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

In prognostics and health management research for heavy vehicle electro-mechanical transmission, 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 modeling, multibody dynamic loading, modal superposition analysis, particle swarm optimization, and the modal assurance criterion. 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 particle swarm optimization procedure optimizes the off-diagonal elements of the modal assurance criterion matrix to determine a 12-sensor layout. Rig tests under six operating conditions show that the windowed Pearson correlation coefficient 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-related and bearing-related measurement regions over the six conditions.