Patent Publication Number: US-2023141708-A1

Title: System And Method for Predicting End of Run for Equipment and Components of Such Equipment Based on Field Inspection and Operational Data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation-in-part of U.S. patent application Ser. No. 17/938,519 filed on Oct. 6, 2022, which claims priority to Canadian Patent Application No. 3,138,441 filed on Nov. 10, 2021. This application also claims priority to Canadian Patent Application No. 3,178,935 filed on Sep. 29, 2022. The contents of these applications are incorporated herein by reference in their entirety. 
    
    
     TECHNICAL FIELD 
     The following generally relates to predicting end of run for equipment and components of such equipment based on field inspection and operational data. 
     BACKGROUND 
     Various industrial processes use equipment that is subject to wear and degradation during its operation. Such wear and degradation can occur to the equipment overall, such that the equipment, and its constituent components, wear out or experience an end-of-run; or can occur individually to certain components. 
     These individual components can experience different levels and/or rates of wear depending on their role and usage within the equipment&#39;s operation. Industrial equipment and, where applicable, individual components, are therefore often inspected for wear, damage, or failure, and are typically replaced based on a periodic schedule. When certain equipment includes multiple components that experience wear, if the operation of the equipment requires downtime to inspect and replace a component, multiple components may be replaced at the same scheduled time, whether or not they need to be replaced. Replacing equipment or components prior to wear out points can lead to wasteful use of resources, while running past these points can increase the risk of failure leading to unplanned outages and/or safety incidents. 
     SUMMARY 
     The presently described system uses operational data for specific equipment, to build and train a model that can be used, along with ongoing operational data and inspection data acquired in the field, to generate end of run predictions for the equipment and/or components thereof. 
     In one aspect, there is provided a computer-implemented method for monitoring equipment, comprising: obtaining a trained model for an item, the item comprising equipment or a component of the equipment, the model having been trained using historical operational data of the type of equipment, and historical wear data acquired by inspecting the type of equipment and/or the type of component; using the trained model to generate an end-of-run prediction for the item using current or post service field inspection data for the item; analyzing the end-of-run prediction to determine a maintenance recommendation; and generating an output based on the prediction. 
     In another aspect, there are provided computer readable media for performing the method. 
     In another aspect, there is provided an equipment monitoring system, comprising: one or more processors; and memory, the memory storing computer executable instructions that, when executed by the one or more processors, cause the system to: obtain a trained model for an item, the item comprising equipment or a component of the equipment, the model having been trained using historical operational data of the type of equipment, and historical wear data acquired by inspecting the type of equipment and/or the type of component; use the trained model to generate an end-of-run prediction for the item using current or post service field inspection data for the item; analyze the end-of-run prediction to determine a maintenance recommendation; and generate an output based on the prediction. 
     In an implementation, the end-of-run prediction can further consider current operational data associated with the item. 
     In an implementation, the maintenance recommendation can include a replacement recommendation. 
     In an implementation, the maintenance recommendation can include a reuse or continued use recommendation. 
     In an implementation, the current field inspection data can be received from a mobile field application utilized at a site comprising the item. The current field inspection data can include at least one measurement indicative of wear of the item. 
     In an implementation, the current field inspection data and current operational data can be used to update the trained model. 
     In an implementation, the trained model can be one of a plurality of trained models, each trained model being associated with a different type of equipment or a different type of component. 
     In an implementation, the equipment can include a slurry pump. The item can include at least one component of the slurry pump. The at least one component can include a casing, a suction liner, an impeller, and/or a hub liner. 
     In an implementation, the operational data can include one or more physical properties of the equipment and/or a medium interacting with the equipment. The operational data can include cumulative hours, cumulative solids, a speed and/or a head the pump is creating, a percent Best Efficiency Point (BEP) flow, and/or any one or more of: i) a size distribution, ii) an amount of any solids that are being pumped, iii) a density of the slurry, and iv) where the pump operates. 
     In an implementation, the output based on the prediction can include a notification. A first notification can be provided to a first user device indicative of new data being provided from the site, and a second notification can be provided by the first user device to a second user device and is indicative of a recommendation based on the prediction. The second user device can be associated with a maintenance system or maintenance coordinator. 
     In an implementation, the output based on the prediction can include an alert. The alert can be indicative of a predicted end-of-run for the item being within a threshold amount of time for that type of item. The alert can be sent to a site supervisor, a maintenance system, or a maintenance coordinator. The alert can include a list of a plurality of items that are within the threshold. 
     In an implementation, the output based on the prediction can be provided at least in part by a mobile field application. 
     In an implementation, the output based on the prediction can be viewable in a user interface provided by a computing device. The computing device can be connected to an enterprise system. The user interface can provide wear data and operational data for a plurality of items. The user interface can provide an area view comprising data for a plurality of items. The user interface can provide a parts view comprising data for a plurality of components of the equipment. 
     Advantages of the system include the ability to accurately project into the future and predict and plan for end of run for components and/or equipment generally, as well as the ability to adapt and change predictions and maintenance planning based on ongoing operational data and field-inspected data. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Embodiments will now be described with reference to the appended drawings wherein: 
         FIG.  1    is a schematic diagram of an equipment monitoring and analysis system. 
         FIG.  2    illustrates an example of slurry pump comprising a number of components that experience wear during operation of the slurry pump. 
         FIG.  3    is an exploded view of a slurry pump illustrating components that can experience wear during operation of the slurry pump. 
         FIG.  4    is a schematic diagram of workflows among elements of the equipment monitoring and analysis system. 
         FIG.  5    is a schematic block diagram of an equipment management engine used in the system shown in  FIGS.  1  and  4   . 
         FIG.  6    is a graph illustrating a head ratio of a slurry pump over time. 
         FIG.  7   a    is a flow chart illustrating a process for training and utilizing a model for predicting end of run for equipment and/or components thereof. 
         FIG.  7   b    is a flow chart illustrating replacement/reuse recommendation generation. 
         FIG.  7   c    illustrates a new inspection data notification. 
         FIG.  7   d    illustrates a recommendation notification. 
         FIG.  7   e    is a flow chart illustrating alert generation. 
         FIG.  7   f    illustrates a summary list of components within an end-of-run threshold. 
         FIGS.  8 ,  9 ,  10 ,  11 ,  12 ,  13 ,  14  and  15    illustrate user interfaces for a mobile field app. 
         FIG.  16    is a user interface for a desktop tool, providing an area overview. 
         FIG.  17    is a user interface for the desktop tool, providing a component overview. 
     
    
    
     DETAILED DESCRIPTION 
     A system is provided that uses operational data for specific equipment, to build and train a model that can be used, along with ongoing operational data and inspection data acquired in the field, to generate end of run predictions for the equipment and/or components thereof. 
     The system can be used with any equipment subject to wear during its operation, including equipment that is periodically inspected and includes parts or components that are replaced either when needed or according to a schedule. For example, end-of-run can be predicted, and this information utilized as described herein, for equipment such as slurry pumps, control valves, slurry spools, piping, and ore preparation equipment used in hydrocarbon recovery and processing operations. In the case of slurry pumps, for example, main components such as the casing, impeller, suction liner, and hub liner typically wear out at different rates. Conventionally, all of the components are replaced at once, or per predefined maintenance schedules (i.e., planned maintenance), even if some have not reached end-of-run. Since parts for this and other types of equipment can be significantly expensive, extending the service time of the components can result in significant economic advantages and reduce wasteful use of resources. 
     While described in the context of hydrocarbon recovery and processing-related operations, the system described herein can be adapted and configured to monitor and predict end-of-run for various other industrial equipment and/or components of such equipment, in industries such as manufacturing, utilities, etc. 
     As described in greater detail below, wear data can be fed into a process algorithm that trains a model to predict end-of-run for each component. Each component typically includes its own relevant parameters that indicate wear. For example, for slurry pumps, the thickness and inside diameter (ID) of the suction liner, the vane length and eye ID for the impeller, and the sidewall thickness of the casing, can indicate wear. This data can be obtained from inspections and entered via a mobile app in the field, which is used along with the trained model, to predict and adjust the end-of-run for the components. The model can also account for ongoing operational data for the particular component (or equipment more generally). This can include any suitable physical properties of the equipment and/or a medium interacting with the equipment. For example, in the case of slurry pumps, in addition to cumulative hours; cumulative solids (tonnage), the speed and the head the pump is creating, the percent Best Efficiency Point (BEP) flow (i.e., how far away from this is the pump operating), the size distribution and amount of the solids (if any) that are being pumped and the density of the slurry, and where the pump operates, among other things, can affect the end-of-run and can be incorporated in the trained model and used to predict or adjust a current prediction for end-of-run as these parameters change over time. 
     A user interface on the mobile app as well as a desktop tool or application can be used to display information such as the estimated end-of-run and how much of the run life of the component has been “used” up to the current date. 
     Referring now to the figures,  FIG.  1    illustrates an equipment monitoring and analysis system  10  (hereinafter also referred to as the “system  10 ”). The system  10  includes an enterprise system  12  representing any computing platform and networked infrastructure used by an organization (e.g., via a computer  13  or computing station as shown) to monitor, communicate with and, optionally, automatically control equipment in one or more facilities. The enterprise system  12  includes at least one core database  18  that can be hosted on one or more servers within the enterprise system  12  which is configured to connect to one or more electronic networks  22 , an equipment management engine  14 , and a maintenance system  16  that can be used by maintenance personnel and/or administrators to schedule and deploy maintenance equipment, personnel, and any materials required to perform a maintenance operation associated with certain equipment, systems, plants, or facilities. For example, the maintenance system  16  can be instructed to replace or repair equipment  32  at various sites  30  within an operation or otherwise related to or associate with the enterprise system  12 . The enterprise system  12  also includes or has access to a data lake  20 , which can include a database or datastore for storing and consuming historical data, among other datasets such as ongoing operational data (that becomes historical data), enterprise data, device data, application data, etc. 
     The network  22  shown in  FIG.  1    is an electronic network  22  such as a wired and/or a wireless communication system, for example, an existing enterprise communication infrastructure or purpose built network for the system  10 . The electronic network  22  can include a communications network such as a telephone network, cellular, and/or data communication network to connect different types of communication devices. For example, the network  22  may include a private or public switched telephone network (PSTN), mobile network (e.g., code division multiple access (CDMA) network, global system for mobile communications (GSM) network, and/or any 3G, 4G, or 5G wireless carrier network, etc.), WiFi or other similar wireless network, and a private and/or public wide area network (e.g., the Internet). 
     The electronic network  22  in this example configuration provides connectivity with and/or into various sites, when to personnel or computing devices at such sites, or by being connected to instruments or computing devices within the sites  30 . In this example, the network  22  provides connectivity into/with three exemplary sites  30 , namely Site 1, Site 2 and Site N. While three sites  30  are shown in  FIG.  1   , it can be appreciated that any number of sites  30  can be within an organization or enterprise and be connected to the network  22  or otherwise be controlled by the organization associated with the enterprise system  12  (i.e., even if not connected to the network  22 ). The sites  30  are shown for illustrative purposes only to demonstrate potential connectivity of the system  10 , and the system  10  can be configured to be connected to any one or more of these sites  30  in any configuration that suits a particular application. For example, the enterprise system  12  can be connected to multiple industrial processes at multiple sites within an organization. 
     To illustrate such configurations, Site 1 shown in  FIG.  1    includes a piece of equipment  32  that includes at least one part  33  that may be repaired or, in the examples below, replaced. The part  33  is also capable of being examined or inspected by manual inspection, automatic (sensor-based) inspection, or both. In this example, an inspector  36  having a mobile device  34  such as a smartphone, tablet, or other computing device, can access the equipment  32  to inspect the equipment  32  and/or at least one component  33  such that manual inspection data can be entered into the system  10  via the mobile device  34 , as discussed in greater detail below. While the example shown in  FIG.  1    illustrates entry of inspection data via the mobile device  34  on-site, it can be appreciated that inspection data collected manually can also be entered into the system  10  via a desktop tool  15  within the enterprise system, within a computer  13  located within the enterprise system or on-site, or via any other suitable and available computing device that is connectable to the system  10 . Furthermore, it can be appreciated that inspection data can also be loaded from files generated by certain measurements including laser scans. Inspection data can also be accessed with Application Programming Interfaces (APIs). 
     Site 2 in this example is shown to illustrate that a site  30  can include multiple pieces of equipment  32 , each having parts  33  that experience wear and can be inspected to create manual inspection data. Site 2 in this example also includes multiple inspectors  36 , each having a personal mobile device  34  to permit inspection data to be entered. 
     Site N in this example is shown to illustrate that various pieces of equipment  32   a ,  32   b ,  32   c  can be interconnected in a sub-system such as a train of devices that are tasked with performing a particular operation in unison or otherwise collectively. An inspector  36  can also access and inspect such equipment (E)  32   a ,  32   b ,  32   c  and enter inspection data via a mobile device  34 . The equipment  32   a ,  32   b ,  32   c  can also include components  33  that experience wear and can be inspected. Site N also illustrates a control system  38 , which can be used to control one or more aspects of an industrial process such as one utilizing equipment  32   a ,  32   b ,  32   c . The control system  38  can be integrated into the system  10 , e.g., if any control operations can be affected by determinations, instructions, reports or other data generated by the system  10 , e.g., by the equipment management engine  14 , maintenance system  16 , or both. For example, an industrial process can include a single or multiple digital control systems (DCSs)  38  to operate that process. Such control systems  38  can be integrated with operational inputs or control parameters of the equipment  32   a ,  32   b ,  32   c . The control systems  38  can also be configured to be integrated with measurement instruments or sensors to gather data to be added to the data lake  20 . The control system  38  shown in  FIG.  1    can be used, for example, to automatically shut down equipment  32  (e.g., when a wear condition is detected), or to provide a local alarm or alert to on-site personnel. The control system  38  can also represent, or be otherwise provided by the maintenance system  16  directly to control operations on-site and/or with the equipment  32  or components  33  directly and thus the control system  38  shown in  FIG.  1    can be symbolic of any control imparted by the system  10 , on-site. As shown in  FIG.  1   , the data lake  20  can be populated using both data gathered at each site as well as from other sources  24 , such as data historians, third party sources of ambient conditions (e.g., ambient temperature), meta data (e.g., mapping of tags to model variables), economic data (e.g., maintenance costs) etc. As shown using a dashed line, the data lake  20  can optionally be accessible via the network  22  directly or may require access via the enterprise system  12 . 
     The computer  13  coupled to the enterprise system  12  as shown in  FIG.  1    represents any device utilized by personnel such as reliability, performance or other engineers and operators, as well as maintenance personnel (who may also have access via the maintenance system  16 ). The computer  13  can be used to access the desktop tool  15  provided by the enterprise system  12  to leverage and consume data generated by the equipment management engine  14  as described further below. 
     Some equipment  32  used in heavy industrial applications such as hydrocarbon extraction, processing and refinement can be subjected to the handling of abrasive fluids such as slurries. Examples of such equipment  32  can include slurry pumps, piping liners and other pipeline components; and pressure vessels/tanks, valves and pumps, to name a few. The slurries handled by the aforementioned equipment  32  can be extremely abrasive, which leads to the erosion of the internal components of the equipment. Equipment  32  such as slurry pumps are often proactively replaced or serviced during routine downtime rather than waiting for the pumps to fail. That is, it is found that to avoid the disruption of a leakage, internal components of the equipment are often serviced or prematurely replaced, which can lead to additional cost as note above. The system  10  can be used to model operational data, including historical data over multiple runs using such equipment  32 , to train a model  90  (see  FIG.  5   ) that predicts end-of-run. Manually acquired inspection data, entered via the mobile devices  34  used by inspectors  36 , can be used with the trained model  90  to predict an end-of-run and adapt to changing conditions and inform maintenance scheduling and part replacement scheduling. 
       FIG.  2    illustrates a slurry pump  40 , which represents one of various types of equipment  32  that can be monitored by the system  10 . The slurry pump  40  includes a pump casing  42  that contains certain internal components (see  FIG.  3   ). The pump  40  is driven by a drive shaft  44  that is powered by a motor  46 , e.g., an electric drive motor. The motor  46  is mounted to the pump casing  42  and is itself can be mounted to other apparatus such as a frame (not shown) or surface such as a floor. The pump casing  42  has a suction cover  48  that includes or otherwise defines a pump or suction inlet  50 . The pump casing  42  also includes a pump or discharge outlet  52 . 
     Referring also to  FIG.  3   , the pump casing  42  is attached to the motor  46  via a stuffing box cover  54 . The casing  42  contains an impeller  56  that rotates within a chamber  58  defined by the casing  42 . The casing  42  also contains a suction cover liner or “suction liner”  60 . The suction liner  60  is thus also an internal component of the pump  40 . The suction liner  60 , rather than hold pressure, serves as a “front-line” degradation component. The suction liner  60  is typically made of a degradation-resistant material such as chrome white iron, rubber, polyurethane or hardened tungsten steel. The suction liner  60  is meant to be the first component to fail in the pump  40  and when it begins to degrade (e.g., erode, corrode, wear, thin or be otherwise damaged), and indicate impending failure, permits leakage through weep holes (not shown) in the suction cover  48 . The suction liner  60  is therefore typically changed periodically as a preventative measure, e.g., during a preventative maintenance operation. The components shown in  FIG.  3    therefore represent examples of parts  33  of equipment  32  (i.e., slurry pump  40  in this example) that can be monitored by the system  10 . 
     While certain examples provided herein refer to degradation monitoring of slurry pumps, it can be appreciated that the principles discussed herein can be adapted and applied to other types of equipment  32  having at least one internal component  33 . 
       FIG.  4    illustrates an example of a workflow that can be implemented using the system  10 . A mobile field app  62  is provided that can be loaded on, and used by, the mobile device  34  of an inspector  36  to enter manually acquired inspection data in the field, e.g., by visually inspecting and/or measurement components  33  of the equipment  32  at a site  30 . The data (e.g., measurements, inspection dates, replacement dates, etc.) entered via the mobile field app  62  is sent to the core database  18 . The core database  18  can also communicate with the mobile field app  62  to provide current data for the particular equipment  32  or part  33  of interest, such as the cumulative hours, tonnage, tag data, etc. The mobile field app  62  can also communicate with a non-tabular data storage  64  to upload photos of equipment and/or the inspection process. The storage  64  is provided in this example implementation to separately store photos to facilitate photo sharing amongst personnel. 
     The maintenance system  16  can be coupled to the core database  18  to provide details of work orders, planned maintenance dates, replacement schedules, etc., which can then be used to populate information in the mobile field app  62  as well as the desktop tool  15 . The maintenance system  16  can also be coupled to the mobile field app  62  directly to enable an inspector  36  in the field to provide recommendations for prolonging a part replacement according to the inspection data. 
     The desktop tool  15  is coupled to the core database  18  to obtain data for visualization and monitoring by personnel not necessarily in the field. Such data can include time remaining, cumulative hours/tonnage, detailed model outputs, graphical data, etc. The desktop tool  15  therefore can provide additional processing and visualization power to an analyst or operator when compared to the mobile field app  62 , used for visualization and inspection data entry. 
       FIG.  4    also illustrates the equipment management engine  14 , which can be implemented using a machine learning platform such as the cloud-based Azure® platform. The engine  14  can receive tag data, flow, speed and other operational data from the data lake  20 , and can provide model results and tag data to the core database  18 . The engine  14  can also receive model configurations, user inputs, and maintenance data via its connection to the core database  18 . 
     As shown in  FIG.  4   , the equipment management engine  14  provides a platform, system, or device that can be configured to provide or otherwise incorporate or utilize a machine learning engine  66 , an end-of-run (EOR) prediction engine  68 , and a maintenance analyzer  70 , details of which are described below. 
     Referring now to  FIG.  5   , an example of a configuration for the equipment management engine  14  is shown. The equipment management engine  14  includes a communications module  80  configured to communicate via a direct connection or by way of an indirect connection via the network  22 , with the maintenance system  16 . The communications module  80  can also be used by the equipment management engine  14  to communicate with entities, systems and devices external to the enterprise system  12  as illustrated in  FIG.  1   . The equipment management engine  14  also includes one or more field data collection interfaces  82  to enable the equipment management engine  14  to communicate via the network  22  with equipment  32 , parts  33 , and mobile field apps  62 , as well as devices, systems, sensors, and other entities to obtain data from a site  30 , equipment  32 , part  33 , control system  38  or other sources or locations within the wider system  10 . As shown in  FIG.  5   , this can include data connections with the network  22  as well as other interfaces to enable direct entry of data and/or communications, e.g., via computers  13  within the enterprise system  12 . The field data collection interface(s)  82  are also configured to populate the core database  18  with data to be used in determining end of run predictions and maintenance scheduling as herein described. The core database  18  can also be updated with data that resides in the data lake  20 . In this example, the field data collection interface(s)  82  can collect, receive or otherwise obtain operational field data  84  (e.g., sensor data, instrument data, manual inputs from a plant, apparatus or process, etc.), as well as field (inspection) data  85  entered via the mobile field app(s)  62 . With respect to operational field data  84 , it should be noted that there are sensor technology improvements such as mounted Ultrasonic Testing (UT) probes that allow for continuous (or high frequency) wear monitoring (e.g. casing and suction liners) that can serve to substantially increase the training data set. Another example is an ability to measure the impeller nose gap with UT from the suction liner (which may only be possible during certain operating ranges). Furthermore, it can be appreciated that the field (inspection) data  85  can also be obtained from post service inspections whereby measurements are made on components already taken out of service thereby avoiding downtime to conduct inspections. Field (inspection) data  85  may also refer to files (e.g., electronic outputs from laser scans) or vendor measurement databases. Furthermore, both field (inspection data)  85  and operational field data  84 , may include weight measurements taken as needed or fed from sensors (e.g. load cells). In other words, various data indicative of wear can be used, in addition to directly measuring the component  33  or equipment  32 . 
     It can be appreciated that the equipment management engine  14  can be implemented using a client device (e.g., computing device  13  shown in  FIG.  1   ) which includes one or more processors other data storage devices storing device data and application data (not shown), the processor(s) being configured to execute instructions that utilize the modules and components shown in  FIG.  5   , including the communications module  80  and field data collection interface(s)  82  by implementing communication protocols utilized by the particular configuration and/or application. That is, while not delineated in  FIG.  5   , the equipment management engine  14  includes at least one memory or memory device that can include a tangible and non-transitory computer-readable medium having stored therein computer programs, sets of instructions, code, or data to be executed by a processor. It can be appreciated that any of the modules and applications shown in  FIG.  5    may also be hosted externally and be available to the equipment management engine  14 , e.g., via the communications module  80  or field data collection interface(s)  82 . The device data, can include, without limitation, an IP address or a MAC address that uniquely identifies client device  13  within the system  10 . The application data, can include, without limitation, login credentials, user preferences, cryptographic data (e.g., cryptographic keys), etc. 
     Other modules not shown in  FIG.  5    that can also be utilized by the equipment management engine  14  and/or client device  13  configured to implement same include, without limitation, a display module for rendering GUIs and other visual outputs on a display device such as a display screen, and an input module for processing user or other inputs received at the client device  13 , e.g., via a touchscreen, input button, transceiver, microphone, keyboard, etc.; standard or customized applications or “apps”, and a web browser application for accessing Internet-based content, e.g., via a mobile or traditional website. 
     To utilize data available in the core database  18  and to perform statistical modelling, the equipment management engine  14  can include various modules as shown in  FIG.  5    that are arranged and configured to process and analyze data according to both engineering (i.e., first) principles and using advanced data-driven analytics using machine learning and/or other advanced automation algorithms. In this example, the equipment management  14  includes a preprocessing module  86  to prepare, transform, and clean the historical data obtained from the data lake  20  and manual inspection data  85  as well as other operational field data  84  that populate the core database  18 . The preprocessing module  86  can be used to not only clean and normalize data, but also perform computations, such as to convert raw data into key performance indicators (KPIs) or other useful outputs that can be used by the machine learning engine  66  in building, training and refining the model  90  as well as using same. An example of operational data  84  that can be processed in the preprocessing stage  86  is shown in  FIG.  6   , which is a graph  130  that illustrates head ratio data for a slurry pump  40  over time and illustrates a prediction or extrapolation that can be determined by the engine  14 . The prediction is made for different operational scenarios e.g., larger and smaller median particle size. It can be appreciated that the example shown in  FIG.  6    is purely illustrative of one of many operational parameters and/or physical properties of the equipment  32  being monitored that can be considered by the machine learning engine  66  in building and refining the trained model  90  for the corresponding equipment  32 . The machine learning engine  66  uses the preprocessing stage  86  outputs to generate one or more trained models  90  that can be used to perform a prediction using a prediction engine  68  to generate a prediction that can be used by a maintenance analyzer  70  (also referred to as the “analyzer  70 ” for brevity). The analyzer  70  can use a prediction generated by the prediction engine  68  to, for example, recommend an alternative replacement schedule for a part  33  according to a predicted end of run for that part  33  determined, in part, from the field data  85  entered during an inspection. The analyzer  70  can also use a prediction generated by the prediction engine  68  to determine an optimized maintenance or replacement schedule for a train or interconnected set of equipment  32   a ,  32   b ,  32   c  (e.g., as shown at Site N in  FIG.  1   ). 
     The analyzer  70  can generate instructions  92  or reports  94  that can be communicated to a site  30  via the network  22  or can be provided to the maintenance system  16 . As shown in  FIGS.  7   a  through  7   f   , and described more fully below, the analyzer  70  can also be involved in generating alerts and notifications that are selectively sent to key personnel associated with the system  10 . It can be appreciated that the maintenance system  16  can also be further integrated with the equipment management engine  14 , e.g., to include the analyzer  70  or the entirety of the equipment management engine  14  in other configurations. The instructions  92  can include commands for control systems  38  to implement automated changes or can include instructional information for an operator for manual operational changes or to automatically shut down an apparatus. 
     As illustrated in  FIG.  5   , the machine learning engine  66  can be used to not only generate the trained model  90  based on historical data and currently obtained data, but also to feed current data (including field data  85 ) to the prediction engine  68  to generate a current prediction for the analyzer  70 . The historical data that is used to train the model  90  can be updated with the most recent data every time that the model  90  runs. That is, the model  90  can be configured to always use an up to date training dataset. It can be appreciated that the model  90  being trained is typically a wear rate. Field inspection data  85  can be used to quantify responses (wear) while the cumulative solids and/or time online could be a more significant variable. At the minimum, with two field measurements and timestamps, wear over time can be calculated based purely on field inspection data. As noted elsewhere, wear is also dependent on other factors. These other factors are typically ascertained from operational field data  84 . In a simple example, the model  90  can then be trained with field measurements ( 85 , responses), time online ( 85 ,  84 , variable), particle size ( 84 , variable), pump speed ( 84 , variable) etc. This example would be a multivariate model  90  using both field inspection data  85  and operational data  84  from sensors and or laboratory analyses. 
     In the configuration shown in  FIG.  5   , the preprocessing module  86  can be configured to compute wear variables as an indicator of the predicted end-of-run for a slurry pump  40  as one example of equipment  32  that can be monitored. In such an example, the wear variables can be used along with operational values such as cumulative hours; cumulative tonnage, the speed/head the pump is creating, the percent Best Efficiency Point (BEP) flow (i.e., how far away from this is the pump operating), the size and amount of the solids (if any) that are being pumped and the density of the slurry, and where the pump operates, among other things, to predict the end-of-run for a part  33 , based on current conditions. It can be appreciated that by using the engine  14 , an operator or engineer can, for example, determine whether an end of run for a component  33  or equipment  32  can be extended. For example, site engineers can see how much they can stretch out a run on a component  33  to avoid unnecessarily replacing the component  33  too early. 
     It can also be appreciated that outcomes from the prediction engine  68 , can be used as inputs to the process simulation(s)  88 , thereby enabling simulations based on predicted wear behavior. The outcome from these simulations can be issued as report(s)  94 , or/and as additional inputs to the maintenance analyzer  70 . Information exchanged between these steps could be automated or entered by users. 
       FIG.  7   a    is a flow chart illustrating a process for training and utilizing a model  90  for predicting end of run for equipment  32  and/or components  33  thereof. At block  100 , the machine learning engine  66  obtains operational and wear data related to a component  33  or equipment  32  for which the model  90  is being trained. Initially, historical data is used to train an initial model  90 , e.g., by capturing multiple runs for various equipment over time, to develop model parameters that can be used to infer and predict based on ongoing data, including measurements obtained from the field app  62 . At block  102 , the machine learning engine  66  generates a model  90  for that equipment type and/or component type. 
     On an ongoing basis, the machine learning engine  66  can refine and update the trained model  90  as new data is acquired, e.g., by iterating through operations  100  and  102 . At block  106 , field data  85  is acquired via the field app  62 . Operational data  84  is also provided via sensors or other inputs available to the system  10 . 
     At block  108  the prediction engine  68  obtains the trained model  90  for the equipment  32  or component  33  that relates to the operational data  84  and the field data  85  and uses that data  84 ,  85  and the trained model  90  at block  110  to generate an end-of-run prediction for the particular piece of equipment  32  and/or a component  33  thereof. 
     This end-of-run prediction can be a new prediction or a refinement or verification of an existing end-of-run value and can propagate through the system such that, for example, the analyzer  70  can analyze the prediction for maintenance and/or scheduling considerations at block  112 . The system  10  can then generate one or more outputs at block  114 , which can include inputs sent to the maintenance system  16 , control instructions, or alerts/notifications at block  116 . The alerts/notifications at block  116  can include emails or other electronic messages, chat messages, project management updates, etc.  FIG.  7   b    illustrates a notification and recommendation message flow at block  116 , in this example, denoted by  116   a . At block  200 , the system  10  obtains field data from the mobile field app  62 . This may be performed by an on-site inspector  36  such as a millwright or other personnel. At block  202 , the site lead is notified that such field data has been collected by the on-site personnel. At block  204 , the site lead is then responsible for using that data to make a recommendation for replacing or reusing the component that has been measured. This can be done using the mobile field app  62  as illustrated further below. The system  10  then sends the replacement/reuse recommendations to a maintenance coordinator, e.g., by sending a notification to the maintenance system  16 . The maintenance coordinator, who in this example is ultimately responsible for making the decision, can agree with or ignore the recommendations by taking into account various other factors. However, the replacement/reuse recommendations provide additional insights from others within the system  10  to enable the maintenance coordinator to make more informed decisions. 
       FIG.  7   c    illustrates a message or notification  210  that can be provided to the site lead to notify them of when the new inspection data has been entered via the mobile field app  62  and is available for review. The notification can be provided as a push notification, text message, email, etc. A link  212  to the inspection app  62  and inspection data can be provided to provide ease of navigation to the data for the site lead.  FIG.  7   d    illustrates a summary  214  of the inspection that can be viewed by the site lead on the mobile field app  62  or desktop tool  15 . In this example, the summary  214  includes a photo link  216  to allow the site lead to drill down into the inspection by obtaining photos taken during the inspection. 
       FIG.  7   e    illustrates an alert message flow as part of  116  in  FIG.  7   a   , denote  116   b  in  FIG.  7   e   . At block  300  the system  10  obtains outputs from an analysis of inspection data, operational data, or both. In this example, at block  302 , the system  10  determines if the predicted end-of-run is within a threshold number of hours for a given part. This is to proactively detect components  33  that are worn within a particular tolerance or setpoint identified by the threshold and to then generate an email (or other message) at block  304  that is sent to the site  30  with a list of parts with low life remaining at the agreed upon interval. In this way, an intervention such as a part replacement or secondary inspection can be made. Such a list is shown by way of example in  FIG.  7     f.    
       FIGS.  8  through  15    illustrate example user interfaces provided by the mobile field app  62  to enable inspectors  36  and other personnel to enter and view field data with respect to equipment  32  and components  33  at various sites  30  within an organization. Referring first to  FIG.  8   , a selection page is shown which enables the user to select a site  30 , e.g., via a drop-down menu  150  as shown. Once a site  30  is selected, the user can select an Inspection and Current Status option  152  or a Recommend Replacements option  154 . The Current Status option  152  enables the user to view existing, or add a new, inspection or to obtain status information. By selecting option  152 , the view shown in  FIG.  9    can be displayed. This view enables the user to select an area of the site  30  using drop-down menu  156 , to select a train (of equipment  30 ) using drop-down menu  158 , and to select an asset using drop-down menu  160 . A current status option  162  can be selected to view status details for that asset. Previous inspections can be viewed by selecting a previous inspections option  164 , and a new inspection can be entered by selecting a new inspection option  166 . 
       FIG.  10    illustrates an inspection page that can be displayed by selecting the new inspection option  164  in  FIG.  9   . In this screen, a Suction Liner tab  170  is selected, which represents a component  33  of a slurry pump  40  in this example. If the inspector  34  is entering inspection data for this suction liner, a number of fields and selections  172  can be made. This includes an inspection date, contextual options (break in work, replaced, adjusted, etc.), and measurement entry boxes (e.g., for thickness and Eye ID measurements in this example). A finish and review option  174  can be selected to enter the data. 
       FIG.  11    illustrates a current status screen, which can be opened by selecting the option  162  shown in  FIG.  9   . Here, predictive analytics model deviation values can be colour-coded to indicate operational health. This screen can be used in the field to allow an inspector  36  (or other personnel) to quickly and conveniently get a snapshot of the current health of that component  33  or equipment  32 . In  FIG.  11   , the overall equipment health tab is shown and similar data can be shown for each component  33  via selection of additional tabs. The current status information  180  includes pump status, an indication of the part with the shortest end-of-run (e.g., impeller in this snapshot), shortest remaining hours, and earliest projected end-of-run date. A current head ratio graph can also be shown, among other data not shown in  FIG.  11    for ease of illustration. 
       FIG.  12    illustrates a current status tab  190  for a casing component  33 . These tabs can be used to drill down into individual components  33  and determine component details  192 , such as current cumulative hours, the predicted end-of-run at that time, critical location and inspection history/replacement details, as well as options to view measurement data (graphs) for certain characteristics such as the belly thickness of the casing in this example. It can be seen that a current predicted thickness is shown as well as a worn value that provides a glimpse of how much wear life is left for this component  33 . 
       FIG.  13    illustrates a previous inspection screen that can be loaded by selecting the previous inspections option  164  in  FIG.  9   . Here, details of a past inspection can be shown, including the date, measured value(s), contextual details, comments, and photos. It can be appreciated that the screen shown in  FIG.  13    can also represent a current inspection in progress. The new photo button  194  is provided to enable additional photos to be added in such a case. 
     Referring again to  FIG.  8   , a recommend replacements option  154  can be selected to allow an inspector  36  or other personnel to make recommendations, which can be particularly convenient for field workers that have access to the mobile field app  62 . A recommendation screen is shown in  FIGS.  14  and  15   , which allows a user to scroll through assets (e.g., equipment  32  and/or components  33 ) at a site  30  and select recommendations such as “reuse” or “replace” or “not applicable” and send recommendations to a coordinator by selecting option  196 .  FIG.  15    illustrates an equipment screen that allows the user to drill down into the components  33  of a piece of equipment  32  to select or de-select the recommendations for specific components  33 . Comments can be added and editing features can also be provided. 
     Referring now to  FIG.  16   , a user interface provided by the desktop tool  15  is shown. This user interface provides an area overview to provide remaining time and other variables for an area broken down by equipment  32  or line of equipment  32 .  FIG.  17    illustrates a similar view but for a specific part or component  33 . Here, additional details for a component can be viewed and data entry made to perform a more detailed and comprehensive assessment, which may or may not occur in the field. For example, the desktop tool  15  can be used by engineers and other personnel within the enterprise system  12  to conduct in-depth analyses and model inspection, model creation, report generation, escalation/alerts, etc. Specifically, the desktop tool  15  facilitates the convenient assessment of multiple equipment  32  together (e.g., pumps in a train at site  30 N in  FIG.  1   ). Different equipment trains can also be compared to each other to ascertain if certain conditions are causing higher or lower wear relative to others. The desktop tool  15  also allow features of linked tables such that a lot of different information that facilitates in depth analysis. The field application can be limited in screen space and limits the user to view each equipment  32  or component  33  at a time, which is less convenient and more time consuming when trying to assess the overall state of an operating unit/facility. 
     For simplicity and clarity of illustration, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements. In addition, numerous specific details are set forth in order to provide a thorough understanding of the examples described herein. However, it will be understood by those of ordinary skill in the art that the examples described herein may be practiced without these specific details. In other instances, well-known methods, procedures and components have not been described in detail so as not to obscure the examples described herein. Also, the description is not to be considered as limiting the scope of the examples described herein. 
     It will be appreciated that the examples and corresponding diagrams used herein are for illustrative purposes only. Different configurations and terminology can be used without departing from the principles expressed herein. For instance, components and modules can be added, deleted, modified, or arranged with differing connections without departing from these principles. 
     It will also be appreciated that any module or component exemplified herein that executes instructions may include or otherwise have access to computer readable media such as storage media, computer storage media, or data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Computer storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. Examples of computer storage media include RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory computer readable medium which can be used to store the desired information and which can be accessed by an application, module, or both. Any such computer storage media may be part of the enterprise system  12 , computing device  13 , mobile device  34 , equipment  32 , control system  36 , network  22 , equipment management engine  14 , maintenance system  16 , or any component of or related thereto, etc., or accessible or connectable thereto. Any application or module herein described may be implemented using computer readable/executable instructions that may be stored or otherwise held by such computer readable media. 
     The steps or operations in the flow charts and diagrams described herein are provided by way of example. There may be many variations to these steps or operations without departing from the principles discussed above. For instance, the steps may be performed in a differing order, or steps may be added, deleted, or modified. 
     Although the above principles have been described with reference to certain specific examples, various modifications thereof will be apparent to those skilled in the art as having regard to the appended claims in view of the specification as a whole.