The invention provides improved methods and apparatus for control using field and control devices that provide a virtual machine environment and that communicate via an IP network. By way of non-limiting example, such field device can be an “intelligent” transmitter or actuator that includes a low power processor, along with a random access memory, a read-only memory, FlashRAM, and a sensor interface. The processor can execute a real-time operating system, as well as a Java virtual machine (JVM). Java byte code executes in the JVM to configure the field device to perform typical process control functions, e.g., for proportional integral derivative (PID) control and signal conditioning. Control networks can include a plurality of such field and control devices interconnected by an IP network, such as an Ethernet.

Methods and apparatus for control using control devices that provide a virtual machine environment and that communicate via an ip network

Connection objects or other such data structures facilitate establishing and configuring connections between objects that model components in a process control system. A first set of data structures (e.g. the object connection type structures) identify valid types for component-to-component pairings and the respective roles of each component in the pairing (e.g., parent or child, source or sink). A second set of data structures (e.g., the parameter connection type structures) supply similar information for parameter-to-parameter connections. Together, these data structures can be used, for example, to validate component-to-component connections suggested by the user and to automatically configure parameter-to-parameter connections. Actual connections, both at the component or parameter level, are reflected using parameter overrides within the parameterized object model—with which the connection objects are constructed.

Process control configuration system with connection validation and configuration

A method is provided for invoking condition monitoring among a plurality of machines, comprising establishing a network of automated local experts at generally fixed locations and interconnected by at least one network connection, configuring each of the local experts to receive vibration data from at least one of the plurality of machines which is located in relative physical proximity thereto and configuring each of the local experts to analyze the received vibration data and to diagnose a condition of the machine providing the received vibration data based on the received vibration, and configuring each of the local experts to transmit diagnostic information relating to the condition of the respective machines via the at least one network connection.

Diagnostic network with automated proactive local experts

The invention provides a computerized method and apparatus which enables a user, even one who has little or no predictive maintenance skills, to establish a predictive maintenance database that defines information needed to monitor equipment in accordance with a predictive maintenance plan. The type of equipment components to be monitored and associated physical characteristics of the components are input to a computer as an equipment configuration, which may include one or more interconnected components. The computer includes a knowledge base that defines relationships between monitoring practices, component types, and physical characteristic information for component types. A predictive maintenance database is constructed for the components using the inference engine operating on the knowledge base, the selected component type, and the selected physical characteristic information. Multiple measurement technologies may be specified for each component. Preferably, for each measurement technology specified, the predictive maintenance database includes measurement points, an analysis parameter set, and an alarm limit set. Equipment configurations may be defined by the user, or they may be stored in a configuration/component warehouse with little or no configuration definition required of the user.

Method and apparatus for establishing a predictive maintenance database

A method for advanced condition monitoring of an asset system includes sensing actual values of an operating condition for an operating regime of the asset system using at least one sensor; estimating sensed values of the operating condition by using an auto-associative neural network; determining a residual vector between the estimated sensed values and the actual values; and performing a fault diagnostic on the residual vector. In another method, an operating space of the asset system is segmented into operating regimes; the auto-associative neural network determines estimates of actual measured values; a residual vector is determined from the auto-associative neural network; a fault diagnostic is performed on the residual vector; and a change of the operation of the asset system is determined by analysis of the residual vector. An alert is provided if necessary. A smart sensor system includes an on-board processing unit for performing the method of the invention.

System and method for advanced condition monitoring of an asset system

A hand held data collector and analyzer achieves superior stability, accuracy and reliability through the use of a fixed frequency anti-aliasing filter and two analog to digital converters, one for high frequency signals and one for low frequency signals. Efficient digital filtering and decimation is achieved by providing specialized hardware filters and decimators and implementing software filters and decimators in a data processor. A true zoom method is employed to take advantage of the filtering and decimation capabilities, and independent digital signal processors operate serially on incoming signals to further reduce demands on the central processing unit. In addition, the RAM system memory is formatted as a pseudo-card to facilitate use of a PCMCIA memory card or the RAM system memory.

Hand held data collector and analyzer system

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Hand held data collector and analyzer system