There is described a system and method for managing control performance of a field device receiving variable data. Variable and setpoint references corresponding to a control loop of the field device are identified. A time delay normal period based on expected oscillations of the variable reference and first settling thresholds associated with the setpoint reference are also identified. An offnormal timestamp is generated based on the variable reference relative to one or more second settling thresholds associated with the setpoint reference. A normal timestamp is generated based on the variable reference relative to the first settling thresholds. A settling time of the control performance is determined based on the normal timestamp, the offnormal timestamp, and the time delay normal period. One or more performance features of the field device are modified based on the determined settling time.

A method includes obtaining an actual value of a condition affected by operating the building equipment, operating the building equipment using a setpoint for the condition, calculating a stability index based on a timeseries of errors between the actual value and the setpoint, comparing the stability index to a criterion, in response to the stability index satisfying the criterion, executing an action relating to the building equipment, and in response to the stability index not satisfying the criterion, not executing the action.

A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and a predefined scalar range is saved with predetermined different fixed scalar values, which define respectively an existing limit potential, wherein a respective limit potential characterizes a change in the diagnostic state, and wherein between adjacent limit potentials a diagnostic partition is formed and wherein different scalar values from the scalar range are assigned to the different diagnostic partitions and wherein different respective diagnostic partitions represent different diagnostic states of the industrial machine, and wherein the monitoring device has at least one interpolation function for mapping the characteristic value tuple on a common, unique, time-dependent scalar value in the predefined scalar range, so that by way of the time-dependent scalar value a time monitoring of the diagnostic state of the industrial machine is achieved.

There is provided a monitoring method and system for an aircraft engine. At an engine controller, a current value of an ageing parameter of at least one component of the aircraft engine is obtained, the at least one component having a life limit associated therewith. Based on the current value of the ageing parameter, it is determined, at the engine controller, whether the life limit of the at least one component has been reached. A start of the aircraft engine is inhibited, at the engine controller, when the life limit of the at least one component has been reached.

An industrial shock absorber system may include at least one sensor that is configured to measure an operating parameter of the industrial shock absorber during operation of the shock. The system may be configured to determine Time-Through-Stroke (TTS) and/or Rod Return Time (RRT) utilizing data from the sensor or sensors. The system may be configured to utilize machine learning to detect and/or predict a failure of the industrial shock absorber.

The present invention discloses a method for controlling a process in a process plant using a controller. The method comprises receivable associated with the process, determining a first value of at least one key performance indicator associated with the transition from the process data of the first process variable between the first steady state and the second steady state, comparing the determined first value of the at least one key performance indicator against a threshold value of the at least one key performance indicator; and determining a correction factor for modifying a set point of the process variable based on the comparison, for controlling the process.

Devices, systems, and techniques relating to steam trap monitoring are described. These include battery-less steam trap monitors that run on power harvested from their environments, systems for acquiring steam trap monitor data for the traps in a facility or across multiple facilities, and techniques for processing steam trap monitor data to reliably determine the status of individual steam traps and potentially other system parameters.

An overall efficiency of an actuator system can be monitored to determine the health of the actuator system. A ratio between the output power of the actuator system and the input power of the actuator system is monitored over time. Degradation of the efficiency of the actuator system may be observed and corrective action taken before failure of the actuator system.

Computer systems, methods, and apparatuses for estimating changes in gas emissions are described. A computer system may monitor overall emission levels based on sensor outputs from a plurality of gas sensors in a facility. The computer system may estimate a total emission level over a time interval based on the accumulative, gas-response-factor weighted detections of the gas sensors. Emissions from maintenance activities may be excluded as appropriate. The total emission level may be compared with total emission level estimated from different time intervals and/or different facilities. The computer system may be further used for comparing emissions across multiple facilities, or emissions from facilities across multiple regions.

Rotating machinery is evaluated by calculating a boundary condition based on a measured value of a parameter related to an operating state of the rotating machinery, and calculating an evaluation value corresponding to the calculated boundary condition based on the reduced order model, during operation of the rotating machinery. The reduced order model is created based on a prediction model including a heat transfer model and a structural model of the rotating machinery for predicting an evaluation value of the rotating machinery corresponding to the boundary condition.