In embodiments of the present invention improved capabilities are described for a system for process monitoring through data collection in an industrial drilling environment comprising a data collector communicatively coupled to a plurality of input channels, each input channel connected to a monitoring point from which data is collected, the collected data providing a plurality of process parameter values for the industrial drilling environment; a data storage structured to store collected data from the plurality of input channels; a data acquisition circuit structured to interpret the plurality of process parameter values from the collected data; and a data analysis circuit structured to analyze the plurality of process parameter values to detect a process condition associated with the industrial drilling environment, wherein an operational process for the industrial drilling environment is altered based on the analysis of the plurality of process parameter values.

Monitoring a processing asset for one of an oil processing facility and a gas processing facility includes a data acquisition circuit structured to interpret a plurality of detection values corresponding to input received from a detection package, the detection package comprising at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of process components; a data analysis circuit structured to analyze a subset of the plurality of detection values to determine a status parameter; and an analysis response circuit structured to adjust a process utilizing the processing asset in response to the status parameter comprising altering at least one process parameter selected from the process parameters consisting of: a temperature, an operating speed, a utilization value of one of the plurality of process components, and a process flow.

Disclosed is a process for creating an event prediction model that employs a data-driven approach for selecting the model's input data variables, which, in one embodiment, involves selecting initial data variables, obtaining a respective set of historical data values for each respective initial data variable, determining a respective difference metric that indicates the extent to which each initial data variable tends to be predictive of an event occurrence, filtering the initial data variables, applying one or more transformations to at least two initial data variables, obtaining a respective set of historical data values for each respective transformed data variable, determining a respective difference metric that indicates the extent to which each transformed data variable tends to be predictive of an event occurrence, filtering the transformed data variables, and using the filtered, transformed data variables as a basis for selecting the input variables of the event prediction model.

A monitoring system includes: a monitoring sensor; a mobile robot configured to switch between an autonomous movement mode and a remote control mode; a monitoring terminal; and a system control apparatus configured to switch between the autonomous movement mode and the remote control mode of the mobile robot based on an alarm activation and to determine a manner of a video to be displayed on a display device. In response to a detection of the alarm activation of the monitoring sensor, the system control apparatus permits the display device to display a video in the security priority area in an unprocessed manner and inhibits the display device from displaying a video in the privacy priority area in the unprocessed manner.

Methods, system and apparatus for monitoring vibration sensitive industrial equipment is disclosed. The system may include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to input received from at least one of a plurality of input sensors, each of the plurality of input sensors operatively coupled to at least one of a plurality of components of the equipment, a signal conditioning circuit structured to process a subset of the detection values on multiples of a key frequency, a vibration analysis circuit structured to identify vibration in at least one of the plurality of components, a data analysis circuit structured to analyze the plurality of detection values and determine a status parameter value, and an analysis response circuit structured to take an action in response to the status parameter value.

According to some embodiments of the present invention there is provided a computerized method for automatic monitoring of control systems. The method may comprise receiving electronic measurement values, measured on one or more conductors of computerized control devices, where the conductors may be a system bus conductor and/or and input-output line of a programmable logic controller. The method may comprise automatically calculating normal data patterns based on an analysis of the electronic measurement values. The method may comprise matching between new electronic measurement values measured on the computerized control devices and the normal data patterns to automatically detect abnormal data patterns. The method may comprise sending automatically an abnormal operation alert in response to the abnormal data patterns.

A plant monitoring device includes storage and one or more processors. The storage includes an alarm database in which a plurality of items of determination information as to alarm information is registrable, the alarm information being identification information for identifying an alarm. The processors are configured to register, in response to input of an item of determination information as to the alarm information, the item of determination information and the alarm information in the alarm database in association with each other.

Introducing a separation into modes for an industrial control system, wherein a training of a model is conducted for each of the modes and an undefined action is detected by monitoring the industrial system within the respective mode.

An equipment monitoring system includes a control unit that switches a detection operation mode of a detector between a simple detection mode where the detector periodically performs a momentary detection operation, and a detailed detection mode where the detector performs a continuous detection operation. In the simple detection mode, a diagnosis unit diagnoses whether an operating state of monitored equipment is a normal state or a state requiring caution based on results of detection by the detector. In the simple detection mode, the control unit maintains the simple detection mode when the diagnosis unit has diagnosed that the operating state of the monitored equipment is a normal state, and switches the detection operation mode of the detector from the simple detection mode to the detailed detection mode when the diagnosis unit has diagnosed that the operating state of the monitored equipment is a state requiring caution.

Remote sensing techniques are being increasingly used for periodic structural health monitoring of vast infrastructures. Conventionally, analysis of visual and other signals captured from sensing devices are used to diagnose faults. Such data collection and analysis is expensive in terms of both computational overheads as well as towards robotic maneuvering of data collection systems, such as a UAV. In accordance with the present disclosure, the data acquisition system is modeled as an intelligent situated agent that autonomously controls data gathering and analysis activities through a cognitive cycle of perception-recognition-action, in order to optimize the cost of efforts in identifying faults that may exist. Also, a reactive, economical planning algorithm around Qualitative Bayesian Network (QBN) that controls the sequence of data collection and analysis has been implemented.