ANN based Sensor Faults Detection, Isolation, and Reading Estimates – SFDIRE: Applied in a nuclear process

In this paper a new method is developed based on the capabilities of the Artificial Neural Networks (ANNs) to detect, isolate, and output an estimated reading of faulty sensor/s reading/s that serves in monitoring a complex process. For a number of measured signals working to monitor a process, we allow training of an equal number of ANN’s to those signals. Each ANN has an output signal equal to one corresponding measured signal and the set of inputs is the rest of other measured signals and other networks output signals using two-hidden layer feedforward network with different number of neurons for each. After the measuring the error between the real signal and the ANN output, the faulty sensor can be detected. The reading of this faulty sensor could be isolated and an estimated reading is sent to the monitoring system. This method could be applied for any difficult to model process to increase its availability and in the same time maintaining safety. In this paper, the application considered is the thermal–hydraulics process of Egyptian Training and Research Reactor-2 (ETRR-2) core cooling system. Data from the reactor are used to train the networks using backpropagation algorithm, An approximated model based on the lumped parameters approach for the thermal–hydraulics of the reactor core is built to compare results against ANN based system. The proposed system deals with 6 signals. From comparison, very good results are achieved when testing the design.

► A sensor faults detection, isolation, and correct reading estimate system was built.
► It is used with any hard to model process to increase its availability of operation.
► Matlab Simulink helps in simulating the system process and extracting learning data.
► The design algorithm of SFDIRE applied on ETRR-2 core cooling system sensors.
► The proposed system had successfully proved.