MECHANICAL FAULT RECOGNITION RESEARCH BASED ON LMD-LSSVM

Mechanical fault vibration signals are non-stationary, which causes system instability. The traditional methods are difficult to accurately extract fault information and this paper proposes a local mean decomposition and least squares support vector machine fault identification method. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

Download Full-text

Research of Mechanical Vibration Signal Classification Based on LMD

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.350 ◽

2015 ◽

Vol 764-765 ◽

pp. 350-358

Author(s):

Zeng Shou Dong ◽

Zhao Jing Ren ◽

You Dong

Keyword(s):

Signal Processing ◽

Support Vector Machines ◽

Least Squares ◽

Signal Sequence ◽

Adaptive Signal Processing ◽

Experimental Result ◽

Support Vector ◽

Local Mean Decomposition ◽

Vector Machines ◽

Local Mean

The traditional signal processing methods are difficult to accurately extract fault information, because mechanical fault vibration signals have non-stationary, which will cause system instability. Local mean decomposition is adaptive signal processing method. However, in the local mean decomposition of the signal, the trend of the endpoint can not be predicted which cause contaminating the entire signal sequence, the original moving average of the signal used over-smoothing treatment, resulting in fault characteristics can not accurately extract. The article introduces waveform matching to solve the original features of signals at the endpoints, using linear interpolation to get local mean and envelope function, then obtain production function PF vector through making use of the local mean decomposition. The energy entropy of PF vector take as identification input vectors. These vectors are respectively inputted BP neural networks, support vector machines, least squares support vector machines to identify faults. Experimental result show that the accuracy of least squares support vector machine with higher classification accuracy has been improved.

Download Full-text

Study on Prediction of Chaotic Time Series Using Least Squares Support Vector Machines

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1061-1062.935 ◽

2014 ◽

Vol 1061-1062 ◽

pp. 935-938

Author(s):

Xin You Wang ◽

Guo Fei Gao ◽

Zhan Qu ◽

Hai Feng Pu

Keyword(s):

Time Series ◽

Support Vector Machine ◽

Support Vector Machines ◽

Least Squares ◽

Prediction Accuracy ◽

Chaotic Time Series ◽

Support Vector ◽

Vector Machines ◽

Online Prediction ◽

Better Than

The predictions of chaotic time series by applying the least squares support vector machine (LS-SVM), with comparison with the traditional-SVM and-SVM, were specified. The results show that, compared with the traditional SVM, the prediction accuracy of LS-SVM is better than the traditional SVM and more suitable for time series online prediction.

Download Full-text

Foundation pit displacement monitoring and prediction using least squares support vector machines based on multi-point measurement

Structural Health Monitoring ◽

10.1177/1475921718767935 ◽

2018 ◽

Vol 18 (3) ◽

pp. 715-724 ◽

Cited By ~ 5

Author(s):

Xiao Li ◽

Xin Liu ◽

Clyde Zhengdao Li ◽

Zhumin Hu ◽

Geoffrey Qiping Shen ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector Machines ◽

Data Processing ◽

Least Squares ◽

Prediction Accuracy ◽

Support Vector ◽

Displacement Monitoring ◽

Processing Efficiency ◽

Foundation Pit ◽

Vector Machines

Foundation pit displacement is a critical safety risk for both building structure and people lives. The accurate displacement monitoring and prediction of a deep foundation pit are essential to prevent potential risks at early construction stage. To achieve accurate prediction, machine learning methods are extensively applied to fulfill this purpose. However, these approaches, such as support vector machines, have limitations in terms of data processing efficiency and prediction accuracy. As an emerging approach derived from support vector machines, least squares support vector machine improve the data processing efficiency through better use of equality constraints in the least squares loss functions. However, the accuracy of this approach highly relies on the large volume of influencing factors from the measurement of adjacent critical points, which is not normally available during the construction process. To address this issue, this study proposes an improved least squares support vector machine algorithm based on multi-point measuring techniques, namely, multi-point least squares support vector machine. To evaluate the effectiveness of the proposed multi-point least squares support vector machine approach, a real case study project was selected, and the results illustrated that the multi-point least squares support vector machine approach on average outperformed single-point least squares support vector machine in terms of prediction accuracy during the foundation pit monitoring and prediction process.

Download Full-text

Fault Diagnosis Method of Gear Based on Local Mean Decomposition and Least Squares Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.415.548 ◽

2013 ◽

Vol 415 ◽

pp. 548-554

Author(s):

Zhou Wan ◽

Xing Zhi Liao ◽

Xin Xiong ◽

Zhi Rong Li

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Squares ◽

Vibration Signal ◽

Support Vector ◽

Vibrational Signal ◽

Local Mean Decomposition ◽

Gear Fault ◽

Input Sample ◽

Local Mean

For differences of time-domain energy distribution of different gear fault vibration signal, an analytical method based on local mean decomposition (LMD) and least squares support vector machine (LS-SVM) is proposed to apply to gear fault diagnosis. First vibrational signal of gear is decomposed into a series of product functions (PF) by LMD method. Then extracting energy characteristic parameters of PF components which contain main fault information to constitute a fault feature vectors, which is considered as input sample of well-trained LS-SVM, and then identifying working state and fault type of different gear can be identified accurately and effectively by diagnostic method based on LMD and LS-SVM.

Download Full-text

Employing Ray-Tracing and Least-Squares Support Vector Machines for Localisation

Sensors ◽

10.3390/s18114059 ◽

2018 ◽

Vol 18 (11) ◽

pp. 4059 ◽

Cited By ~ 2

Author(s):

Benny Chitambira ◽

Simon Armour ◽

Stephen Wales ◽

Mark Beach

Keyword(s):

Support Vector Machine ◽

Support Vector Machines ◽

Outage Probability ◽

Least Squares ◽

Ray Tracing ◽

Direct Method ◽

Support Vector ◽

Experimental Setup ◽

Vector Machines

This article evaluates the use of least-squares support vector machines, with ray-traced data, to solve the problem of localisation in multipath environments. The schemes discussed concern 2-D localisation, but could easily be extended to 3-D. It does not require NLOS identification and mitigation, hence, it can be applied in any environment. Some background details and a detailed experimental setup is provided. Comparisons with schemes that require NLOS identification and mitigation, from earlier work, are also presented. The results demonstrate that the direct localisation scheme using least-squares support vector machine (the Direct method) achieves superior outage to TDOA and TOA/AOA for NLOS environments. TDOA has better outage in LOS environments. TOA/AOA performs better for an accepted outage probability of 20 percent or greater but as the outage probability lowers, the Direct method becomes better.

Download Full-text