An Automated Sensor Fusion Approach for the RUL Prediction of Electromagnetic Pumps

The remaining useful life (RUL) prediction of industrial cyber-physical system components demands the use of reliable prognostics parameters and frameworks. Against the traditional use of a single measure of degradation, data from multiple sensors provide abundant characteristic information for modeling, assessing, and extracting useful parameters via appropriate signal processing and sensor fusion methods. This study introduces a multi-sensor prognostics approach which merges highly prognosible statistical features from vibrational and pressure sensor measurements after a multi-level wavelet decomposition of the signals. The prognostic algorithm presented in this work for solenoid pump RUL prediction is a multi-objective genetic algorithm-optimized long short-term memory (MOGA-LSTM) which accepts the fused sensor features as input and returns the RUL of the pump as output. The framework was tested on a run-to-failure experiment on a VSC63A5 Solenoid pump following a significant pump malfunction caused by a clogged suction filter after the test. Using standard prognostic performance evaluation metrics, the performance of the prognostics framework was compared with other reliable state-of-the-art methods with a remarkable comparative advantage in addition to better automation potentials for real-time condition monitoring and RUL prediction.