The empirical mode decomposition (EMD) substitutes a signal′s average value with its envelope mean in a numerical algorithm, which causes a mode mixing problem and introduces analytical error. The synchrosqueezed wavelet transform (SWT) first reallocates the energy distribution according to the element modulus in the time-scale plane, then projects the time-scale domain onto the time-frequency plane to obtain more concentrated frequency curves. The SWT′s orthogonality along with its data-driven nature not only reduces the mode mixing effect but also improves the time-frequency resolution.The multi-component simulation example proves SWT′s superior capacity for time frequency characterization. A segment of misaligned displacement signal in rotating machinery is also tested. The energy distribution resulting from the SWT has sharper resolution in the time-frequency plane, in which the feature components are all concentrated in their time and frequency positions. Its outstanding characteristics make it a powerful tool for the condition monitoring and fault diagnosis of mechanical equipment.