Vibration analysis for predictive maintenance and improved reliability of rotating machines in ETRR-2 research reactor

Abstract

Abstract In this work, both hardware and software modifications in a typical research reactor protection system (RPS) is proposed. The reactor cooling pumps are tripped based on vibrations safety signals of the pumps while the reactor SCRAM signal is initiated based on low flow rate and pressure drop across the reactor core which is a direct result of pumps trip. The main objective of this work is to develop reactor SCRAM signal based of core cooling pumps vibration signals. The early shutdown of the reactor based on pumps vibration signals is of significant importance not only in cooling the decay power of the reactor core after shutdown but also to prevent pumps failure. In the hardware model, the core cooling pumps vibration signals are feed to RPS to initiate reactor SCRAM signal. In the software model, a modular artificial neural network (ANN) is used in modeling the vibration monitoring of the research reactor (ETRR-2). The input and the output signals of the vibration transducer are used as a source data for training the neural network model. The type of the network used in this methodology is the supervised Multilayer Feed-Forward Neural Networks with the back-propagation (BP) algorithm. Vibration analysis programs are used in research reactors (RRs) to identify faults in machinery, plan machinery repairs, and keep machinery functioning for as long as possible without failure. The vibration severity limits are determined based on the International Organization for Standardization (ISO) 10816. The ANNs were designed using two different methods; one is by using hardware application contained two out of three voting and dynamic modules for trip signal by using ANNs. The current model classifies the vibration signals into five ranges low, good, satisfactory, unsatisfactory, and unacceptable vibration. The ANN is trained to detect the signal and vote to take the correct and safe action. The results demonstrate that the ANN can help in taking predictive actions for the safe core coolant pumps operation.