Rotating machines are one of the most common equipment in modern industry, the health condition of the equipment is closely linked to safety of workers and production effectiveness. Thus accurate and robust fault diagnostic approaches are vital to safety production. In practice, diagnostic accuracy is seriously affected by noises, especially in low signal-to-noise (SNR) ratio conditions, and the quality of fault features is positively link to the diagnosing accuracy. In consideration of distinguishable feature expression can improve diagnosing result and robust to wider range of experimental conditions, this paper presents a novel spectrogram based local fluctuation feature (SLFF) for low SNR conditions. Firstly, signals are transformed into spectrograms. Then a feature extracting window bank is established on spectrograms for SLFF. At last, a support vector machine (SVM) is applied as a fault classifier for evaluating the proposed feature. The proposed SLFF represents the basic spectral shape and variation which leads to robust and well distinguishable feature expression, the feature reveals the differences of spectral local variation trends between normal and fault types that can improve the discrimination under the influence of strong noises. The effectiveness of the proposed method has been proved experimentally in this paper.

旋转机械是现代工业中最常见的设备之一，设备的健康状况与工人的安全和生产效率息息相关。因此，准确而可靠的故障诊断方法对于安全生产至关重要。在实践中，诊断准确性会受到噪声的严重影响，尤其是在低信噪比（SNR）的情况下，并且故障特征的质量与诊断准确性有着积极的联系。考虑到可区分的特征表达可以改善诊断结果，并在更大范围的实验条件下具有较强的鲁棒性，本文针对低信噪比条件提出了一种新颖的基于频谱图的局部涨落特征（SLFF）。首先，信号被转换成频谱图。然后，在频谱图上为SLFF建立特征提取窗口库。终于，支持向量机（SVM）被用作故障分类器，以评估提出的特征。提出的SLFF代表了基本的频谱形状和变化，从而导致了稳健且可区分的特征表达，该特征揭示了正常类型和断层类型之间频谱局部变化趋势的差异，可以改善在强噪声影响下的分辨力。本文通过实验证明了该方法的有效性。该特征揭示了正常类型和断层类型之间频谱局部变化趋势的差异，可以改善在强噪声影响下的辨别力。实验证明了该方法的有效性。该特征揭示了正常类型和断层类型之间频谱局部变化趋势的差异，可以改善在强噪声影响下的辨别力。本文通过实验证明了该方法的有效性。