The present disclosure discloses a control method of industrial Internet of Things (IoT) based on abnormal identification. The IoT includes: an obtaining unit, which is configured to obtain a first machining parameter; a detection unit, which is configured to obtain real-time image data when the first machining parameter is abnormal; an extraction unit, which is configured to obtain a keyframe and obtain a second machining parameter; a judgment unit, which is configured to determine an abnormal cause based on the first machining parameter and the second machining parameter; and a communication unit, which is configured to transmit the abnormal cause to a user terminal through a service platform.

In one aspect, a method of generating a model for HVAC system control is provided. The method includes generating a model of the performance of an HVAC system, providing the generated model to at least one of an optimal control system and a diagnostic system, and automatically tuning the HVAC system using the generated model and at least one of the optimal control system and the diagnostic system.

According to one embodiment, a distributed control system comprises a communication network and a plurality of control devices configured to control devices to be controlled, respectively. The control devices each include a simulator to which a program organization unit is allocated in advance, configured to simulate the allocated program organization unit, and a shared memory that stores a simulation result of the program organization unit simulated by the simulator to be shared with another control device. At least one of the control devices includes a simulation table database that can store therein an execution time of each of the program organization units allocated in advance to the control devices, and a simulation commander that stores, in the simulation table database, the execution time of each of the program organization units corresponding to the simulation result.

A system and method for implementing a control process within a process control system and resolving inconsistencies during execution of the control process includes loading the logical structure of the control process, loading a plurality of instantiation objects or processes when the control process is instantiated, using the instantiation objects to instantiate a procedural element of the control process as the control process calls for the procedural element during execution, executing the procedural element as part of the control process, and deconstructing the procedural element as execution of the procedural element is completed during execution of the control process. Resolution of inconsistencies includes executing a first model of an entity in a controller, executing a second model of the entity in an execution engine, detecting a difference between the models, generating a prompt and receiving an operation instruction to continue the process or abort the process.

Performance of a decision-making logic (35) of a controller (31) of an industrial automation control system is monitored during field operation of the controller (31). A supervisory system (20) receives operational data collected during field operation of the controller (31). The supervisory system performs an analysis of the operational data to assess performance of the decision-making logic (35), using pre-operational data generated prior to field operation of the controller (31) and/or a performance assessment logic (27) generated prior to field operation of the controller (31). The supervisory system (20) generates an analysis output based on a result of the analysis

Method and system of determining ground-to-tire friction coefficient for an aircraft landing event. The method uses an aircraft computational model to repeatedly model the landing event, varying one or more initial conditions of the aircraft model until a best match between a modelled value and a provided value of aircraft vertical acceleration is determined. The method uses initial conditions associated with the best match of modelled and provided vertical acceleration values and a strain value from a sensor on the aircraft landing gear, with the ground-to-tire friction coefficient is a variable. The method models the landing gear to generate a modelled strain value and compares this with the measured strain value, and repeats the landing gear modelling step with a different value for the ground-to-tire friction coefficient until a best match between the modelled strain value and the measured strain value is determined and outputting the respective friction coefficient value.

A system for monitoring a condition of a mobility system includes a number of sensors coupled to the mobility system, the number of sensors being structured and configured to generate data indicative of use of the mobility system during use, and a controller implementing a trained machine learning system. The controller is structured and configured to receive the data, characterize a lifecycle stage of the mobility system using the trained machine learning system based on at least the received data, and generate an alert for required maintenance for and/or predicted breakdown of the mobility system based on the characterized lifecycle stage.

A method of estimation on a curve of a relevant point for detecting an anomaly of a motor. The method includes selecting a profile including a binary code, each component of which codes a direction of variation between two consecutive characteristic points of at least one learning curve, a model making it possible to estimate a relevant point based on a set of characteristic points of a curve and a filter. The method also includes applying the filter of the profile selected to the curve, determining a set of characteristic points of the filtered curve and of a binary code, comparing the determined code and the code of the profile selected, and estimating, as a function of the comparison, the relevant point on the curve based on the characteristic points of the filtered curve and the model of the profile selected.

A method is disclosed for synchronizing a checking apparatus, in which the checking apparatus is configured for testing at least one first electronic closed-loop control unit. Further disclosed is a checking apparatus which is transferable to a synchronized state. Additionally disclosed is a composite system which includes at least two checking apparatuses. Also disclosed are a checking apparatus for testing at least one first closed-loop control unit, and a composite system including at least one checking apparatus and a further checking apparatus, the latter checking apparatus being configured to have the same effect as the first checking apparatus.

A system for predicting HVAC equipment failure includes an actuator and a controller. The actuator is coupled to the HVAC equipment and configured to drive the HVAC equipment between multiple positions. The actuator includes a processing circuit configured to collect internal actuator data characterizing an operation of the actuator and a communications circuit coupled to the processing circuit. The communications circuit is configured to transmit the internal actuator data outside the actuator. The controller is configured to provide control signals to the actuator and receive the internal actuator data from the actuator. The controller includes a failure predictor configured to use the internal actuator data to predict a time at which the HVAC equipment failure will occur.