Systems, apparatus, and methods for adjusting parameters in response to anticipated component state

A monitoring apparatus, comprising:

• a data collector including a controller, the data collector communicatively coupled to a plurality of input sensors operatively coupled to an industrial environment, and
• the controller including:
• a data collection band circuit structured to implement an adaptative sampling protocol that adjusts at least one collection parameter for at least one of the plurality of input sensors based on detected operational phase transitions from which to process output data, wherein the adaptive sampling protocol increases sampling rates during detected transient conditions and decreases sampling rates during normal operating conditions to optimize data storage while capturing critical events;
• a pattern recognition circuit structured to recognize at least one output data pattern and select a signature from a plurality of signatures associated with output data patterns;
• a machine learning data analysis circuit structured to receive the output data patterns from the at least one of the plurality of input sensors and the selected signature, wherein:
• the machine learning data analysis circuit is trained with a portion of the plurality of signatures associated with the output data patterns, including the selected signature to recognize one or more states, including one or more operational states and one or more anticipated states of the industrial environment;
• and
• the trained machine learning data analysis circuit continuously predicts multiple time-period anticipated states of the industrial environment based on the selected signature, which corresponds to an operational state of a component within the industrial environment, wherein the multiple time-period predictions enable both immediate and long-term operational planning;
• and
• a response circuit structured to adjust an operation of the component of the industrial environment based on the multiple time-period predicted anticipated states.

A method for data collection in an industrial environment, comprising:

• collecting sensor data from a plurality of input sensors operatively coupled to the industrial environment, the plurality of input sensors communicatively coupled to a data collector;
• implementing an adaptive sampling protocol that adjusts at least one collection parameter for at least one of the plurality of input sensors from which to process output data based on the collected sensor data, wherein the at least one collection parameter alters a physical collecting of sensor data by adjusting, for at least one sensor, at least one of:
• a bandwidth for sensor data, a multiplexing configuration, a timing parameter, a frequency range, or a granularity of collection of sensor data;
• receiving the collected sensor data from the at least one of the plurality of input sensors;
• performing a pattern recognition operation on the collected sensor data to recognize at least one output data pattern and select a signature from a plurality of signatures associated with output data patterns;
• performing a machine learning operation to train a neural network with the signatures associated with the output data patterns, including the selected signature to recognize one or more states of a component of the industrial environment;
• continuously predicting multiple time-period anticipated states of the industrial environment using the trained neural network based on the selected signature which corresponds to an operational state of the component within the industrial environment;
• and
• adjusting an operation of the component of the industrial environment in response to the predicted anticipated state of the component.