Research on Multi-source Sparse Optimization Method and Its Application in Compound Fault Detection of Gearbox

Wireless network control system has high failure rate, and is difficult to be diagnosed. Wireless network transmission signal effectively reflect the failure categories. In order to effectively detect the wireless network control system fault, this paper presents a fault detection method of correlation dimensional nonlinear timing characteristics for wireless network transmission signal, which mainly improves the traditional correlation dimension extraction algorithm. The method processes and analyzes the collected transmission signal of four types wireless network control system in fault condition, and then extract fault feature through an improved correlation dimension algorithm. It improves the calculation accuracy of the correlation dimension with a standard deviation 15% -30% than that of the traditional algorithm, and it significantly enhances the clustering distribution characteristics, reflecting its superiority in fault detection. Fault detection results show that the improved feature extraction method for correlation dimension can effectively detect failure in wireless network control system, whose accuracy is improved by 21.4%, and has great practical value.

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Nonlinear fault detection threshold optimization method for RAIM algorithm using a heuristic approach

GPS Solutions ◽

10.1007/s10291-015-0494-9 ◽

2015 ◽

Vol 20 (4) ◽

pp. 863-875 ◽

Cited By ~ 4

Author(s):

Pan He ◽

Gang Liu ◽

Chun Tan ◽

Yan-e Lu

Keyword(s):

Fault Detection ◽

Detection Threshold ◽

Optimization Method ◽

Heuristic Approach ◽

Threshold Optimization

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Compressive sensing of wireless sensors based on group sparse optimization for structural health monitoring

Structural Health Monitoring ◽

10.1177/1475921717721457 ◽

2017 ◽

Vol 17 (4) ◽

pp. 823-836 ◽

Cited By ~ 14

Author(s):

Yuequan Bao ◽

Zuoqiang Shi ◽

Xiaoyu Wang ◽

Hui Li

Keyword(s):

Compressive Sensing ◽

Optimization Algorithm ◽

Random Sampling ◽

Wireless Sensors ◽

Original Data ◽

Optimization Method ◽

Sparse Optimization ◽

Group Sparsity ◽

Vibration Data ◽

Using Data

Vibration signals of most civil infrastructures have sparse characteristics (i.e. only a few modes contribute to the vibration of the structures). Therefore, the vibration data usually have sparse representation. Additionally, the vibration data measured by the sensors placed on different locations of structure have almost the same sparse structure in the frequency domain. On basis of the group sparsity of the structural vibration data, we proposed a group sparse optimization algorithm based on compressive sensing for wireless sensors. Different from the Nyquist sampling theorem, the data are first acquired by a nonuniform low-rate random sampling method according to compressive sensing theory. We then developed the group sparse optimization algorithm to reconstruct the original data from incomplete measurements. By conducting a field test on Xiamen Haicang Bridge with wireless sensors, we illustrate the effectiveness of the proposed approach. The results show that smaller reconstruction errors can be achieved using data from multiple sensors with the group sparse optimization method than using data from only single sensor. Even using only 10% random sampling data, the original data can be reconstructed using the group sparse optimization method with a small reconstruction error. In addition, the modal parameters can also be identified from the reconstruction data with small identification errors.

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Fault Detection for Linear Discrete Time-Varying Systems with Measurement Packet Dropping

Mathematical Problems in Engineering ◽

10.1155/2013/697345 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 4

Author(s):

Yueyang Li ◽

Shuai Liu ◽

Zhonghua Wang

Keyword(s):

Fault Detection ◽

Discrete Time ◽

Adjoint Operator ◽

Optimal Solution ◽

Optimization Method ◽

Packet Dropout ◽

Time Varying ◽

Time Varying Systems ◽

Residual Generator ◽

Detection Filter

The fault detection (FD) problem for linear discrete time-varying (LDTV) systems with measurement packet dropouts is considered. The objective is to design a new observer-based fault detection filter (FDF) as a residual generator through employing packet dropout information on the measurement sequence. Based on some new defined input-to-output operators, the FD problem is formulated in a framework of maximizing stochasticH−/H∞orH∞/H∞performance index. By introducing an adjoint-operator-based optimization method, the analytical optimal solution can be derived in terms of solving a modified Riccati equation. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

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