Systems and methods for boiler regulation are disclosed. The system can receive boiler data from a boiler and compare the boiler data to a normal operating range to detect an abnormality. Based on a plurality of rules, the system can identify an anticipated root cause and at least one corrective action. Based on the at least one corrective action, the system can generate and/or output instructions for the boiler to perform the at least one corrective action. The system can display an indication of the abnormality and/or the at least one corrective action.

Methods and systems for fault identification and mitigation in an engine system. A state observer obtains current state information from the engine system, and a feature calculator uses data obtained from the state observer to calculate one or more feature indicators, which are monitored by a health estimator for the occurrence of a change using one or more change probability models. When the health estimator identifies a change, a fault isolator determines a component of the engine system that is subject to fault or health deterioration.

A workflow deployment system comprising at least one computing device having a memory unit and a first communication unit, and a plurality of software agents, wherein each software agent is installable on an electronic apparatus of the plurality of electronic apparatuses, wherein each software agent exchanges data with the electronic apparatus, wherein the memory unit stores workflow data related to a workflow for performing a task, the workflow comprising at least a first workflow package for a first part of the task, wherein the computing device assigns the first workflow package to the first apparatus, and to provide workflow data related to the first workflow package to the software agent of the first apparatus, to receive a problem solution request from the software agent, to perform, upon reception of the request, a workflow modification process; and to provide the customized data to the software agent of the first apparatus.

A method operates a technical or non-technical system. At least one information element of a first type which relates to the system and is dependent on the respective system state of the system is transmitted according to the method from at least one first facility of the system to at least one second facility. The second facility uses at least one information element of a second type which originates neither from the first facility nor from a different facility of the system, i.e. it comes from a source other than the system, to estimate the system state, checks, on the basis of the estimated system state, whether the received information matches the estimated system state to a predefined extent, and, in the event of a match to the predefined extent, regards the information as trusted, and otherwise generates a warning signal indicating a possible data attack.

An operation assistance method for presenting a recommended operational action in response to an alert issued in a facility includes: detecting issuance of the alert; extracting, from an alert history storage unit, an issuance history of a same type of alert as the alert of which the issuance or the sign of issuance has been detected; extracting, from an operation history storage unit storing an operation history of the facility, an operational action after issuance of the same type of alert; extracting, from an operational data storage unit storing information indicating an operational state of the facility, information indicating the operational state after the operational action, and verifying an influence of the operational action on the operational state; and presenting the recommended operational action in response to the alert of which the issuance has been detected, based on the extracted operational action and the verified influence.

Example implementations involve a system for Predictive Maintenance using Generative Adversarial Networks for Failure Prediction. Through utilizing three processes concurrently and training them iteratively with data-label pairs, example implementations described herein can thereby generate a more accurate predictive maintenance model than that of the related art. Example implementations further involve shared networks so that the three processes can be trained concurrently while sharing parameters with each other.

A system for managing sensors of a building includes a data repository configured to store sensor data from the sensors, a building management system (BMS) controller configured to monitor or control components of the building based on sensor data provided by the sensors, and a sensor diagnostic system. The sensor diagnostic system is configured to receive samples of the sensor data from the sensors, classify each of the samples of the sensor data as faulty or non-faulty, generate supplemental data based on a subset of the samples of the sensor data that are classified as faulty and corresponding attributes of the subset of the samples of the sensor data that are classified as faulty, and provide the supplemental data to the BMS controller to monitor or control the components of the building based on the supplemental data.

The non-limiting technology described herein is a failure managing framework for complex systems that determines and restores functionality of failing systems and sub-systems using a function-based intervention approach having ontological content such as provided in a System State Graph directed graph. An integration framework allows integration of multiple intervention definition paradigms and selects the best for the current scenario; modifies procedures according to current context by encapsulating operator's tacit knowledge; provides an additional safety net during application of intervention and allows both autonomous operations and assistance to a human operator in the loop.

An anomaly determination device and an anomaly determination method determine an anomaly of a device based on state data of the device, by using a first determination model configured to determine whether a predetermined anomaly has occurred in the device, and a second determination model configured to classify state of the device, and output the determined anomaly of the device as an unknown anomaly in a case where the anomaly of the device is not the predetermined anomaly.

A system for diagnosing an additive manufacturing device is provided. The system includes a first module configured to: obtain one or more parameters for a digital twin of a component of the additive manufacturing device based on raw data from the component of the additive manufacturing device; and generate physics features for the digital twin of the component of the additive manufacturing device based on the one or more parameters and one or more transfer functions, a second module configured to obtain one or more classifiers for classifying the component as a first condition or a second condition based on physics features; and a third module configured to: determine a health of the component based on the generated physics features of the first model and the one or more classifiers.