The Importance of Intelligent Sensing and Control

SAM Controllers delivers full automation, optimized machine performance, self-reporting predictive failure and embedded operational analytics to any existing machine in an easy to install retrofit kit.

 

This is done through powerful edge computing, intelligent sensing and Real-Time control modeling of machines as they operate using direct measurement of electricity for instant feedback on all actions.

 

All machines, big and small powered by electrical energy create unique electrical consumption signatures. The amount that these signature reveal is commensurate with the intensity of signature analysis.

 

A cursory examination of a machine’s electrical signature, for example Watts every 15 minutes provides some information on the machine. When this information is tracked, it reveals the daily energy consumption patterns. Tracking and comparing this information over time reveals trends and patterns in energy consumption that can indicate wear and tear in the system. All of this from collecting one rough data point every 15 minutes.

 

An intensive analysis of a machines electrical signature reveals every bit of the internal operations of a machine because everything within the machine consumes energy to perform its function, whether mechanical or electromechanical and must be reflected in the electrical signature because it is the source of the energy.

 

Intelligent sensing is based on the premise that real data is the most valuable part of any control system. In any machine operation, something real is occurring, and an intelligent control system must be aware of it all, unencumbered by lack of data or errors in sensing.

 

SAM Controllers leverages this new layer of data processing in the sensors, at the point of data acquisition using the mathematical principle of oversampling by inputting much more data than is needed on each channel and rapidly processing it into an error free data input stream at the needed frequency.

 

If the control system requires error free data inputs every 10 microseconds, the intelligent sensing must complete all the data acquisition, conversion, preparation, processing, calibration and verification before that. If the system requires data inputs of 1 microsecond, the intelligent sensing must perform those tasks before that to supply error free data every microsecond.

 

When intelligent sensing is incorporated, Real-Time modeling becomes possible, where all system sensors and inputs are perfectly synchronized at the point of input, eliminating timing as a variable and providing an exact model of the system operation at all times.

 

A human does not always focus on their elbow, but will if it is required. The data input must always be going to the brain giving the human an option to choose whether or not to focus on the elbow. The human knows that the sensing input is always there and reliable. Similarly, SAM Controllers are microprocessor based control systems that deliver error free, reliable data upon input allowing the controller to maintain passive awareness of all processes only engaging active control when required.

 

Mechanical machines like pumps are designed to be autonomous, however they are not intelligent. Once turned on, they will blindly follow the mechanical pressure set points and continue to pump until the pressure switch is triggered. If there is a line or pipe rupture, the pump doesn’t know. It will continue to operate until it burns out because it has no awareness of the system. There is no real time feedback on the actual operation.

 

This lack of embedded awareness is a significant liability for anybody who relies on this pump. Embedding Real-Time awareness into the system controller delivers expert level operational decision making at all times into a small, durable control system using state of the art artificial intelligence and machine learning.

 

The SAM Controller approach of intelligent sensing and controls improves the performance and efficiency of machines, processes and systems, delivering autonomy, through self-reporting of failure and embedded operational analytics.