Flange Integrity Classification Using Artificial Intelligence

A computer-implemented method for flange integrity classification using artificial intelligence is described. The method includes obtaining images of a flange, wherein an image of the images is captured at a predetermined angle of image capture. The method includes classifying a condition of the flange using a trained machine learning model. Further, the method includes rendering an indication of the condition of the flange.

TECHNICAL FIELD

This disclosure relates generally to machine-learning based flange integrity classification.

BACKGROUND

Flanges include projecting collars that are physically coupled to seal a pressurized vessel or pipe. Multiple flanges are used in a pipeline. Standards applicable to flanges are promulgated by organizations such as the American Society of Mechanical Engineers (ASME) and American National Standards Institute (ANSI).

SUMMARY

An embodiment described herein provides a method for flange integrity classification using artificial intelligence. The method includes obtaining images of a flange, wherein the images are captured at a predetermined angle of image capture. The method includes classifying a condition of the flange using a trained machine learning model. The method also includes rendering an indication of the condition of the flange.

An embodiment described herein provides an apparatus comprising a non-transitory, computer readable, storage medium that stores instructions that, when executed by at least one processor, cause the at least one processor to perform operations. The operations include obtaining images of a flange, wherein the images are captured at a predetermined angle of image capture. The operations include classifying a condition of the flange using a trained machine learning model. The operations also include rendering an indication of the condition of the flange.

An embodiment described herein provides a system. The system comprises one or more memory modules and one or more hardware processors communicably coupled to the one or more memory modules. The one or more hardware processors is configured to execute instructions stored on the one or more memory models to perform operations. The operations include obtaining images of a flange, wherein the images are captured at a predetermined angle of image capture. The operations include classifying a condition of the flange using a trained machine learning model, wherein a machine learning model is trained to classify the condition of the flange using synthetic images. Additionally, the operations include rendering an indication of the condition of the flange.

DETAILED DESCRIPTION

Flanges enable connections between pipes of pipeline systems that transport various materials, such as oil, gas, water, and the like. Additionally, flanges can securely seal pressurized vessels that store various materials. A flange can include two flange collars bolted together using a set of bolts mated with corresponding nuts. A seal is created between the flange collars, enabling a strong joint or connection between two pipes. Ensuring the integrity of flanges is vital to maintaining a safe pipeline or pressurized vessel. In some cases, inspections of flanges in the field are conducted infrequently, since traditional inspections require that a trained operator travel to the pipeline and visually inspect the flanges. Additionally, once on-site, in traditional inspections the operator may find that flanges are located in difficult to view or hidden locations due to the design of the pipeline. Further, inexperienced operators are often unable to properly evaluate conditions of the flanges in traditional inspections. Accordingly, in traditional techniques operators require extensive, costly, and time consuming training in order to evaluate flanges.

Embodiments described herein enable flange integrity classification using artificial intelligence. In particular, the present techniques use enable an automated assessment of the integrity and health of one or more flanges. Using an industrial tablet pre-installed with a mobile application, a systematic and automated procedure to assess the integrity and health of flanges is provided. In examples, the application prompts an operator to visit specific flanges and capture images from specified angles. The application, using these images, will determine if any conditions are present using a trained machine learning model.

Traditional flange integrity classification is limited to detection of loosened bolts by analyzing a change in an angle of the bolt over time or acoustic inspection techniques. Other traditional techniques use Phase Array Ultrasonic Testing (PHAT), where a probe is focused and electronically swept to scan an area of interest, without moving the probe. The use of the probe consumes a relatively long period of time for inspection, and a trained operator is required to operate the probe and interpret the results. As a result, traditional techniques are limited to testing for loosened bolts or using ultrasonic testing. The present techniques detect a variety of flange conditions that can cause future leaks, and do not require a trained operator. Image capture is performed to obtain images of a flange from multiple angles. For example, an untrained person (e.g., without specialized knowledge) captures images of flanges from multiple angles as instructed by the mobile application. In some embodiments, the mobile application can used for training of operators. In examples, an electronic device (e.g., a robot, drone) captures images of flanges from multiple angles as instructed by the mobile application. A trained machine learning model takes the captured images as input, and outputs a predicted condition of the flanges.

FIG.1is an illustration of coupled flanges100. In some embodiments, the coupled flanges100are included in a pipeline. For example, the pipeline system can be a gathering system, transmission system, or and distribution system with multiple flanges forming joints or connections across pipes of the pipeline. The flanges are often under high pressure and can transport hazardous or flammable material. Accordingly, flanges in poor condition can create a dangerous environment.

In the example ofFIG.1, the coupled flanges100include a first coupled flange pair110, a second coupled flange pair130, and a third coupled flange pair150. The first coupled flange pair110includes a number of bolts112,114,116, and118. The bolts extend through bolt holes in collars120A and120B of the coupled flange pair110. A number of nuts122,124,126, and128receive the threaded end of the bolts112,114,116, and118. Tightening the nuts122,124,126, and128secures the collars120A and120B of the coupled flange pair110, creating a seal between pipe 101 and pipe 102.

In the example ofFIG.1, the second coupled flange pair130includes a number of bolts132,136, and138. The bolts extend through bolt holes in collars140A and140B of the coupled flange pair130. A number of nuts142,146, and148receive the threaded end of the bolts132,136, and138. The nuts142,146, and148are threaded onto respective bolts132,136, and138.

The coupled flange pair130exhibits a number of conditions. In examples, a condition refers to a state of a flange with regard to its appearance, quality, or working order. In examples, the condition is a normal, healthy condition wherein the flange is configured according to predetermined maintenance instructions and bolt tightening data (e.g., bolting patterns, torque and tensioning figures, procedures, techniques and recommended controlled bolting equipment). In some embodiments, a normal condition is determined based on a flange type and standards promulgated by organizations such as the American Society of Mechanical Engineers (ASME) and American National Standards Institute (ANSI). In some embodiments, the condition is poor or unhealthy, where the flange exhibits defects that reduces the integrity of the coupled flange. In examples, defects include short bolting, missing bolts, angular or parallel misalignment of the flange heads, missing screws, and the like. In some embodiments, a condition of the flange is critical. A flange in critical condition exhibits a severe flange abnormality that can cause immediate disruption to a system that includes the flange.

A short bolting defect is illustrated using bolt132and nut142. The short bolting defect is indicated by the bolt132being short and failing to extend fully through bolt holes of the collar140A and the collar140B. At bolt holes134and144, a missing bolt defect is illustrated. A missing bolt is indicated by bolt holes without bolts, and reduces the integrity of the coupled flanges. The collars140A and140B of the coupled flange pair130are misaligned as shown at reference number131. In particular, the collar140A and collar140B show a parallel misalignment, which is indicated by an offset131between a centerline135of the collar140A and a centerline133of the collar140B.

The third coupled flange pair150includes a number of bolts152,154,156, and158. The bolts extend through collars160A and160B of the third coupled flange pair150. A number of nuts162,164,166, and168receive the threaded end of the bolts152,154,156, and158. The nuts162,164,166, and168are threaded onto respective bolts152,154,156, and158. The collars160A and160B of the coupled flange pair150are misaligned as shown at reference number 151. In particular, the collar160A and collar160B show an angular misalignment, which is indicated by an angle 151 between a centerline 155 of the collar160A and a centerline 153 of the collar160B.

Other defects of a flange include, for example, scratches, gouges, pits, and dents. In examples, scratches are caused by contact with hard, abrasive materials, and can result from mishandling in transit or from the removal of protective coatings. In examples, gouges are created by a dull object dragging across the flange face, such as a screwdriver, flange. Gouges can be in transit from the fabrication plant to site, or during commissioning. Pits are small rounded areas of material loss, sometimes in groups and caused by corrosion. In examples, pits are created after the flanges are operational for a period of time. Similarly, dents can be caused during the installation and commissioning phases through impact with equipment such as cables, rigging and positioning of mating flanges.

For ease of illustration, the defects inFIG.1are shown as visually observed by the human eye. However, in some embodiments, defects associated with the coupled flanges are small and slight as to not be visible to the human eye. Additionally, a single flange or a flange pair can exhibit any number of defects. For example, a flange pair can exhibit missing bolts, short bolts, parallel misalignment, angular misalignment, scratches, gouges, pits, dents, and loose bolts either alone or in various combinations.

The present techniques include an intelligent system that detects faulty flanges that could eventually leak. Flanges that are not tightened well, have loosened bolts, missing bolts, or short bolting do not retain sufficient force to prevent inner liquid from leaking through the flanges, and are more susceptible to leaks. Moreover, flanges that feature angular or parallel misalignment have higher probability to exhibit leaks than perfectly aligned flanges. Hence, detecting these faults or anomalies in flanges as early as possible reduces the likelihood of leak incidents as well as maintenance costs associated with damaged flanges.

For ease of description, the flanges described herein are described generally with flange collars and a number of bolts. However, the flanges can be of many different types, such as Weld Neck Raised Face (WNRF), Socket Weld (SW), Slip-On Flange, Flat Faced (FF), Lap Joint, Ring Joint, Threaded Flange, Reducing Flange, Blind Flange, and the like. Face types of the flanges include, for example, flat face, raised face, ring joint face, tongue and groove, and male and female faces. Additionally, in examples, the flanges can include a number of finishes, such as serrated or smooth. Flange integrity classification of various types of flanges is enabled by the present techniques, as the basis of anomaly / defect identification is image analytics augmented with machine learning capabilities.

FIG.2shows a series of mobile application screenshots via displays202A,202B, and202C (collectively referred to as displays202) rendered at respective industrial tablets200A,200B, and200C (collectively referred to as industrial tablets200). For ease of description, the mobile application is described as executing on the industrial tablets200. However, the mobile application according to the present techniques can be executed on any electronic device, such as a tablet, laptop, desktop, commercial tablet, smartphone and the like. In some embodiments, the industrial tablets200include built in sensors that detect gas leaks, hazardous or flammable plumes, and the like. For ease of illustration, particular mobile application screenshots are illustrated. However, the mobile application according to the present techniques can have any number of screenshots corresponding to rendered prompts, information, and instructions described herein. Further, the mobile application is not limited to the visual display of prompts, information, and instructions. In examples, the mobile application can generate prompts, information, and instructions using output devices including but not limited to speakers, remote displays, light indicators (e.g., traffic lights). Further, the mobile application can output haptic feedback via the industrial tablet, such as vibrations, to guide an operator.

In some embodiments, the mobile application has the ability to measure a wide multitude of flange sizes in different locations and conditions. The mobile application can detect a flange in an image and, if present, defects with their respective locations on an image. In examples, a first industrial tablet200A includes a display202A. A prompt to instruct a user to capture images of a flange is rendered on the display202A. In some embodiments, the prompt includes instructions for image capture of a flange from predetermined angles or positions. In some embodiments, the predetermined angle of image capture refers to enabling panoramic views of a flange joint connection to ascertain maintenance job precision enabling the application to evaluate the flange joint and identify defects, if any. For example, identification of parallel / planar misalignment uses predetermined angles of image capture relative to the flange that captures a front view, side view and top view of the flange.

InFIG.2, a second industrial tablet200B includes a display202B. A map204is rendered on the display202B. The map204shows the flanges that operator needs to capture and the flanges that have already been captured for inspection. In the example ofFIG.2, locations206,208, and210that have been previously visited by the operator are marked using an item rendered on top of the map204. In some embodiments, the map204is a real world image of the system, with augmented reality elements superimposed on top of the image. In some embodiments, the map204is a digital map.

Step by step directions are provided to guide the operator towards a next flange location, positioned at location 212. The operator captures one or more images of the flange using an image capture mechanism (e.g., camera or camera sensor) of the industrial tablet. In the example ofFIG.2, the operator is guided to a next Flange A at Location X. The user can visit Flange A at Location X, and capture images of Flange A at Angle 1 and Angle 2 as prompted via display202A. In some embodiments, on-screen instructions are rendered for training purposes. For example, an operator in training is guided by instructions generated by the mobile application. In some embodiments, the mobile application prompts the operator for further advanced capturing to acquire supplementary images of the flange. For example, if a defect is detected, the operator is prompted to capture additional images. This will facilitate a faster troubleshooting process in the means of abnormality severity level detection. In some embodiments, the operator is prompted to take additional images when a first set of captured images is of poor quality, blurry, dimly lit, or unfocused.

In some embodiments, the industrial tablet includes a GPS sensor that uses GPS a location of the industrial tablet to determine the particular flange being captured. Location identification of flanges is enabled through a Geographic Information System (GIS) feature of assets (e.g., linear assets, in particular considering pipeline is a linear asset). GIS / Linear Reference System data is strengthened with coordinates (latitude, longitude) to enable accurate tracing and mapping of the break flanges, valves, associated pipe fittings etc. to enable expedited services in operation and maintenance. Reversibly, since the location of the each flange is known, the industrial tablet could determine its GPS location using the embedded GPS sensor and subsequently determine at which flange the tablet is located. This can be used to immediately identify which flange in the system the operator is capturing. In some embodiments, the industrial tablet includes a scan functionality to scan a quick response (QR) code permanently attached to the flange, which includes the flange’s unique identification number. QR code tags can be also equipped with radio frequency identification (RFID) tags for quicker identification of the flanges.

A third industrial tablet200C includes a display202C. An image of the flange is rendered with augmented reality elements superimposed atop of the flange A. As illustrated, nuts, washers, and bolts are shown with arrows as augmented reality elements 216. The present techniques use augmented reality to superimpose, in real time, detected defects on top of an image of the flange, along with other information. In examples, the other information is details about the flange j oint which includes flange size and rating (e.g. size in inch diameter with pressure rating in pounds; gasket type, flange type, process fluid in the subject along with characteristics like pressure /flow / temperature etc.). This mobile application can also be connected in real-time with Distributed Control System (DCS) and Document Management System (DMS), etc. to enable accurate and quick data capturing for the subject flange joint under consideration. In some embodiments, the augmented reality elements are an indication of the condition of the flange. In examples, a short bolting defect is rendered using augmented reality elements showing a short bolt that fails to extend fully through bolt holes of the flange collars. A missing bolt defect is rendered using augmented reality elements showing bolt holes without bolts, and a parallel misalignment is rendered using augmented reality elements showing an offset between a centerlines of the flange collars. Similarly, an angular misalignment is rendered using augmented reality elements by showing an angle between a centerlines of the flange collars.

In some embodiments, an indication of the condition of the flange is rendered at industrial tablets200using message that provides the operator with the type defect. For example, the text can be one or more of “short bolt,” “missing bolt,” “loose bolt,” “parallel misalignment,” or “angular misalignment.” In some embodiments, instructions to remedy the defect are provided to the operator. The industrial tablet renders a warning to operators when a severe flange abnormality is detected (e.g., missing screw or bolt, significant flange misalignment, missing gasket etc.)

To determine conditions of the flange, cameras of the industrial tablet (e.g., industrial tablets200) capture images of the flange and apply a deep leaning algorithm that classifies flanges based on their physical arrangement. The defects occur externally with respect to the flange, and can be detected visually using a vision sensor (e.g., camera). In some embodiments, a recursive neural network deep learning algorithm is trained using properly labeled images of flanges in healthy and poor condition, and the machine learning model is trained to classify flanges. In examples, the machine learning models according to the present techniques are integrated on industrial tablets to support maintenance personnel (e.g., operators) in performing flange surveys and detecting anomalies that are intuitively visible. The maintenance personnel will direct the tablet’s camera to flanges while the survey is performed, and the mobile application determines if the physical arrangement of the captured flanges meets predetermined specifications. In some embodiments, required specifications of the flanges are determined from a database of a Document Management System (DMS) of the subject operating facility. For example, a DMS of an operating facility is located on a centralized server. A clone of the database can be copied to a cloud server where other edge devices (tablets) can communicate. Additionally, partial copies of the database can also be stored locally on the tablet in case server is not reachable. The device hosting the mobile application is communicatively coupled to the DMS in online (Industrial WiFi) / offline mode to fetch the technical specifications.

The mobile application includes machine learning models trained to classify flange integrity. The machine learning model takes as input one or more images of flanges and outputs a classification of flange integrity. The classified flange integrity identifies conditions of the flange, including defects present at the flange. In some embodiments, the machine learning model is trained to classify flange integrity using images of the flanges as input, as described with respect toFIG.3. In some embodiments, the machine learning model is trained to classify flange integrity by segmenting images of flanges and inputting the image segments to train the machine learning model, as described with respect toFIG.4.

FIG.3is a block diagram of training a machine learning model for flange integrity classifications using artificial intelligence. Images of flanges with known defects are captured. The known defects are used to labels the images, and the labeled images are used to train the machine learning models at block304. In some embodiments, the machine learning models are trained using synthetic images as described with respect toFIG.5. The trained machine learning models output flange integrity classifications as indicated at block306. In examples, the flange integrity classifications include defects/abnormalities of the flange and a bounding box associated with each respective defect/abnormality. In some embodiments, the bounding box corresponds to a real-world location associated with the classified defect or abnormality. In some embodiments, the mobile application uses the classified defect and associated bounding box to superimpose augmented reality elements indicated by the defect onto an image of the captured flange in real time, as described with respect toFIG.2.

FIG.4is a block diagram of training a machine learning model for flange integrity classification in a two-part network. In the example ofFIG.4, the two-part network includes image segmentation and training a machine learning model to classify flanges. In some embodiments, image segmentation is performed using a convolutional neural network architecture for fast and precise segmentation of the images, and object detection is performed using real time object detection. For example, image segmentation is structured similar to a U-Net model, and the object detection of the abnormalities is modeled using a You Only Look Once (YOLO) network.

Images of flanges are captured, as illustrated at block402. In some embodiments, the machine learning models are trained using synthetic images as described with respect toFIG.5. At block404, the images are segmented. For example, for each flange image, the mobile application will first dissect and segment each pixel to a flange’s component, such as flange body, screw, and bolt. At block 406, the segmented images are illustrated. In some embodiments, the segmented images include sub-images that correspond to the flange body, screw, and bolt. The segmented images are labeled using the condition of the flange-sub image. For example, bolts and screws are labeled according to conditions, such as being tight, loose, or missing. The flange body is labeled according to conditions, such as faces being aligned or misaligned.

The segmented images are input to machine learning models 408 for training. A classifier is trained used to detect and locate abnormalities within the flange using the segmented images. The trained machine learning models output flange integrity classifications. In examples, the classification includes defects/abnormalities of the flange and a bounding box associated with the defects/abnormalities. In some embodiments, the bounding box corresponds to a real-world location associated with the classified condition, including any defect or abnormality. In some embodiments, the mobile application uses the classified condition, including the defect or abnormality, and associated bounding box to superimpose augmented reality elements onto an image of the captured flange in real time, as described with respect toFIG.2.

In examples, the machine learning models described with respect toFIGS.3and4are deep neural networks that require large labeled datasets in order to be properly trained to recognize patterns and generalize to new images not encountered previously. Images of flanges with labeled defects are scarce. Moreover, the images of flanges are typically manually labeled, which can be time consuming and tedious.FIG.5is an illustration of synthetic flanges500. The synthetic flanges are generated using a sim-2-real transfer in machine learning. For example, simulations are used to generate synthetic flange images for the purpose of training machine learning models. The simulated data represents a wide variety of healthy and unhealthy flanges, and is representative of the real-world data. Machine learning models are trained by using the synthetic images, and the flange integrity classifications generated by machine learning models trained using synthetic images generate classifications of flange integrity using real-world images.

Simulated data is used to avoid the aforementioned manual labeling and due to the lack of real images of faulty flanges. Machine learning models typically require datasets with at least 5,000 images (and sometimes reach the millions). The simulated data is generated using three-dimensional (3D) modeling software (e.g., Blender). Many parameters of the simulated environment and gauge are randomly sampled in both the faulty and normal state to create a sufficiently large dataset to use in training the machine learning model.

In some embodiments, the machine learning models are trained and then converted to reduce a size of the trained machine learning models. Tablets typically use different architectures than devices used to train machine learning models. In some cases, tablets are computationally weaker than devices used to train machine learning models. Therefore, in order to execute the trained machine learning model on a tablet, it is converted into a format compatible with the tablet’s architecture. These formats use datatypes that hold limited information. Moreover, some optimizations and trimming procedures are applied to the trained machine learning model to further reduce its size and inference time (e.g., time to process the image and provide an output). In examples, a machine learning library is used for training and inference of deep neural networks, while a lite version of the machine learning library executes the trained deep neural networks. The lite version of the machine learning library is executed on mobile or embedded devices. In some examples, the mobile or embedded devices have limited compute, memory, and power resources. In some embodiments, the Tensorflow is a library used in regular deep learning models on relatively complex devices while Tensorflow Lite is used for devices that have limited resources in terms of computation and memory.

In examples, the machine learning models are trained and then quantized to reduce a size of the trained machine learning models. In some embodiments, quantization constrains weights of the trained machine learning model to obtain a lightweight trained machine learning model that is operable using fewer compute resources when compared to the compute resources used to train the machine learning model. The mobile application is equipped with a dataset of all critical flanges in a given area, along with the computationally light version of the machine learning model for classification on the tablet. Accordingly, in some embodiments the lightweight trained machine learning model is deployed at an industrial tablet, such as industrial tablets200ofFIG.2. In some embodiments, the industrial tablet stores recorded data captured locally and transmits it to a server whenever a connection (e.g., data transmission on a network) is available.

The trained machine learning model classifies images of flanges captured by the industrial tablet. In some embodiments, the images of flanges captured by the industrial tablet are processed by the mobile application. In some embodiments, the mobile application identifies the type of flange. The flange can be identified based on a neck type, flange dimensions, etc. In some embodiments, the mobile application determines the specifications of the flange. Specifications of the flange include, for example, the number of bolts, neck spacing and vertical and horizontal misalignment. In some embodiments, the specifications are known and obtained from ASME 16.5 standards.

In some embodiments, the captured images are input to the trained machine learning models and the classified flange integrity output by the trained machine learning models is uploaded to a remote server or a database for further analysis and management. The database includes historical records of flange conditions and updated images of flanges with defects that can be reviewed and confirmed remotely by maintenance experts. In some embodiments, once faulty flanges are confirmed, a notification is transmitted to a corrective maintenance group to schedule maintenance jobs. In examples, the notification includes an identity, location, and determined condition (including any defects) of the flange.

In some embodiments, the images of flanges are captured for input to trained machine learning models located on a remote server. The industrial tablet can transmit the captured images wirelessly to the trained machine learning models installed on the server for analysis of the data collected. The tablet stores the images sequentially. In some embodiments, the images are transmitted from the tablet to a server where they are stored sequentially or in time series data and used for data analytics and modeling. Sequential or time-series data refers to readings that are stamped with the time of reading. For example, flange A had minor misalignment within acceptable tolerance on October 28, and the same flange had an even greater misalignment on November 25. Storing data in this fashion enables changes overtime to be monitored. Additionally, future conditions are predicted using the sequential data to determine if an intervention is needed.

In examples, in data analytics the server collects time-series statistical information about the health status of all the flanges in the plant. A server manager analyzes the flange defects and creates correlation maps between a defective flange and other defective flanges in the plant. In some embodiments, correlation relationships are created for defective flanges and other alarms/flags in the process. For example, a misaligned flange was detected in a plant, and after a couple of weeks pump in the same line as the misaligned flange was defected. Both incidents can be correlated for causality. In examples, during modeling flange images are used to enhance and improve a flange failure detection algorithm model. Using the analytics output, a prediction model is developed to predict future failures of flanges. In examples, the predicted future failures are used to schedule preventive maintenance.

In some embodiments, the flange data stored in the cloud system is accessed for further analysis, and also to check the history of the assets integrity. For example, personnel in a control room (e.g., central space where a large physical facility or physically dispersed service can be monitored and controlled) can access the flange data for further analysis. In some embodiments, a communication hub is established between an operator on location at the plant, and personnel in a remote control room. This will integrate multiple work-related necessary tools in one. In examples, the communication hub includes a server collecting data from different platforms and systems, and publishing results to a dashboard that can be accessed by operators and engineers. The communication hub connects operators in a central location and maintenance / operator craft on field in real-time utilizing digital capability like augmented reality and mobility to enhance two way interaction between different functional units of the operating facility. The communication hub enables integration of multiple user functionalities like work permits, equipment data sheets, real time operating parameters, piping and instrumentation diagram (P&ID) / 3D models of plant, minimum maintenance requirements, job safety analysis (JSA) etc. In some embodiments, a backend analyzer system flags flanges that exhibit one or more defects, and sends a request to the maintenance crew to replace or correct the defective flange. The status of the defective flange is updated by the maintenance team once the flange is corrected. The communication hub links a flange clustering system and maintenance system together and display the data in one platform.

FIG.6is a process flow diagram of a process for flange integrity classification using a trained machine learning model. In some embodiments, the machine learning models are trained as described with respect toFIGS.3and4. The present techniques introduce a systematic and automated procedure to assess the integrity of flanges. An intrinsically safe tablet (e.g., tablets200ofFIG.2) pre-installed with inspection software (e.g., mobile application) prompts operators to visit a specific flange and obtain multiple images of the flange, and the flange integrity is classified. In examples, an intrinsically safe tablet is a device that can be used in industrial facility classified as hazardous. Hazardous areas, such as hydrocarbon facilities, can require any electrical device operated in constrained areas to be sealed in a way such that it cannot produce any sparks that could cause ignitions.

At block602, images of a flange are obtained. An image of the flange is captured at a predetermined angle of image capture. In some embodiments, an industrial tablet (e.g., industrial tablet202) includes one or more sensors, such as a camera sensor or gas sensor. The camera sensor is used to capture images of the flange. The gas sensor is used to detect hazardous or flammable gas plumes. In some embodiments, a mobile application executing on the industrial tablet prompts the operator to capture images at predetermined angles of image capture. The predetermined angle of image capture is based on a map of a system including flanges. In some embodiments, the operator is guided to the location of a flange via prompts from the mobile application. In examples, guidance to the location of the flange is based on a map of a system including flanges. In examples, guidance to the location of the flange is based on GPS information captured by the industrial tablet.

At block604, a condition of the flange is classified using a trained machine learning model. In some embodiments, the trained machine learning model is trained as described with respect toFIGS.3and4. For example, the trained machine learning model is a trained convolutional neural network (CNN) that obtains the images of the flange as input and outputs the classified condition, including a defect. In another example, the trained machine learning model includes segmenting the images of the flange; and image segmentation, and classifying the condition of the flange using a YOLO network. In some embodiments, the machine learning model is trained using a simulated dataset with synthetic flange images (e.g., dataset500ofFIG.5) that mimic real-world flange images. In some embodiments, the trained machine learning model is lightweight and deployed to an industrial tablet with light calculations used to output flange integrity classifications.

At block606, an indication of the condition of the flange is rendered. In some embodiments, augmented reality elements corresponding to a defect associated with the condition of the flange are superimposed on an image of the flange in real time. The image of the flange is rendered via a display (e.g., displays202ofFIG.2) of the industrial tablet. In examples, the augmented reality elements correspond to a corrective measure responsive to the condition of the flange, and is superimposed on the image of the flange in real time. In examples, a bolt and corresponding nut are augmented reality elements superimposed on an image of the flange rendered on a display (e.g., display202C ofFIG.2), illustrating inserting the bolt into the bolt holes of the flange and securing the nut to the bolt. In some embodiments, maintenance instructions and bolt tightening data (bolting patterns, torque and tensioning figures, procedures, techniques and recommended controlled bolting equipment) are rendered based on the condition of the flange. This will enable the user to correct the condition (e.g., correct the defect) at the time of inspection, if feasible.

In this manner, the present techniques enable classifications of flange integrity at a lower cost when compared to traditional techniques. The present techniques enable flange integrity detection and correction by an operator with little to no experience and training required. Moreover, the flange integrity classification according to the present techniques results in less time spent per inspection when compared to traditional techniques. The images captured for input to the trained machine learning model, and the classifications the condition of the flange establishes a complete and accurate record of the health of a system including multiple flanges. In some embodiments, the health of a system including multiple flanges is recorded through the detection of multiple anomalies or defects. This automated historical data is stored locally, at the industrial tablet. The automated historical data is transmitted to a cloud location or server when data transmission is available.

FIG.7is a schematic illustration of an example controller700(or control system) for flange integrity classification using artificial intelligence according to the present disclosure. For example, the controller700may be operable according to the process600ofFIG.6, using the mobile application and included in an industrial tablet200ofFIG.2. The controller700is intended to include various forms of digital computers, such as printed circuit boards (PCB), processors, digital circuitry, or otherwise parts of a system for supply chain alert management. Additionally the system can include portable storage media, such as, Universal Serial Bus (USB) flash drives. For example, the USB flash drives may store operating systems and other applications. The USB flash drives can include input/output components, such as a wireless transmitter or USB connector that may be inserted into a USB port of another computing device.

The controller700includes a processor710, a memory720, a storage device730, and an input/output interface740communicatively coupled with input/output devices760(for example, displays, keyboards, measurement devices, sensors, valves, pumps). Each of the components710,720,730, and740are interconnected using a system bus750. The processor710is capable of processing instructions for execution within the controller700. The processor may be designed using any of a number of architectures. For example, the processor710may be a CISC (Complex Instruction Set Computers) processor, a RISC (Reduced Instruction Set Computer) processor, or a MISC (Minimal Instruction Set Computer) processor.

In one implementation, the processor710is a single-threaded processor. In another implementation, the processor710is a multi-threaded processor. The processor710is capable of processing instructions stored in the memory720or on the storage device730to display graphical information for a user interface on the input/output interface740.

The memory720stores information within the controller700. In one implementation, the memory720is a computer-readable medium. In one implementation, the memory720is a volatile memory unit. In another implementation, the memory720is a nonvolatile memory unit.

The storage device730is capable of providing mass storage for the controller700. In one implementation, the storage device730is a computer-readable medium. In various different implementations, the storage device730may be a floppy disk device, a hard disk device, an optical disk device, or a tape device.

The input/output interface740provides input/output operations for the controller700. In one implementation, the input/output devices760includes a keyboard and/or pointing device. In another implementation, the input/output devices760includes a display unit for displaying graphical user interfaces.

There can be any number of controllers700associated with, or external to, a computer system containing controller700, with each controller700communicating over a network. Further, the terms “client,” “user,” and other appropriate terminology can be used interchangeably, as appropriate, without departing from the scope of the present disclosure. Moreover, the present disclosure contemplates that many users can use one controller700and one user can use multiple controllers700.

The computing system can include clients and servers. A client and server can generally be remote from each other and can typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship. Cluster file systems can be any file system type accessible from multiple servers for read and update. Locking or consistency tracking may not be necessary since the locking of exchange file system can be done at application layer. Furthermore, Unicode data files can be different from non-Unicode data files.

Particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. As one example, some processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results.