Due to manufacturing tolerance and deterioration during operation, different blades in a fan assembly exhibit geometric variability. This leads to asymmetry which will be amplified in the running geometry by centrifugal and aerodynamic loads. This study investigates a phenomenon known as Alternate Passage Divergence (APD), where the blade untwist creates an alternating pattern in passage geometry and stagger angle around the circumference. After the formation of alternating tip stagger pattern, APD’s unsteady effect, APD-induced Non-Synchronous Vibration (APD-NSV, abbreviated as NSV), can cause the blades from one group to switch to the other creating a travelling wave pattern around the circumference. Thus, it can potentially lead to high cycle fatigue issues. More importantly, this phenomenon occurs close to, or at, peak efficiency conditions and can significantly reduce overall efficiency. Therefore, it is vital to attenuate the NSV behaviour. The random nature of mis-staggering patterns complicate the evolution of NSV significantly. Thus, machine learning techniques are used to analyse mis-stagger patterns to identify patterns that can lead to NSV and thus help avoid it. Numerical results from 113 numerical cases (1.6 million CPU hours) are used to train and test the classifier. From the results, two parameters contributing to NSV behaviour have been identified with one of them enhancing the understanding found in the previous study.