A system for identifying a state of a target of an industrial environment that generally includes a first handheld device including one or more sensors configured to record a first type of state-related measurement; a second handheld device including one or more sensors configured to record a second type of state-related measurement; and a server that receives the first type of state-related measurement from the first handheld device and the second type of state-related measurement from the second handheld device. The server includes intelligent systems configured to: process the first type of state-related measurement and the second type of state-related measurement against pre-recorded data stored within a knowledge base to identify the state of the target; and update the pre-recorded data according to at least one of the first type of state-related measurement or the second type of state-related measurement.

A system for predicting a service event from vibration data generally includes an industrial machine comprising at least one vibration sensor disposed to capture vibration of a portion of the industrial machine; a vibration analysis circuit in communication with the at least one vibration sensor; a multi-segment vibration frequency spectra structure that facilitates mapping the captured vibration to one vibration frequency segment of a multi-segment vibration frequency; a severity unit algorithm that receives the frequency of the captured vibration and the corresponding vibration frequency segment and produces a severity value which is then mapped to one of a plurality of severity units defined for the corresponding vibration frequency segment; and a signal generating circuit that receives the one of the plurality of severity units, and based thereon, signals a predictive maintenance server to execute a corresponding maintenance action on the portion of the industrial machine.

An industrial machine predictive maintenance system generally includes a mobile data collector swarm comprising one or more mobile data collectors configured to collect health monitoring data representative of conditions of one or more industrial machines located in an industrial environment; an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto; and a computerized maintenance management system (CMMS) that produces at least one of orders and requests for service and parts responsive to receiving the industrial machine service recommendations.

A system generally includes a sensor detecting a condition of an industrial machine, the sensor producing a signal that varies over time and substantially corresponds with the condition; an analog to digital converter that receives the signal and samples the signal at a streaming sample rate that is at least twice a dominant frequency of the signal, the sampled signal being output from the analog to digital converter as a sequence of data values; and at least one digital signal router that receives the sequence of data value and a sub-sampling rate, wherein the sub-sampling rate is lower than the streaming sample rate, and produces at least one sub-sampled output sequence of data comprising select samples from the sequence of samples based on at least one of the sub-sampling rate and a ratio of the streaming sample rate and the sub-sampling rate.

Methods and an expert system for processing a plurality of inputs collected from sensors in an industrial environment are disclosed. A modular neural network, where the expert system uses one type of neural network for recognizing a pattern relating to at least one of: the sensors, components of the industrial environment and a different neural network for self-organizing a data collection activity in the industrial environment is disclosed. A data communication network configured to communicate at least a portion of the plurality of inputs collected from the sensors to storage device is also disclosed.

A storage device of a noise inspection apparatus is configured to store a neural network machine-learned to receive, as inputs, an original sound characteristic value indicating a characteristic of sound generated by a transmission and an evaluation sound characteristic value indicating a characteristic of sound that reaches a vehicle cabin, and output a route part characteristic value that is a value indicating a characteristic of a vibration transfer of a vehicle part positioned on a vibration transfer route from the transmission to the vehicle cabin. An execution device of the noise inspection apparatus is configured to calculate, as an estimated value of the route part characteristic value, an output of the neural network that has received, as inputs, measured values of the original sound characteristic value and the evaluation sound characteristic value.

A reconfigurable network for interconnecting tools in a bottom hole assembly is disclosed. The network includes nodes that have reconfigurable switches that can be configured to provide a conductive path for the bus through the node, connect a terminator to the bus, and/or connect a tool to the bus. The exact configuration (i.e., states) of the switches in each node may be automatically selected based on a detected fault in a tool attached to a node and/or the state of other switches in the node or/a specific request is received. Various node embodiments and a method and circuit for automatically disconnecting a tool from the network in response to a tool fault are further disclosed.

In some embodiments, a load power monitoring system includes a distribution board to distribute a electric power applied from a external electric power supply source or a first renewable energy source to an electric device, at least one power metering device to sense electric energy of at least one of the electric power supply source and the first renewable energy source, a second power metering device to sense electric energy distributed to the electric device, a third power metering device to sense electric energy generated from a second renewable energy source, and a monitoring server to collect electric energy data sensed at each of the power metering devices and monitor the load power based on the collected electric energy data.

The present disclosure describes systems and methods for interpreting data from a plurality of input sensors, wherein each of the plurality of input sensors is operationally coupled to a component of an industrial environment. Methods including operating a self-organizing network on the data from the plurality of input sensors, thereby determining a structure in the data, determining a reduced dimensionality view of the data in response to the determined structure in the data, wherein the reduced dimensionality view includes fewer dimensions than the data from the plurality of input sensors, and providing the reduced dimensionality view to a user interface are disclosed together with systems therefore.

Devices, systems and methods for data acquisition in a food processing environment are disclosed. A device may include an analog switch, a data acquisition circuit to request and receive values corresponding to input sensors connected to the analog switch and associated with a food processing system and a data pool. A device may further include a data storage component to store specifications and anticipated state information for the input sensors, and a response circuit to provide direction regarding a manner in which detection values are requested, wherein the analog switch is adapted to selectively couple, in response to the anticipated state information, at least one of the plurality of inputs to at least one of the plurality of outputs.