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Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model

机译：基于位移不变字典学习和隐马尔可夫模型的轴承故障检测与诊断

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摘要

Many existing signal processing methods usually select a predefined basis function in advance. This basis functions selection relies on a priori knowledge about the target signal, which is always infeasible in engineering applications. Dictionary learning method provides an ambitious direction to learn basis atoms from data itself with the objective of finding the underlying structure embedded in signal. As a special case of dictionary learning methods, shift-invariant dictionary learning (SIDL) reconstructs an input signal using basis atoms in all possible time shifts. The property of shift-invariance is very suitable to extract periodic impulses, which are typical symptom of mechanical fault signal. After learning basis atoms, a signal can be decomposed into a collection of latent components, each is reconstructed by one basis atom and its corresponding time-shifts. In this paper, SIDL method is introduced as an adaptive feature extraction technique. Then an effective approach based on SIDL and hidden Markov model (HMM) is addressed for machinery fault diagnosis. The SIDL-based feature extraction is applied to analyze both simulated and experiment signal with specific notch size. This experiment shows that SIDL can successfully extract double impulses in bearing signal. The second experiment presents an artificial fault experiment with different bearing fault type. Feature extraction based on SIDL method is performed on each signal, and then HMM is used to identify its fault type. This experiment results show that the proposed SIDL-HMM has a good performance in bearing fault diagnosis.
机译：许多现有的信号处理方法通常预先选择预定的基函数。这种基本功能的选择依赖于有关目标信号的先验知识，而这在工程应用中总是不可行的。字典学习方法为从数据本身学习基础原子提供了一个雄心勃勃的方向，目的是找到嵌入信号中的基础结构。作为字典学习方法的一种特殊情况，不变位移字典学习（SIDL）在所有可能的时移中使用基本原子来重建输入信号。位移不变性的性质非常适合提取周期性脉冲，这些脉冲是机械故障信号的典型症状。在学习了基本原子之后，信号可以分解为一组潜在成分，每个潜在成分都由一个基本原子及其对应的时移重建。本文介绍了SIDL方法作为一种自适应特征提取技术。然后提出了一种基于SIDL和隐马尔可夫模型（HMM）的有效的机械故障诊断方法。基于SIDL的特征提取可用于分析具有特定陷波大小的仿真信号和实验信号。该实验表明，SIDL可以成功提取方位信号中的双脉冲。第二个实验提出了一种具有不同轴承故障类型的人工故障实验。对每个信号进行基于SIDL方法的特征提取，然后使用HMM识别其故障类型。实验结果表明，所提出的SIDL-HMM在轴承故障诊断中具有良好的性能。

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