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首页> 外文期刊>Mechanical systems and signal processing >The local maxima method for enhancement of time-frequency map and its application to local damage detection in rotating machines
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The local maxima method for enhancement of time-frequency map and its application to local damage detection in rotating machines

机译：增强时频图的局部最大值方法及其在旋转机械局部损伤检测中的应用

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In this paper a new metnod or fault detection in rotating machinery is presented. It is based on a vibration time series analysis in time-frequency domain. A raw vibration signal is decomposed via the short-time Fourier transform (STFT). The time-frequency map is considered as matrix (M×N) with N sub-signals with length M. Each sub-signal is considered as a time series and might be interpreted as energy variation for narrow frequency bins. Each sub-signal is processed using a novel approach called the local maxima method. Basically, we search for local maxima because they should appear in the signal if local damage in bearings or gearbox exists. Finally, information for all sub-signals is combined in order to validate impulsive behavior of energy. Due to random character of the obtained time series, each maximum occurrence has to be checked for its significance. If there are time points for which the average number of local maxima for all sub-signals is significantly higher than for the other time instances, then location of these maxima is "weighted" as more important (at this time instance local maxima create for a set of △f a pattern on the time-frequency map). This information, called vector of weights, is used for enhancement of spectrogram. When vector of weights is applied for spectrogram, non-informative energy is suppressed while informative features on spectrogram are enhanced. If the distribution of local maxima on spectrogram creates a pattern of wide-band cyclic energy growth, the machine is suspected of being damaged. For healthy condition, the vector of the average number of maxima for each time point should not have outliers, aggregation of information from all sub-signals is rather random and does not create any pattern. The method is illustrated by analysis of very noisy both real and simulated signals.
机译：本文提出了一种新的旋转机械方法或故障检测方法。它基于时频域中的振动时间序列分析。原始振动信号通过短时傅立叶变换（STFT）分解。时频图被视为具有N个子信号且长度为M的矩阵（M×N）。每个子信号均被视为时间序列，并可能被解释为窄频点的能量变化。每个子信号都使用一种称为局部最大值方法的新颖方法进行处理。基本上，我们搜索局部最大值，因为如果轴承或齿轮箱中存在局部损坏，则它们应该出现在信号中。最后，将所有子信号的信息组合在一起，以验证能量的脉冲行为。由于获得的时间序列具有随机性，因此必须检查每个最大出现的重要性。如果存在所有子信号的局部最大值的平均时间显着高于其他时间实例的时间点，则将这些最大值的位置“加权”为更重要的（此时，为一个子对象创建局部最大值）。在时频图上设置△fa模式）。该信息称为权重向量，用于增强频谱图。当权重向量应用于谱图时，非信息能量被抑制，而谱图上的信息特征得到增强。如果频谱图上的局部最大值分布产生宽带循环能量增长的模式，则怀疑机器已损坏。对于健康状况，每个时间点的最大平均值的向量不应具有离群值，来自所有子信号的信息聚集是相当随机的，不会创建任何模式。通过分析非常嘈杂的真实和模拟信号来说明该方法。

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