A building management system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to ingest asset information; cause a graphical model of the building to include a fault indicator based on the asset information, the fault indicator corresponding to a fault occurring on a first physical asset corresponding to a first virtual asset; cause a display device of a user device to display the graphical model within a user interface; receive a selection of the fault indicator from a user via the user interface; and in response to receiving the selection, cause the user interface to navigate to a fault-driven view of the graphical model depicting the first virtual asset and one or more second virtual assets corresponding to one or more second physical assets affected by the fault occurring on the first physical asset.

Method for assessing health conditions of industrial equipment (S), said equipment having one or more determined failure modes (F.sub.1, . . . , F.sub.N), each of said failure modes having one or more determined failure causes (FC.sub.D1, . . . , FC.sub.DK) and/or one or more undetermined failure causes (FC.sub.U1, . . . , FC.sub.UM), further comprising: acquiring input data (D.sub.IN) related to said equipment; calculating failure mode assessment data (R.sub.Fi, RUL.sub.Fi, A.sub.Fi, RSK.sub.Fi, POF.sub.Fi) for each failure mode (F.sub.i) determined for said equipment, wherein the calculation of said failure mode assessment data comprises: if said failure mode (F.sub.i) has one or more determined failure causes (FC.sub.D1, . . . , FC.sub.DK): executing a first calculation procedure to calculate failure cause assessment data (R.sub.FCj, RUL.sub.FCj, A.sub.FCj) for each failure cause (FC.sub.Dj) determined for said failure mode, said failure cause assessment data being calculated on the basis of said input data (D.sub.IN); calculating said failure mode assessment data on the basis of the failure cause assessment data (R.sub.FCj, RUL.sub.FCj, A.sub.FCj) calculated for each failure cause (FC.sub.Dj) determined for said failure mode; if said failure mode (F.sub.i) has one or more undetermined failure causes (FC.sub.U1, . . . , FC.sub.UM), executing a second calculation procedure to calculate said failure mode assessment data, said failure mode assessment data being calculated on the basis of said input data (D.sub.IN); calculating a system assessment data (R.sub.S, RUL.sub.S, A.sub.S, RSK.sub.S, POF.sub.S) for said equipment, said system assessment data being calculated on the basis of said failure mode assessment data (R.sub.Fi, RUL.sub.Fi, A.sub.Fi, RSK.sub.Fi, POF.sub.Fi).

Systems and methods of monitoring solidification quality and automatic correcting any detected error in additive manufacturing are described. The present disclosure includes a build station for manufacturing one or more parts and a controller having one or more computer-vision based system coupled to the build station. One or more camera is provided to obtain a plurality of images of the solidified parts at predetermined settings. The present disclosure introduces a predictive model trained by machine learning algorithm, the predictive model calculates level of solidification quality of a manufactured part and build parameters value to be adjusted. The present disclosure introduces a plurality of validation coupons having various shapes to enhance more accuracy in manufacturing, wherein the validation coupons further include block data which is distributed to electronic ledger system.

The present disclosure relates to a task processing method and device based on defect detection, a computer readable storage medium, and a task processing apparatus. The method includes receiving a detection task; determining a task type of the detection task; storing the detection task in a task queue if the task type is a target task type; and executing the detection task in a preset order and generating a feedback signal when a processor is idle. The detection task of the target task type includes an inference task and a training task. Executing the training task includes modifying configuration information according to a preset rule based on product information in the detection task; acquiring training data and an initial model according to the product information; and using the training data to train the initial model according to the configuration information to obtain a target model and store it in memory.

Example computer-implemented methods, media, and systems for isolating an emergency within process systems of a hydrocarbon processing facility are disclosed. One example method includes receiving a drawing of multiple process systems of a hydrocarbon processing facility. A respective identifier is assigned to each of multiple drawing vectors, where each of the multiple drawing vectors corresponds to a respective component of the multiple process systems. The multiple drawing vectors are coded based on the assigned identifiers. One or more drawing vectors connected to a location of the emergency are determined from the multiple coded drawing vectors. One or more connections between the one or more drawing vectors and the location of the emergency are identified. A first isolation valve for isolation of the emergency is determined from one or more isolation valves corresponding to the one or more connections. The first isolation valve is provided for the isolation of the emergency.

A press and a process for operating it, has an upper tool and a lower tool for shaping a workpiece and a work unit including a press drive and/or a drawing device, is controllable by a controller, to perform the shaping process. The controller outputs, to the work unit, press process values, which define the sequence of events of the shaping process. Workpiece parameters characterize, for example, the material and/or the shape and/or at least one dimension and/or quality of the workpiece, which still has to be shaped or has already been shaped. The controller is assigned a composition matrix which contains, for each defined workpiece parameter and for each defined press process value, an individually changeable function, to describe the relationship between every one of the defined workpiece parameters and the defined press process values. This composition matrix allows a quick and rapid adjustment and adaptation of the press.

A computer-implemented method for detecting faults and events related to a system includes receiving sensor data from a plurality of sensors associated with the system. A hierarchical failure model of the system is constructed using (i) the sensor data, (ii) fault detector data, (iii) prior knowledge about system variables and states, and (iii) one or more statistical descriptions of the system. The failure model comprises a plurality of diagnostic variables related to the system and their relationships. Probabilistic reasoning is performed for diagnostic or prognostic purposes on the system using the failure model to derive knowledge related to potential or actual system failures.

Embodiments described below include a method for calibrating a digital twin, the digital twin being representative of a complex system governed by a set of parameters. The method includes receiving a subset of data from a plurality of sensors of the complex system then calculating an error that represents a difference in state between the digital twin and the complex system. In addition, a gradient of the calculated error is calculated and utilized to generate a set of candidate parameters for the complex system. The candidate parameters are generated using a gradient optimization. The candidate parameters are provided to the digital twin model and the error value is based on the candidate parameters. Steps may be repeated iteratively including calculating of the error and its gradient, generating of the candidate parameters, and applying the candidate parameter to the digital twin until the calculated error is smaller than a user-defined tolerance.

A defect profiling and tracking system for a process-manufacturing enterprise is provided. The system includes a memory and a processor. The processor is configured to access entity data for a plurality of entities of the process-manufacturing enterprise and process parameter data for one or more deviating entities. The processor is configured to analyze the entity data and the process parameter data for each of the deviating entities to determine a plurality of relationships between quality defects and the process parameters to generate a unique entity specific process signature (EPS) for each entity. The processor is configured to receive real-time process parameter data for one or more entities to generate a real-time process signature for the one or more entities and compare the real-time process signature of each entity with EPS corresponding to the entity to detect one or more EPS matches that are indicative of a quality defect.

Implementations transmit, to a distributed control node (DCN), default alarm configuration file(s) that are determined based on a particular type of function block. Those implementations transmit the default alarm configuration file(s) to the DCN based on determining that the DCN is executing a function block that is of the particular type and/or is utilizing a function block, that is of the particular type, in alarm monitoring. A default alarm configuration file that is transmitted to a DCN can be stored locally at the DCN to enable viewing and/or editing thereof via human-machine interface (HMI) interfacing with the DCN. Implementations additionally or alternatively relate to reflecting, in an alarms database accessible via a process automation network, local updates that are made via an HMI to a default alarm configuration file locally stored at a DCN.