Fault Detection of Piezoelectric Array Element Using Acoustic Field and Particle Swarm Optimization

Medical ultrasonic array transducers are prone to various defects due to hardware malfunction, mechanical damages, aging, and fatigue issues. Faulty elements result in distorted acoustic field, higher side lobe level (SLL), and image resolution degradation. Fault detection of piezoelectric array element is an obvious and important prerequisite for any restoration or compensative reaction. In this work, an inverse optimization approach on the few measured samples of the radiated acoustic field is proposed to estimate the contribution of the element, its position and severity of its faulty condition. The proposed method is evaluated by 100 random simulated test datasets, based on finite element model (FEM) of a linear array transducer. Three element faulty types inclusive of Intact, Weak, and Dead, are considered in the datasets to measure a lateral profile of the radiated far-field acoustic field from the transducer. The problem on the acoustic field is solved by Particle Swarm Optimization (PSO) algorithm. The high detection accuracy of about 99%, as depicted in the results section, demonstrates the effectiveness of this method to detect Weak and Dead elements. The proposed method outperforms the electrical test equipment to check the sensitivity and capacitance of individual elements, especially for the 2D transducers containing large number of elements and physically unavailable sub-elements for the electrical tests.