PSO-MAC-Based Optimization of Vibration Sensor Placement for Heavy Vehicle Electromechanical Transmission

Abstract

In prognostics and health management (PHM) research for heavy vehicle electromechanical transmission (EMT), vibration signal dataset construction faces challenges of poor acquisition quality and low signal-to-noise ratio, leading to difficulties in fault feature extraction and unsatisfactory diagnosis performance. This study develops an operating-condition-informed sensor-placement framework integrating calibrated finite-element (FE) modeling, multibody dynamic loading, modal superposition analysis, particle swarm optimization (PSO), and the modal assurance criterion (MAC). The transmission-housing models are updated using impact hammer modal tests, and modal consistency is evaluated by comparing experimental and numerical mode shapes. Dynamic bearing loads under rated conditions are then obtained from a multibody model and applied together with the actual mounting constraints. Candidate locations are screened from the resulting mode shapes, and a discrete PSO procedure optimizes the off-diagonal elements of the MAC matrix to determine a 12-sensor layout. Rig tests under six operating conditions show that the windowed Pearson correlation coefficient (WPCC) of the gear-related pair ranges from 0.583 to 0.784, while the mean gear-bearing coefficient remains between −0.030 and 0.007. The correlation-based partitions yield mean silhouette coefficients of 0.326–0.514. The gear group remains in the same cluster under all six conditions, whereas the internal relationships among the bearing-related channels vary with the conditions. The results demonstrate that the selected placement retains common structural-response information while providing stable differentiation between the gear- and bearing-related measurement regions over the six conditions.