Lu Chen, Wang Yang, Ragulskis Minvydas, Cheng Yujie
School of Reliability and Systems Engineering, Beihang University, Xueyuan Road No.37, Haidian District, Beijing, China.
Science & Technology on Reliability and Environmental Engineering Laboratory, Xueyuan Road No.37, Haidian District, Beijing, China.
PLoS One. 2016 Oct 6;11(10):e0164111. doi: 10.1371/journal.pone.0164111. eCollection 2016.
Rotating machinery is one of the most typical types of mechanical equipment and plays a significant role in industrial applications. Condition monitoring and fault diagnosis of rotating machinery has gained wide attention for its significance in preventing catastrophic accident and guaranteeing sufficient maintenance. With the development of science and technology, fault diagnosis methods based on multi-disciplines are becoming the focus in the field of fault diagnosis of rotating machinery. This paper presents a multi-discipline method based on image-processing for fault diagnosis of rotating machinery. Different from traditional analysis method in one-dimensional space, this study employs computing method in the field of image processing to realize automatic feature extraction and fault diagnosis in a two-dimensional space. The proposed method mainly includes the following steps. First, the vibration signal is transformed into a bi-spectrum contour map utilizing bi-spectrum technology, which provides a basis for the following image-based feature extraction. Then, an emerging approach in the field of image processing for feature extraction, speeded-up robust features, is employed to automatically exact fault features from the transformed bi-spectrum contour map and finally form a high-dimensional feature vector. To reduce the dimensionality of the feature vector, thus highlighting main fault features and reducing subsequent computing resources, t-Distributed Stochastic Neighbor Embedding is adopt to reduce the dimensionality of the feature vector. At last, probabilistic neural network is introduced for fault identification. Two typical rotating machinery, axial piston hydraulic pump and self-priming centrifugal pumps, are selected to demonstrate the effectiveness of the proposed method. Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.
旋转机械是最典型的机械设备类型之一，在工业应用中发挥着重要作用。旋转机械的状态监测与故障诊断因其在预防灾难性事故和保证充分维护方面的重要性而受到广泛关注。随着科学技术的发展，基于多学科的故障诊断方法正成为旋转机械故障诊断领域的研究热点。本文提出了一种基于图像处理的旋转机械故障诊断多学科方法。与传统的一维空间分析方法不同，本研究采用图像处理领域的计算方法，在二维空间中实现自动特征提取和故障诊断。所提出的方法主要包括以下步骤。首先，利用双谱技术将振动信号转换为双谱等高线图，为后续基于图像的特征提取提供依据。然后，采用图像处理领域中一种新兴的特征提取方法——加速鲁棒特征，从转换后的双谱等高线图中自动提取故障特征，最终形成高维特征向量。为了降低特征向量的维度，突出主要故障特征并减少后续计算资源，采用t分布随机邻域嵌入来降低特征向量的维度。最后，引入概率神经网络进行故障识别。选择两种典型的旋转机械——轴向柱塞液压泵和自吸离心泵，来验证所提方法的有效性。结果表明，所提出的基于图像处理的方法具有较高的准确率，从而为旋转机械故障诊断提供了一种高效的手段。
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