标题： 基于相似性的旋转机械预测性维护框架 显示英文标题

标题： Similarity-Based Predictive Maintenance Framework for Rotating Machinery

摘要： 在智能制造中，数据驱动技术常用于旋转机械的工况监测和故障诊断。 经典方法使用监督学习，其中分类器在标记数据上进行训练，以预测或分类机器的不同运行状态。 然而，在大多数工业应用中，标记数据在数量和类型上都有限。 因此，它无法满足训练需求。 在本文中，通过将分类任务视为与参考样本的相似性度量，而不是监督分类任务来解决这个问题。 基于相似性的方法需要少量的标记数据，因此满足了现实工业应用的需求。 因此，本文引入了一个基于相似性的预测性维护（PdM）框架。 对于机器的每个运行状态，根据机器的运行条件生成并标记参考振动信号。 随后，使用统计时间分析、快速傅里叶变换（FFT）和短时傅里叶变换（STFT）从捕获的振动信号中提取特征。 对于每种特征类型，使用三种相似性度量，即结构相似性度量（SSM）、余弦相似性和欧几里得距离，以测量测试信号与特征空间中参考信号之间的相似性。 因此，评估了九种特征类型-相似性度量组合的设置。 实验结果证实，与基于机器学习（ML）的方法相比，基于相似性的方法在具有中等计算需求的情况下能够实现非常高的准确性。 此外，结果表明，使用FFT特征结合余弦相似性相较于其他设置会带来更好的性能。 显示英文摘要

摘要： Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to predict or classify different operational states of the machine. However, in most industrial applications, labeled data is limited in terms of its size and type. Hence, it cannot serve the training purpose. In this paper, this problem is tackled by addressing the classification task as a similarity measure to a reference sample rather than a supervised classification task. Similarity-based approaches require a limited amount of labeled data and hence, meet the requirements of real-world industrial applications. Accordingly, the paper introduces a similarity-based framework for predictive maintenance (PdM) of rotating machinery. For each operational state of the machine, a reference vibration signal is generated and labeled according to the machine's operational condition. Consequentially, statistical time analysis, fast Fourier transform (FFT), and short-time Fourier transform (STFT) are used to extract features from the captured vibration signals. For each feature type, three similarity metrics, namely structural similarity measure (SSM), cosine similarity, and Euclidean distance are used to measure the similarity between test signals and reference signals in the feature space. Hence, nine settings in terms of feature type-similarity measure combinations are evaluated. Experimental results confirm the effectiveness of similarity-based approaches in achieving very high accuracy with moderate computational requirements compared to machine learning (ML)-based methods. Further, the results indicate that using FFT features with cosine similarity would lead to better performance compared to the other settings.