Vibration Fault Diagnosis for Wind Turbine Based on Enhanced Supervised Locally Linear Embedding

2014 ◽  
Vol 1008-1009 ◽  
pp. 983-987
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Learning Algorithm ◽  
Operating Conditions ◽  
Fault Classification ◽  
Maintenance Cost ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis for wind turbine is an important task for reducing their maintenance cost. However, the non-stationary dynamic operating conditions of wind turbines pose a challenge to fault diagnosis for wind turbine. Fault diagnosis is essentially a kind of pattern recognition. In this paper, a novel fault diagnosis method based on enhanced supervised locally linear embedding is proposed for wind turbine. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Enhanced supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The wind turbine gearbox ball bearing vibration fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

Machinery Fault Diagnosis Based on Supervised Locally Linear Embedding

2014 ◽  
Vol 536-537 ◽  
pp. 49-52
Author(s):  
Xiang Wang ◽  
Yuan Zheng
Keyword(s):  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Classification Performance ◽  
Fault Classification ◽  
Locally Linear Embedding ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Low Dimensional ◽  
Machinery Fault Diagnosis ◽  
Locally Linear

Fault diagnosis is essentially a kind of pattern recognition. In this paper propose a novel machinery fault diagnosis method based on supervised locally linear embedding is proposed first. The approach first performs the recently proposed manifold learning algorithm locally linear embedding on the high-dimensional fault signal samples to learn the intrinsic embedded multiple manifold features corresponding to different fault modes. Supervised locally linear embedding not only can map them into a low-dimensional embedded space to achieve fault feature extraction, but also can deal with new fault samples. Finally fault classification is carried out in the embedded manifold space. The ball bearing fault signals are used to validate the proposed fault diagnosis method. The results indicate that the proposed approach obviously improves the fault classification performance and outperforms the other traditional approaches.

Supervised Locally Linear Embedding for Fault Diagnosis

2010 ◽  
Vol 139-141 ◽  
pp. 2599-2602
Author(s):  
Zheng Wei Li ◽  
Ru Nie ◽  
Yao Fei Han
Keyword(s):  
Fault Diagnosis ◽  
Recognition Performance ◽  
Feature Space ◽  
Difficult Problem ◽  
Fault Classification ◽  
High Dimensional ◽  
Locally Linear Embedding ◽  
Local Linear Embedding ◽  
Linear Embedding ◽  
Locally Linear

Fault diagnosis is a kind of pattern recognition problem and how to extract diagnosis features and improve recognition performance is a difficult problem. Local Linear Embedding (LLE) is an unsupervised non-linear technique that extracts useful features from the high-dimensional data sets with preserved local topology. But the original LLE method is not taking the known class label information of input data into account. A new characteristics similarity-based supervised locally linear embedding (CSSLLE) method for fault diagnosis is proposed in this paper. The CSSLLE method attempts to extract the intrinsic manifold features from high-dimensional fault data by computing Euclidean distance based on characteristics similarity and translate complex mode space into a low-dimensional feature space in which fault classification and diagnosis are carried out easily. The experiments on benchmark data and real fault dataset demonstrate that the proposed approach obtains better performance compared to SLLE, and it is an accurate technique for fault diagnosis.

Machinery fault diagnosis via an improved multi-linear subspace and locally linear embedding

2018 ◽  
Vol 40 (14) ◽  
pp. 4014-4026
Author(s):  
Yansheng Zhang ◽  
Dong Ye ◽  
Yuanhong Liu ◽  
Yu Cai
Keyword(s):  
Fault Diagnosis ◽  
Linear Subspace ◽  
Vibration Signal ◽  
Vector Model ◽  
Locally Linear Embedding ◽  
Support Vector ◽  
Multiple Sensors ◽  
Diagnosis Method ◽  
Linear Embedding ◽  
Locally Linear

Traditional fault diagnosis methods mainly depend on the vector model to describe a signal, which will lead to information loss and the curse of dimensionality. In order to overcome these problems, in this paper an improved multi-linear subspace (MLS) method and locally linear embedding (LLE) are integrated (MLSLLE) to extract significant features. To obtain more information, first it is suggested that multiple sensors should be used to sample the vibration signal of a machine from different positions; then, these data are projected into different subspaces, where each sample is represented as a tensor form, respectively; finally, higher-order singular value decomposition and LLE are introduced to extract significant features. Thus a fault diagnosis method is proposed based on MLSLLE and support vector machines. The advantages of the proposed fault diagnosis method are validated by two real bearing data sets.

scholarly journals Symbolic Important Point Perceptually and Hidden Markov Model Based Hydraulic Pump Fault Diagnosis Method

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4460 ◽  
Author(s):  
Yunzhao Jia ◽  
Minqiang Xu ◽  
Rixin Wang
Keyword(s):  
Fault Diagnosis ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hydraulic System ◽  
Hidden Markov ◽  
Operating Conditions ◽  
Fault Classification ◽  
Hydraulic Pump ◽  
Diagnosis Method ◽  
Original Time

Hydraulic pump is a driving device of the hydraulic system, always working under harsh operating conditions, its fault diagnosis work is necessary for the smooth running of a hydraulic system. However, it is difficult to collect sufficient status information in practical operating processes. In order to achieve fault diagnosis with poor information, a novel fault diagnosis method that is the based on Symbolic Perceptually Important Point (SPIP) and Hidden Markov Model (HMM) is proposed. Perceptually important point technology is firstly imported into rotating machine fault diagnosis; it is applied to compress the original time-series into PIP series, which can depict the overall movement shape of original time series. The PIP series is transformed into symbolic series that will serve as feature series for HMM, Genetic Algorithm is used to optimize the symbolic space partition scheme. The Hidden Markov Model is then employed for fault classification. An experiment involves four operating conditions is applied to validate the proposed method. The results show that the fault classification accuracy of the proposed method reaches 99.625% when each testing sample only containing 250 points and the signal duration is 0.025 s. The proposed method could achieve good performance under poor information conditions.

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