The present disclosure provides a method for gas meter replacement prompt based on a smart gas Internet of Things and a system thereof. The method is applied to a sub platform of a management platform of a smart gas indoor device, wherein the method includes: obtaining model data, use data, and maintenance data of a target gas meter in a smart gas data center; determining a target time for replacing the target gas meter and uploading the target time to the smart gas data center based on the model data, use data and maintenance data of the target gas meter, wherein the smart gas data center is configured to send the target time to a smart gas service platform, and the smart gas service platform is configured to send the target time to a smart gas user platform.

An example method comprises receiving historical wind turbine failure data and asset data from SCADA systems, receiving first historical sensor data, determining healthy assets of the renewable energy assets by comparing signals to known healthy operating signals, training at least one machine learning model to indicate assets that may potentially fail and to a second set of assets that are operating within a healthy threshold, receiving first current sensor data of a second time period, applying a machine learning model to the current sensor data to generate a first failure prediction a failure and generate a list of assets that are operating within a healthy threshold, comparing the first failure prediction to a trigger criteria, generating and transmitting a first alert if comparing the first failure prediction to the trigger criteria indicates a failure prediction, and updating a list of assets to perform surveillance if within a healthy threshold.

A system and method are provided for determining cleaning schedules for equipment. The equipment includes fired heaters and/or heat exchangers. The method includes obtaining historical sensor data; transforming the obtained sensor data using an engineering first principles process; applying data analytics to the transformed data to generate at least one statistical model; predicting an indicator of fouling in the equipment using operating data and the at least one statistical model; obtaining cost data associated with the equipment being analyzed; determining from the prediction and cost data a desired cleaning schedule for the equipment; and providing an output associated with the desired cleaning schedule.

Systems and methods of managing a building are disclosed. In some embodiments, a method includes receiving, by a processing circuit, an indication to execute a digital twin, the digital twin including one or more fault detection or diagnostics functions and a virtual representation of a piece of equipment, the virtual representation including one or more entities of a building and relationships between the entities of the building, executing the digital twin based on the virtual representation of the piece of building equipment to generate an indication of a fault or a diagnosis of the fault for the one or more pieces of building equipment, and storing an indication of the fault or a diagnosis of the fault, or a link to the fault or the diagnosis of the fault, in the virtual representation of the piece of equipment.

A computer-implemented method for predicting an effect of an intervention on managed computing resources using a first system automation management system, comprising an automated operations controller and at least one automation agent is disclosed. The method comprises sending initial state data of the first system automation management system to a second system automation management system which is a functional duplicate of the first system automation management system, sending a status change command and a related expected response vector, equivalent to a result of the intervention to the second system automation management system, determining, by the second system automation management system, a predicted response vector of the managed computing resources in response to the received status change command, and responding, by the second system automation management system, with the determined response vector and a set of predicted actions derived therefrom.

A method for diagnostics and monitoring of anomalies in a cyber-physical system (CPS) includes obtaining information related to anomalies identified in the CPS. The obtained information includes at least one value of one or more CPS variables. One or more classifying features of the identified anomalies in the CPS are generated based on the obtained information. Classification of the identified anomalies in the CPS into two or more anomaly classes is performed based on the generated classifying features. Each of the two or more anomaly classes is associated with one or more anomaly characteristics. Diagnostics of anomalies are performed in each of the two or more anomaly classes by calculating values of the anomaly characteristics associated with each of the two or more anomaly classes. Anomalies of each of the two or more anomaly classes are monitored based on the calculated values of the anomaly characteristics associated with each of the two or more anomaly classes.

A computer-implemented method for detecting an anomaly in a technical system. The method includes detecting an environment state vector and a system state vector, the environment state vector including at least one first value which characterizes a physical environment condition or a physical operating condition of the technical system, and the system state vector including at least one second value which characterizes a physical condition of the technical system; ascertaining, using an environment anomaly model, an environment value which characterizes a probability or a probability density value with which the environment state vector occurs; ascertaining, using a system anomaly model, a system value which characterizes a conditional probability or a conditional probability density value with which the system state vector occurs if the environment state vector occurs; signaling the presence of an anomaly or signaling the absence of an anomaly based on the environment value and/or the system value.

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.

State of the art systems used for industrial plant monitoring have the disadvantage that they fail to correctly assess reason for dip in performance of the plant and in turn trigger appropriate corrective measures. The disclosure herein generally relates to industrial plant monitoring, and, more particularly, to a system and method for development and deployment of self-organizing cyber-physical systems for manufacturing industries. The system monitors and collects data with respect to various parameters, from the industrial plant. If any performance dip is detected, the system determines corresponding cause, and also triggers one or more corrective actions to improve performance of the plant and different plant components to a desired performance level.

An integrated vehicle health management (IVHM) system to resolve equipment-fault related anomalies detected by cyber intrusion detection system (IDS). A benefit of the present system is that it can result in fewer alerts that need manual analysis. A combination of cyber and monitoring with integrated vehicle health management (IVHM) may be a high value differentiator. As a solution gets more mature through a learning loop, it may be customized for different customers in a cost-effective manner, something that might be expensive to develop on their own for most original equipment manufacturers (OEMs). An IVHM symptom pattern recognition matrix may link a pattern of reported symptoms to known equipment failures. This matrix may be initialized from the vehicle design data but its entries may get updated by a learning loop that improves a correlation by incorporating results of investigations.