Methods and systems for monitoring data collection are described. The system can include a data acquisition circuit structured to interpret a plurality of detection values, each of the plurality of detection values corresponding to at least one of a plurality of input sensors communicatively coupled to the data acquisition circuit; a signal evaluation circuit comprising: a timer circuit structured to generate at least one timing signal; and a phase detection circuit structured to determine a relative phase difference between at least one of the plurality of detection values and the at least one timing signal from the timer circuit; and a response circuit structured to perform at least one operation in response to the relative phase difference.

The present disclosure describes systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process. A system can include a data circuit for analyzing a plurality of sensor inputs, a network control circuit for sending and receiving information related to the plurality of sensor inputs to an external system, and a storage device. The data circuit may continuously monitor sensor inputs and store them in an embedded data cube, and the self-sufficient data acquisition box dynamically determines what information to send based on analyzing historical data.

An information display device, includes: a display that displays a display screen including information on measurement values respectively corresponding to measurement tags; and a controller. When one or more of the measurement values are greater than a first threshold, the controller causes the display to display a first pop-up screen, including information on the one or more measurement values, superimposed on the display screen.

Context-awareness in preventative maintenance is provided by receiving sensor data from a plurality of monitored systems; extracting a first plurality of features from a set of work orders for the monitored systems, wherein individual work orders include a root cause analysis for a context in which a nonconformance in an indicated monitored system occurred; predicting, via a machine learning model, a nonconformance likelihood for each monitored system based on the first plurality of features; selecting a subset of alerts based on predicted nonconformance likelihoods for the monitored systems; in response to receiving a user selection from the first set of alerts and a reason for the user selection, recording the reason as a modifier for the machine learning model; and updating the machine learning model to predict the subsequent nonconformance likelihoods using a second plurality of features that excludes the additional feature identified from the first plurality of features.

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points related to machine components, wherein at least one of the plurality of input channels is connected to a data collection point on a rotating machine component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data, each of the plurality of detection values corresponding to at least one of the plurality of input channels; and an expert system analysis circuit structured to analyze the collected data, wherein the expert system analysis circuit determines a failure state for the rotating machine component based on analysis of the plurality of detection values, wherein upon determining the failure state the expert system analysis circuit provides the failure state to a data storage.

A system, a control unit, and a method for controlling operation of a technical system are provided. The technical system includes a plurality of sensors. The method includes receiving first sensor data from a first sensor of the plurality of sensors. The method includes detecting a first sensor anomaly based on failure of the first sensor to generate the first sensor data. The failure of the first sensor includes generation of anomalous first sensor data. The method also includes validating the first sensor anomaly based on a comparison between the first sensor data and a virtual first sensor data. Thereafter, a control command is generated to the technical system by replacing the virtual first sensor data in lieu of the first sensor data when the first sensor anomaly is validated.

Systems, methods, and apparatuses for data collection in a mining environment are disclosed. One exemplary embodiment is a system comprising a data storage structured to store at least one collector route and at least one sensor specification; a data collector communicatively coupled to input channels, and providing detection values from the input channels in response to a selected one of each of the at least one collector route and the at least one sensor specification; a data acquisition circuit structured to interpret the detection values from the data collector; a data analysis circuit structured to: analyze detection values; and determine a data collection quality parameter by evaluating at least one of: the at least one selected sensor collector route and the at least one selected sensor specification; and an analysis response circuit structured to adjust at least one of: the selected at least one sensor collector route and the at least one selected sensor specification, in response to the data collection quality parameter.

Systems for self-organizing data collection and storage in a manufacturing environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the manufacturing system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

The present disclosure describes systems for data collection in an industrial environment having a self-sufficient data acquisition box for capturing and analyzing data in an industrial process. A system can include a data circuit for analyzing a plurality of sensor inputs, and a network control circuit for sending and receiving information related to the sensor inputs to an external system. The system may provide sensor data to a plurality of other similarly configured systems, and the system dynamically reconfigures where it sends data and what quantity of data it sends based on an availability of the other similarly configured systems.

The present disclosure describes monitoring systems for data collection in an industrial environment. A system can include a data collection circuit to collect output data from a plurality of sensors such as vibration sensors, ambient environment condition sensors and local sensors for collecting non-vibration data proximal to a machine in the environment. The sensors may be communicatively coupled to a data collection circuit, and a machine learning data analysis circuit may receive the output data and learn received data patterns predictive of at least one of an outcome and a state. The monitoring system may determine if the output data matches a learned received output data pattern, wherein the data collection circuit collects data points from sensors based on the learned received output data patterns, the outcome, or the state.