Monitoring Methods, Computer Program Product, Monitoring Unit, Gas Analysis Device, and use of Artificial Intelligence

A monitoring method for a system having a plurality of devices, which form nodes in a neural network, which maps an interaction of the devices in the system, the method comprising:

• a) providing a trained neural network having a plurality of edges which each correspond to a zero correlation or an operating correlation, a correlation value being determined during training for zero correlations, and the correlation value having a value which is below an adjustable correlation threshold value;
• b) placing the system into an active operating state and capturing at least one of measured values and control commands, each measured value and control command corresponding to least one of (i) at least one node and (ii) at least one edge of the neural network;
• c) determining a network deviation parameter which combines correlation values of a plurality of edges of the neural network, the correlation values being determined utilizing at least one of the measured values and control commands captured during step b);
• d) identifying an abnormal operating state of the system, if a value of the network deviation parameter exceeds an adjustable network threshold value, a warning being issued if the abnormal operating state is identified;
• and
• e) identifying an edge of the neural network which corresponds to the zero correlation in a normal operating state as a defective edge, if a value of the correlation value associated with the identified edge exceeds the associated correlation threshold value.

a non-transitory computer readable medium encoded with a computer program which, when executed by processor of a monitoring unit for monitoring a system which has a plurality of devices which interact to operate a system, causes at least one of measured values and control commands to be captured and processed, the program comprising:

• a) program code for establishing a trained neural network having a plurality of edges which each correspond to a zero correlation or an operating correlation, a correlation value being determined during training for zero correlations, said correlation value have a value which is below an adjustable correlation threshold value;
• b) program code for placing the system into an active operating state and capturing at least one of measured values and control commands, each measured value and control command corresponding to least one of (i) at least one node and (ii) at least one edge of the neural network;
• c) program code for determining a network deviation parameter which combines correlation values of a plurality of edges of the neural network, the correlation values being determined utilizing at least one of the measured values and control commands captured during step b);
• d) program code for identifying an abnormal operating state of the system, if a value of the network deviation parameter exceeds an adjustable network threshold value, a warning being issued if the abnormal operating state is identified;
• and
• e) program code for identifying an edge of the neural network which corresponds to the zero correlation in a normal operating state as a defective edge, if a value of the correlation value associated with the identified edge exceeds the associated correlation threshold value.

An artificial intelligence module for monitoring a gas analysis device;

• wherein the artificial intelligence at least issues a warning during use and implements a monitoring method;
• and
• wherein the monitoring method comprises:
• a) providing a trained neural network having a plurality of edges which each correspond to a zero correlation or an operating correlation, a correlation value being determined during training for zero correlations, said correlation value have a value which is below an adjustable correlation threshold value;
• b) placing the system into an active operating state and capturing at least one of measured values and control commands, each measured value and control command corresponding to least one of (i) at least one node and (ii) at least one edge of the neural network;
• c) determining a network deviation parameter which combines correlation values of a plurality of edges of the neural network, the correlation values being determined utilizing at least one of the measured values and control commands captured during step b);
• d) identifying an abnormal operating state of the system, if a value of the network deviation parameter exceeds an adjustable network threshold value, a warning being issued if the abnormal operating state is identified;
• and
• e) identifying an edge of the neural network which corresponds to the zero correlation in a normal operating state as a defective edge, if a value of the correlation value associated with the identified edge exceeds the associated correlation threshold value.