An electric motor is provided comprising a rotor unit, a stator unit, a housing, and a bearing mechanism. The rotor unit rotates about a vertical center axis. The stator unit is disposed around the rotor unit. The stator unit is fixed inside of the housing. The bearing mechanism rotatably supports the rotor unit relative to the stator unit. The housing comprises a cylindrical wall portion with a bottom and a cover plate, which covers an opening of the cylindrical wall portion. The bearing mechanism further comprises at least two bearings, an upper bearing is sustained by the cover plate and a lower bearing is sustained by the bottom of the housing. The cover plate is made of electrically conductive elements and comprises at least one projection protruding toward the upper outer side of the cover plate so that the at least one projection is configured to contact, directly or indirectly, at least one grounding contact of a controller, which drives and controls the electric motor disposed on the upper outer side of the cover plate. The controller is connected to a ground potential through the cover plate and the motor housing.

An electric motor includes a rotor assembly rotatable about a vertical center axis, a stator assembly around the rotor assembly, a housing inside of which the stator assembly is fixed, and a bearing mechanism rotatably supporting the rotor assembly. The housing includes a cylindrical wall portion inside of which the stator assembly is located, a bottom at a bottom vertical end of the cylindrical wall portion and extending radially outward, and a cover plate on one side of the stator assembly opposite to the bottom. The cover plate includes electrically conductive elements and at least one columnar projection protruding away from the stator assembly so that the at least one projection contacts at least one grounding contact of a controller on one side of the cover plate, the controller being electrically connected to a ground potential through the housing.

A computation unit having at least one computation core, a primary memory device, and at least one main connecting unit for connecting the at least one computation core to the primary memory device, the computation unit having at least two functional units, at least a first functional unit of the at least two functional units being embodied a) to receive first data from at least one further functional unit of the at least two functional units, and/or b) to transmit second data to at least one further functional unit of the at least two functional units.

The invention discloses a MIMO different-factor partial-form model-free control method. In view of the limitations of the existing MIMO partial-form model-free control method with the same-factor structure, namely, at time k, different control inputs in the control input vector can only use the same values of penalty factor and step-size factors, the invention proposes a MIMO partial-form model-free control method with the different-factor structure, namely, at time k, different control inputs in the control input vector can use different values of penalty factors and/or step-size factors, which can solve control problems of strongly nonlinear MIMO systems with different characteristics between control channels widely existing in complex plants. Compared with the existing control method, the inventive method has higher control accuracy, stronger stability and wider applicability.

The invention discloses a MIMO different-factor compact-form model-free control method. In view of the limitations of the existing MIMO compact-form model-free control method with the same-factor structure, namely, at time k, different control inputs in the control input vector can only use the same values of penalty factor and step-size factor, the invention proposes a MIMO compact-form model-free control method with the different-factor structure, namely, at time k, different control inputs in the control input vector can use different values of penalty factors and/or step-size factors, which can solve control problems of strongly nonlinear MIMO systems with different characteristics between control channels widely existing in complex plants. Compared with the existing control method, the inventive method has higher control accuracy, stronger stability and wider applicability.

A process module is automatically configured for operation in a plant. During the operation of the process module in a first phase in a first operational environment, operational data that is related to the process module is received by a computer data system. In a second phase, when the process module is connected to a second operational environment, operational data is received again. The computer data system has a reply function that receives and processes a query and provides a response. Processing the query includes processing the operational data that was received during the first and second phases. The process module is then configured by using configuration parameters that are derived from the response.

Providing separate real-time and configuration segments in a database. The real-time segment provides real-time data values to a real-time application and the configuration segment provides configuration data values to the real-time application. Utilizing two or more configuration segments enables changes to configuration data values without impacting real-time applications.

A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I/O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and/or I/O data delivery mechanisms, HMI routines, and the like.

A distributed control system (DCS) of an industrial process plant includes a data center storing a plant information model that includes a description of physical components, the control framework, and the control network of the plant using a modeling language. A set of exposed APIs provides DCS applications access to the model, and to an optional generic framework of the data center which stores basic structures and functions from which the DCS may automatically generate other structures and functions to populate the model and to automatically create various applications and routines utilized during run-time operations of the DCS and plant. Upon initialization, the DCS may automatically sense the I/O types of its interface ports, detect communicatively connected physical components within the plant, and automatically populate the plant information model accordingly. The DCS may optionally automatically generate related control routines and/or I/O data delivery mechanisms, HMI routines, and the like.

A method includes determining the value of a plurality of selected process variables. Preferably, the respective current value of each selected process variable or a variable derived therefrom is compared with one or more reference values, and in each case at least one deviation or at least one rate of change is determined. On the basis of the determined plurality of deviations and/or rates of change, a stability of a current process state is determined. A computing unit visualizes a process states determined for different points in time and/or cycles by a chart such that a trajectory of the stability of process states for a predetermined period of time and/or over multiple cycles is visible in the chart. The computing unit visualizes a reference that, at a certain point in time, at least one factor was existing which is relevant for an evaluation of the result of the stability's trajectory.