Identification Method of Shaft Orbit in Rotating Machines Based on Accurate Fourier Height Functions Descriptors

Abstract

In this paper, an algorithm based on two novel shape descriptors and support vector machine (SVM) is proposed to improve the recognition accuracy and speed of shaft orbits of rotating machines. Firstly, two novel shape descriptors, respectively, named accurate Fourier height functions 1 (AFHF1) and accurate Fourier height functions 2 (AFHF2) are presented based on height function (HF) and Fourier transformation. Both AFHF1 and AFHF2 shape descriptors are constant to similarity transforms and also have intrinsic invariance to the starting point change and are more compacted than HF. Therefore, they perform well on the global or local features of the contours of shaft orbits. Then, the AFHF1 and AFHF2 shape descriptors are utilized to extract features of shaft orbits in the simulated dataset and measured dataset. Taking extracted feature vectors as the input, SVM is adopted in order to classify the fault types according to the shapes of shaft orbits. Finally, a series of descriptors including shape context (SC), inner-distance shape context (IDSC), triangular centroid distances (TCDs), and HF were compared to verify the performance of the proposed AFHF1 and AFHF2 shape descriptors. The average accuracy of our method in simulated dataset and measured dataset are all higher than 99.83%, the average recognition time of each sample is no more than 19 milliseconds. The experiments demonstrate that the proposed method has the best recognition accuracy and real-time and antinoise performance.

Fine-grained fault recognition method for shaft orbit of rotary machine based on convolutional neural network

“…Table 1 shows the corresponding relationship between shaft orbit shapes and the fault types [5]. The shaft orbits are simulated according to [12], and a series of variables are introduced to refine the classification of different shaft orbits. (1) The slenderness is defined as / , where and represent the short and long axes of the graph, respectively.…”

“…However, it is always a difficult problem to choose the appropriate feature descriptors and the corresponding classifier. The common feature descriptors used in the identification of shaft orbit are Fourier descriptors (FD) [7], chain code [8], Walsh descriptor (WD) [9], Hu invariant moment [10], histogram of oriented gradients (HOG) [11], comprehensive geometric characteristic (CGC) [2] and accurate Fourier height functions (AFHF) [12]. The commonly used classifiers are BP neural network [10] and support vector machine (SVM) [12].…”

Fault Diagnosis of Rotating Machinery Based on Convolutional Neural Network and Singular Value Decomposition

“…e diagnosis heavily depends on the expert knowledge or experience. e common methods are Hu invariant moment [13], Fourier descriptor [14], Walsh transform [15], and so on, but these methods mainly rely on the mathematical transformation of graphics and cannot accurately extract the features of the image. In the recent years, with the rise of deep learning [16], deep learning techniques represented by convolutional neural networks (CNNs) have shown particular advantages and potentials in image feature extraction and recognition.…”

Novel Orbit-based CNN Model for Automatic Fault Identification of Rotating Machines

“…In the past few years artificial intelligence methods have been exploited to utilize the shaft orbits for automatic fault identification and classification of turbomachinery (e.g., Carbajal-Herná ndez et al 2016, Jeong et al 2016, Wu et al 2018, Khodja et al 2019. Carbajal-Herná ndez et al (2016) developed the Lernmatrix associative memory approach associated with orbital pattern recognition to classify the imbalance and misalignment faults of induction motors.…”

“…The laboratory testing data were used in the methodology demonstration. Wu et al (2018) presented two shape description symbols from the orbits by using high functions and Fourier transform which are used in support vector machine (SVM) approach for fault detection of turbomachine. Khodja et al (2019) also implemented the CNN deep learning model to classify the bearing faults of rotating machines based on the orbit images generated from the time-frequency features of the vibration signals.…”