Fault Detection of Electrical Yield Meter Based on Electrical Resistance Tomography

As an advanced detection technique, the electrical yield meter (EYM) based on electrical resistance tomography has increasingly been applied in various industry fields. But EYM often works under high temperature differences, strong vibrations, high friction, and great corrosions, thus the faults are random and inevitable. How to effectively detect and find EYM faults remains unresolved, while the measurements of EYM failures/faults can provide a way to distinguish the faults themselves in practice. In this paper, an experimental platform is firstly built to simulate and construct the actual EYM working process, and thereby different types of EYM faults are reconstructed to obtain a set of sufficient normal and abnormal measurements. After performing the principal component analysis to capture the key features hidden in measurements, all faults are divided to six clusters by a typical c-means clustering algorithm. Therefore, a fault detection method is created along different measuring conditions and flowing patterns. Experimental evaluation in practical engineering shows that the proposed method can quickly and effectively distinguish fault types through actual measurements. Consequently, the proposed fault detection method is feasible and effective to ensure the relief and stable EYM measurement in practice.