data patterns from the processed information126that are associated with the maximum damage instances can be identified by the trained projection model144for the specific component based on the identification of the most significant operating parameters412and other component attributes414.

FIG. 5shows a block diagram of the damage minimizer108in accordance with examples disclosed herein. A degradation estimator502included in the damage minimizer108receives information regarding the most significant operating parameters412and other component attributes414for the most applicable damage mechanisms for each of the components and performs a ‘what-if’ analysis that provides the best and worst performing scenarios with the least and most degradation respectively for each of the components. In an example, the AI-based projection model144is employed by the degradation estimator502to determine the best and worst performing scenarios of a given component due to the most applicable damage mechanism corresponding to that component. The AI-based projection model144can be trained via supervised or unsupervised training to identify particular instances where individual trends of the operating parameters412and the other attributes414as well as combinations of the individual trends have consequences for damage mechanisms for the components. Turning to the corrosion example, flow of a particular fluid through a component such as a pipeline with increasing temperature may project higher degradation for the pipeline. Conversely, flow of a particular fluid through a component such as a pipeline with decreasing temperature may project lower degradation for the pipeline. In an example, the AI-based projection model144can be configured to extrapolate individual trends and various combinations of each of the individual trends with other trends to obtain various projections.

An action recommender504included within the damage minimizer108identifies actions to be recommended by analyzing the individual trends and the combinations of the individual trends. For example, via comparisons of various individual trends with the respective thresholds, certain actions can be determined to bring those attributes which are exceeding the thresholds back within the threshold values. Certain other actions may be determined based on combination of individual trends. By the way of illustration, corrosion due to a liquid being held or transported by a component such as a tank or a pipeline may be within a threshold but external environmental factors such as temperature may be trending to cause higher corrosion. Accordingly, action to counter such effects may be determined. Converse analysis can also be implemented wherein the corrosion may be trending towards exceeding the individual threshold but environmental factors such as temperature or other events such as a planned maintenance activity in the near future for the affected component may render any action to counter the effect redundant, so no action may be initiated by the action recommender504. In another example, wherein a corrective action to counter the damage mechanism requires human intervention, the corrective action would involve informing the concerned personnel of the results of the analysis along with the recommendation.

An input generator506generates an input or initiates the action based on the determinations from the action recommender504. As mentioned herein, the asset maintenance system100can interface with third party control tools based on the API put forth by such tools to initiate automatic actions that can effect changes within the entity190which mitigate the impact of the damage mechanism. For example, to increase temperature, decrease pressure or other such automatic actions552can be automatically initiated by the control interfaces562that can be included in the input generator506. The input generator506also includes messaging interfaces564that can be configured to send various communications to contacts configured therein. The communications can include alerts554that are generated in response to any urgent actions that may be identified by the action recommender504. As discussed herein, the processed information126includes time series data of the various component attributes. Therefore, if any component attribute is changing at a rapid rate, the action recommender504may associate an urgency with the recommended action whereby the input generator506can be configured to send out an alert554in response to receiving the recommended action from the action recommender504. In an example, an automatic action may also be accompanied by an alert554that informs the responsible personnel regarding implementation of the automatic action. In a further example, the input generator506may also be configured to log the various actions implemented within the action logs556. The action logs556can be used in various ways. For example, the action logs556may be used to further train one or more of the parameter model142and the AI-based projection model144. In another example, the action logs556may be further processed to generate periodic or one-time reports regarding the functioning of the assets110.

FIG. 6is a flowchart600that details a method of executing monitoring and maintenance activities within an entity as executed by the asset maintenance system100in accordance with examples disclosed herein. The method begins at602wherein the processed information126regarding the assets110pertaining to a particular damage mechanism acting within the entity190is accessed. Data pertaining to one of the various damage mechanisms such as friction, corrosion or even a particular corrosion type from the various types of corrosion can be accessed at602. The processed information126can include inputs from the historical data122related to the assets110with respect to the damage mechanism combined with the information from the data model104. The historical data122in some examples, can include data collected via various inspections and maintenance activities. This data can further include the time series measurements of the operating parameters. Different time series data can be accessed at602based on the kind of damage mechanism being analyzed.

Curated historical data can encompass information such as but not limited to, data identifying particular components within the asset110such as the asset name, the component name, the component type stored in the t_Node table310, the attribute values of the components which may be stored in the t_NodeAttribute table330, values required for estimating effects of particular damage mechanisms which may be calculated from the component attributes extracted from the t_NodeAttribute table330and the like. The curated historical data accessed at602can also include hierarchical information regarding the components such as the particular unit and asset that the component forms a part of or a child component that may be contained in the component which can be obtained from one or more of the t_NodeRelation350or t_NodeComponentRelation390tables. The attribute information regarding the component can include data related to the material of construction, the damage mechanism associated with the component such as the CML groups, RBI data, consequence evaluations, susceptibility levels of the component for each of the damage mechanisms acting within the entity190and the like. For example, when the damage mechanism pertains to corrosion, the accessed data may include thickness measurements and corrosion susceptibility. In some examples, the processed information126can include time series data such as but not limited to time/date of the measurements, operating parameters, thickness measurement location, process fluid, the insulation thickness, the cladding material and the like. Derived or calculated values such as but not limited to, initial damage calculations like initial corrosion output which can include a component thickness and corrosion rate corresponding to a specific date/time also form part of the processed information126. The initial damage calculations can also include the evaluated consequences obtained by extrapolating the corrosion rate across a time period. It can be appreciated that corrosion is discussed herein as an example of a damage mechanism other damage mechanisms may be similarly monitored and managed to mitigate the degradation of components caused by such damage mechanisms and for prolonging the life of the assets110.

In an example, the processed information126can include data imported from the various data sources120such as the sensors within the entity190, various proprietary databases associated with the entity190and the engineering documents that may be generated during various processes. The data regarding the assets110is cleansed to remove erroneous data values, de-duplicated to identify duplicates which can be reconciled into single records, collated and consolidated to comply with data standards. In an example, the cleansed data can also be enriched with additional data such as the information from the data model104described above. The data thus processed can be imported into the data repository130via user defined templates which enables generating a data set that can comply with specific industry standards such as API 580 or API 581. In further processing, data validation rules can be run against the entity data prior to storage to the data repository130to ensure integrity of entity records.

Different types of components or component classes are analyzed at604based on the attributes and the measured values from the processed information126to determine a component class that is most affected by or most vulnerable to one or more of the damage mechanisms for which the data was accessed at602. For example, a component class or components of a particular type can be impacted most by a damage mechanism like corrosion whereas another component class or another type of components may be impacted most or may be the most affected due to friction. Based on the particular damage mechanism being analyzed, a particular component class is determined at604as the most vulnerable. In an example, if the damage has already occurred, then the most vulnerable component class can be a collection of a particular type of components that has sustained maximum damage from the DM as a whole even if there may be variations in the damage to each individual component within the most vulnerable component class. In an example, if potential future damage is being assessed, the most vulnerable component class is a collection of components wherein the projected attributes indicative of the damage show maximum deviation from the optimal values signifying low degradation in the historical data122. Degradation of the component class can be determined based on the historical data122versus the current data124comparisons wherein the various attributes of the components within the component class can be compared to respective prior values. In some examples, particular attributes can be affected by particular damage mechanism and accordingly, comparisons of such attributes can enable determination of degradation due to corresponding damage mechanisms. By comparing the average degradation of a characteristic attribute of a damage mechanism in between different component classes, for example, the most vulnerable component class for that damage mechanism or the component class that is most prone to corrosion (wherein the damage mechanism being analyzed is corrosion) is identified at604.

In an example, a most applicable or a highest impacting damage sub-mechanism can be optionally identified at606for the component classes identified at604. Referring to corrosion as an example of the damage mechanism, a corrosion type which shows maximum impact on the most vulnerable component class of the entity190for that damage mechanism is identified as the damage sub-mechanism. In an example, measurable component attributes can be associated within the asset maintenance system100for each of the damage sub-mechanisms and the measurable component attributes that are indicative of highest damage can be identified as the most applicable damage sub-mechanism for that most affected component class. In an example, the damage sub-mechanism corresponding to a component attribute which may have a highest deviation from prior values can be determined as the most applicable damage mechanism. As mentioned herein, various types of corrosions such as but not limited to, amino acid corrosion, environmental cracking, caustic corrosion and the like may be acting within the entity190and the damage sub-mechanism which most affects the component class is determined at606.

At608, the time series data including values captured through various sensors, manual measurements at different time points are analyzed to identify the key performance factors for the most vulnerable component class for the most applicable damage sub-mechanism. The time series data can be analyzed with respect to various factors which not only include the damage sub-mechanisms but also the process fluids, the materials employed in the various processes, the construction materials of the components, the operational parameters like temperatures, pressures and the like. The impact of these factors is analyzed by the data analyzer106in identifying key performance factors affecting a given component class for a given damage mechanism using the rules1622.

A statistical methodology such as a correlation algorithm can be employed at610for identifying the most significant operating parameters and other characteristics that affect within the most applicable damage sub-mechanism. A ‘what-if’ analysis using regression techniques is performed on the data patterns of the most significant operating parameters and the component attributes. For example, effects of various operating parameters such as temperature, pressure, liquid pH etc. in combination with component attributes such as the material of construction of the component, process fluid, insulation, cladding material and the like can be projected or extrapolated to future time points to identify instances of maximum and minimum degradation with the most applicable damage sub-mechanism. The projection model144can be used to make the projections for the maximum and minimum degradation instances. At612, the operating parameters and the component attributes associated with the maximum degradation instances can be identified.

FIG. 7is a flowchart700that details a method of implementing changes within the entity190to reduce degradation due to a damage mechanism in accordance with examples disclosed herein. The method begins at702wherein the values of the operating parameters and the component attributes of a component associated with instances of maximum degradation are selected. For each of the components, the values of the operating parameters and the attributes that were selected are compared with the corresponding values in the current data at704. The differences between the values selected at702and the values from the current data are obtained at706. The differences can be obtained for example, via executing mathematical operations between the selected values and the current values in the case of numerical parameters or attributes.

Corrective actions to bring the current values in line with the respective optimal ranges are identified at708. The corrective actions can include the actions which can be automatically performed or the corrective actions can include those actions that necessitate human intervention. Automatically executed actions can involve those actions which can be executed without human intervention such as but not limited to, settings of the operating parameters like temperature, pressure, shutting down an element or activation of necessary mechanisms such as for reducing flow through the degraded element and the like. It is determined at710if the corrective actions can be automatically implemented. In an example, the input generator506can be configured with the corrective actions that are automatically executed via the control interfaces562which may enable a tripping event within a factory control system and the like. The corrective actions can include varying the operating parameters to be consistent with the optimal ranges. The corrective actions can also include taking an element offline from a communication network within the entity190such as a factory network and activating a backup element, and the like. If it is determined at710that the corrective actions can be automatically implemented, such automatic corrective actions are implemented at712via the control interfaces562. For example, corrective actions such as adjusting operational parameters, disabling a malfunctioning device etc. can be automatically implemented by generating input signals that cause the associated machinery to make adjustments per the identified corrected actions. In some examples, the control and administrative activities of the machinery can be enabled by APIs put forth by the machinery which APIs are made use of by the asset maintenance system100for the automatic implementation of the corrective actions. If the corrective actions cannot be automatically implemented, the asset maintenance system100can be configured to determine messages to be transmitted at714including the corrective actions and the messages thus determined are transmitted to the responsible personnel via the messaging interfaces564at716.

When analyzing data related to DM such as corrosion, the asset inputs from the asset maintenance system100can include, hierarchical information such as the units, assets, components, DMS, CML groups, analyses such as RBI analysis, DM evaluation, thickness measurement, CMLs etc. In addition curated historical data of the assets including the identification and hierarchical information of the elements in the entity190, the time series data, thickness measurement locations (TMLs), process fluids, insulation, cladding material etc. is employed in the analysis of corrosion mechanisms. An initial corrosion output such as thickness and corrosion rate obtained at a certain date/time, and evaluated consequences such as the future projections based on the corrosion rate and the measured thickness enables the asset maintenance system100to analyze corrosion and obtain the corrective actions. Examples of some of the various GUIs140associated with the asset maintenance system100are discussed below. It can be appreciated that user interfaces other than those discussed below can also be associated with the asset maintenance system100. Additionally, the below user interfaces show data related to corrosion but the GUIs140can also enable display and manipulation of data related to other damage mechanisms.

FIG. 8shows a capture user interface800associated with the asset maintenance system100in accordance with the examples disclosed herein. As mentioned herein, the asset maintenance system100receives data from the various data sources associated with the entity190and generates processed information126. The capture user interface800shows the data captured by the asset maintenance system100from the data sources120for a selected component810. The various elements within the entity190are organized for display within the capture user interface800per the data model104. Accordingly, the capture user interface800displays among other elements, damage mechanisms802, CMLs804and inspections806associated with the component810. The attributes812and characteristics814of the component810are also displayed.

FIG. 9shows an audit screen900of the asset maintenance system100in accordance with examples disclosed herein. The audit screen900provides access to the various audit reports912executed by the asset maintenance system100for the different components of the entity190. The site box902and unit box904enable selection of the site and the unit for which the audit reports are desired. The details906regarding the various components that are flagged on audit are shown on the audit screen900. The details906not only include the component identification and hierarchy information such as the node name, the parent component, the node id and the like but the information at906can also include the node characteristic908, the value of the node characteristic914and the reason916associated with the node characteristic that caused the component to be flagged.

FIG. 10shows a DMR screen1000that facilitates review of the various DMs acting within the entity190in accordance with the examples disclosed herein. The DMR screen1000displays, in addition to the component name1002, the operational parameters1004, the various properties or attributes1006of the component such as the material of the component, nature of the material and the applicable damage mechanism1008. The various damage sub-mechanisms1010that can be applicable for the various components are also displayed. The applicable damage sub-mechanisms can either be assigned by the users to particular components or may be automatically assigned based on configuration of the asset maintenance system100for particular damage mechanism or from the processed information126.

FIG. 11illustrates a UI1100that shows a node document1102in accordance with some examples disclosed herein. The processed information126can include data extracted from such node documents. The documents can include engineering information resources associated with the entity190available via the data sources120. Text analysis techniques and natural language processing (NLP) can be employed for gathering information from the node documents.

FIG. 12shows a UI1200that enables ‘what-if’ analysis by users in accordance with the disclosed examples. As mentioned herein, statistical models such as but not limited to, regression analysis can be applied by the asset maintenance system100to time-series data from the entity190in order to obtain projections regarding the effects of corrosion on the assets110. The UI1200can be configured to show data related to a particular piece of equipment. Various UI elements including a damage scale1210and sliders related to operation parameters such as operational pressure1202, coating quality1204, fugacity of CO21206and operating pH1208enable a user to vary the operational parameters to study the effect of the respective parameters on corrosion. The damage scale1210and the sliders can be operated synergistically so that movement of the marker1212can indicate a numerical value representative of the corrosion on the particular piece of equipment and the slides1202-1208are correspondingly moved to indicate the operational parameter values associated with that particular value of corrosion. Conversely, the UI1200permits the user to move one or more of the sliders1202,1204,1206and1208to study the effect of that particular slider on the corrosion. The marker1212moves on a scale of green1214, amber1216and red1218wherein green area1214indicates a minimum corrosion situation, the amber area1216indicates tolerable corrosion while the red area1218indicates high corrosion situation that may need to be corrected.

In some examples, the various thresholds for the damage indicators—red, amber and green can be automatically set based on one or more of the historical data122and the rules1622. The image1220shows various portions of an asset with different levels of corrosion effect. The portion of the image1220under the green scale1214shows no corrosion, while the portion of the image1220under the amber scale1216shows tolerable corrosion which does not need immediate action. The portion of the image1220under the red scale1218however shows considerable damage which requires correction. Accordingly, a person operating the sliders1202,1204,1206,1208and the scales1214,1216, and1218can be made aware via graphics the amount of corrosion damage indicated by the scales.

In some examples, all high corrosion situations need not be addressed. Certain situations may exist wherein high corrosion is temporarily detected but can eventually settle down to lower corrosion rates. Such situations can be detected based on the rules1622. For example, subject matter experts (SMEs) like engineers may configure the rules1622to prevent alarms from being raised under certain circumstances. One methodology of preventing alarms can include adjustment of the green, amber and red thresholds so that a corrosion value that would have otherwise moved the marker1212into the red area1218causes the marker to remain within the amber area1216during an anomaly situation for example, via varying the extent of areas under different colors on the damage scale1210.

FIG. 13illustrates a computer system1300that may be used to implement the asset maintenance system100. More particularly, computing machines such as desktops, laptops, smartphones, tablets, wearables which may be used to generate or access the data from the asset maintenance system100may have the structure of the computer system1300. The computer system1300may include additional components not shown and some of the components described may be removed and/or modified. In another example, a computer system1300can sit on external-cloud platforms such as, Amazon Web Services, or internal corporate cloud computing clusters, or organizational computing resources, etc.

The computer system1300includes processor(s)1302, such as a central processing unit, ASIC or other type of processing circuit, input/output devices1312, such as a display, mouse keyboard, etc., a network interface1304, such as a Local Area Network (LAN), a wireless 802.11x LAN, a 3G or 4G mobile WAN or a WiMax WAN, and a computer-readable medium1306. Each of these components may be operatively coupled to a bus1308. The computer-readable medium1306may be any suitable medium which participates in providing instructions to the processor(s)1302for execution. For example, the computer-readable medium1306may be non-transitory or non-volatile medium, such as a magnetic disk or solid-state non-volatile memory or volatile medium such as RAM. The instructions or modules stored on the computer-readable medium1306may include machine-readable instructions1364executed by the processor(s)1302to perform the methods and functions of the AI-based asset maintenance system100.

The asset maintenance system100may be implemented as software stored on a non-transitory computer-readable medium and executed by the one or more processors1302. For example, the computer-readable medium1306may store an operating system1362, such as MAC OS, MS WINDOWS, UNIX, or LINUX, and code1364for asset maintenance system100. The operating system1362may be multi-user, multiprocessing, multitasking, multithreading, real-time and the like. For example, during runtime, the operating system1362is running and the code for the asset maintenance system100is executed by the processor(s)1302.

The computer system1300may include a data storage or non-transitory computer readable storage medium1310, which may include non-volatile data storage. The data storage1310stores data used by the asset maintenance system100. The data storage1310may be used to store the processed information136, intermediate values generated during the analysis of damage mechanisms or components, the rules1622, one or more of the trained parameter model142and the projection model144and the like.

The network interface1304connects the computer system1300to internal systems for example, via a LAN. Also, the network interface1304may connect the computer system1300to the Internet. For example, the computer system1300may connect to web browsers and other external applications and systems via the network interface1304.