Indirect methods to monitor the surface integrity of grinding wheels by acoustic emission (AE) have been proposed, aiming to ensure their optimal performance. However, the time-frequency analysis of the content of these signals has not been addressed in the literature. AE signal analysis performed only in the frequency domain makes it impossible to locate faults on the grinding wheel surface during the dressing operation and examine the behavior of the frequencies contained in these signals over time. In this regard, the time-frequency analysis of AE signals during dressing through STFT (short-time Fourier transform) can contribute toward the proposal of new monitoring methodologies, thus reflecting the optimization of the grinding process. This paper proposes an algorithm based on the Kaiser window to adjust the STFT parameters to ensure an appropriate balance between time-frequency resolutions. Besides, this algorithm is used to investigate the characteristic frequencies in the aluminum oxide grinding wheel in dressing operation. The results indicate that the spectral content of the AE signals during dressing follows a uniform behavior, but their amplitude changes depending on the characteristics of topography and sharpness of the grinding wheel cutting edges.

为了确保砂轮的最佳性能，已经提出了间接方法来通过声发射（AE）监控砂轮的表面完整性。但是，这些信号的内容的时频分析尚未在文献中讨论。仅在频域中进行的AE信号分析无法在修整操作期间定位砂轮表面上的故障，也无法检查这些信号随时间变化的频率行为。在这方面，通过STFT（短时傅立叶变换）在修整过程中对AE信号进行时频分析可有助于提出新的监测方法，从而反映出研磨过程的优化。本文提出了一种基于Kaiser窗口的算法，用于调整STFT参数，以确保时频分辨率之间的适当平衡。此外，该算法还用于研究修整操作中氧化铝砂轮的特征频率。结果表明，修整过程中AE信号的频谱内容遵循统一的行为，但其幅度会根据地形特征和砂轮切削刃的锋利度而变化。