The natural intermittent impulses created by impeller in case of centrifugal pump, and reciprocating motions of piston in case of axial pump exhibit strong cyclo-stationary phenomenon. This makes the identification of defect impulses difficult in their vibration signals even with the powerful signal processing techniques such as Fast-spectral correlation. To deal with the issue, this work proposes noise subtraction and marginal enhanced square envelope spectrum (MESES) for detecting bearing defects in the centrifugal and axial pump. For subtraction of noise, Fast Fourier Transform (FFT) of signal of unknown defect condition is computed and then FFT of normal working condition is subtracted from the signal of unknown defect condition. Then, inverse FFT is applied to resulting signal to construct denoised signal in time domain. The noise subtraction is done in frequency domain so as to avoid time lag problem which generally occurs in two signals obtained at different time. Further, the signal is processed by Fast- spectral correlation. To select the spectral frequency having fault related information, a criterion named as marginal band selection indicator (MBSI) is proposed. The spectral frequency band having the highest MBSI is selected for computing MESES. After selection of spectral frequency, MESES is computed and Feature frequency is find out. Finally, comparison of feature frequency is done with the bearing fundamental fault frequencies to conform the presence of defect.

离心泵叶轮产生的自然间歇脉冲和轴流泵活塞的往复运动表现出强烈的循环。- 平稳现象。这使得即使使用强大的信号处理技术（例如快速光谱相关）也难以识别振动信号中的缺陷脉冲。为了解决这个问题，这项工作提出了噪声减法和边际增强方形包络谱（MESES）来检测离心泵和轴流泵中的轴承缺陷。为了减去噪声，先计算未知缺陷状态信号的快速傅立叶变换（FFT），然后从未知缺陷状态信号中减去正常工作状态的FFT。然后，对结果信号应用逆 FFT 以构建时域中的去噪信号。噪声减除是在频域中进行的，以避免通常在不同时间获得的两个信号中出现的时滞问题。更远，信号通过快速频谱相关处理。为了选择具有故障相关信息的频谱频率，提出了一种称为边际频带选择指标（MBSI）的准则。选择具有最高MBSI的频谱频带来计算MESES。选择频谱频率后，计算MESES并找出特征频率。最后，将特征频率与轴承基本故障频率进行比较，以符合缺陷的存在。