中文摘要：

自适应最稀疏时频分析（adaptive and sparsest time-frequency analysis，AST—FA）是一种新的时频分析方法，该方法需要事先确定较为准确的初始值，缺乏自适应性．针对ASTFA存在的问题，提出了基于初值优化的ASTFA方法．该方法使用残余量的能量作为优化目标函数，使用不同的初始值对信号进行分解，当残余量的能量最小时，则认为该初始值为最优初始值．因此，该方法能够自适应地寻找最优的初始值，增加了ASTFA方法的自适应性．采用仿真信号将该方法与原ASTFA方法进行对比，结果表明该方法能自适应地得到更准确的分解结果．对仿真信号和滚动轴承故障数据进行分析，结果表明ASTFA在抑制端点效应和模态混淆、抗噪声性能、提高分量的准确性等方面要优于经验模态分解（empirical mode decomposition，EMD），并能有效应用于滚动轴承故障诊断．

英文摘要：

Adaptive and sparsest time-frequency analysis（ASTFA） is a new method for time-frequency analysis.ASTFA is lack of adaptivity as comparatively accurate initial values have to be set beforehand.Ai- ming to solve the problem existed in ASTFA, adaptive and sparsest time-frequency analysis method based on initial value optimization was proposed.The energy value of the residue is applied as the optimization ob- jective function,and different initial values are used for signal decomposition.Initial values are considered to be the best only if the energy value of the corresponding residue is the smallest.Therefore, the adaptivity of ASTFA method is improved by the proposed method as the best initial values can be found adaptively. Simulation signal is applied to compare the proposed method and the initial ASTFA method. The results show that more accurate decomposition results can be adaptively obtained by using the proposed method. Analysis of simulation signal and rolling bearing fault signal shows that compared with empirical mode de- composition（EMD）method, the proposed method is superior at least in restraining end effect and mode mixing, anti-noise performance and gaining more accurate components.Meanwhile, the proposed method is effective in rolling bearing fault diagnosis.