Health Diagnosis of Roadheader Based on Reference Manifold Learning and Improved K-Means

The safe and stable operation of roadheader is of great significance to the efficient and rapid production of a coal mine. Health diagnosis based on vibration signals has been studied in bearings and motors. Complex geological conditions and bad working environment lead to the characteristics of nonlinear and time-varying vibration signals of a roadheader. In this paper, a health state analysis method based on reference manifold (RM) learning and improved K-means clustering analysis was proposed; the method was verified by using the real-time collected roadheader cutting reducer fault signal. Firstly, the comparison signal and analysis signal were extracted from the actual collected vibration data of the roadheader, and the referential analysis samples were constructed through time domain and wavelet packet energy analysis. Then, the characteristic structure of the low-dimensional space of the referential analysis samples is obtained by Locally Linear Embedding (LLE), which is a method of manifold learning. Through the improved K-means clustering analysis method, the low-dimensional structure parameters were analyzed and the clustering effect index was obtained, which was used as the health evaluation index (HEI). Finally, the normal distribution model of the health evaluation index is established, and the confidence interval of the health evaluation index is determined, so as to realize the health state analysis of the roadheader and realize the fault warning function. Through the analysis of data of three sensors, the results show that the roadheader failed on the 15th day, which is consistent with the actual working condition. Through practical analysis, the effectiveness of the method was verified and provided a kind of fault analysis idea and method for equipment working under complex working conditions and the theoretical basis for fault type analysis.

Download Full-text

Analysis method of the cavitation vibration signals in poppet valve based on EEMD

Advances in Mechanical Engineering ◽

10.1177/1687814021998114 ◽

2021 ◽

Vol 13 (2) ◽

pp. 168781402199811

Author(s):

Beibei Li ◽

Qiao Zhao ◽

Huaiyi Li ◽

Xiumei Liu ◽

Jichao Ma ◽

...

Keyword(s):

Ensemble Empirical Mode Decomposition ◽

Center Frequency ◽

Analysis Method ◽

Vibration Signals ◽

Poppet Valve ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Cavitation Intensity ◽

Eemd Method ◽

Peak Value

To study the vibration characteristics of the poppet valve induced by cavitation, the signal analysis method based on the ensemble empirical mode decomposition (EEMD) method was studied experimentally. The component induced by cavitation was separated from the vibration signals through the EEMD method. The results show that the IMF2 component has the largest amplitude and energy of all components. The root mean square (RMS) value, peak value of marginal spectrum, and center frequency of marginal spectrum of the IMF2 component were studied in detail. The RMS value and the peak value of the marginal spectrum decrease with a decrease of cavitation intensity. The center frequency of marginal spectrum is between 12 kHz and 20 kHz, and the center frequency first increases and then decreases with a decrease of cavitation intensity. The change rate of the center frequency also decreases with an increase of inlet pressure.

Download Full-text

Diagnosis Research on Main Bearing Wear of Gasoline Engines in Mechanical Engineering

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.644.304 ◽

2013 ◽

Vol 644 ◽

pp. 304-307 ◽

Cited By ~ 1

Author(s):

Chang Shun Wang

Keyword(s):

Gasoline Engine ◽

Vibration Monitoring ◽

Analysis Method ◽

Characteristic Parameters ◽

Main Bearing ◽

Vibration Signals ◽

Gasoline Engines ◽

Test Spot ◽

Monitoring Mechanism ◽

Spectral Analysis Method

The different clearances of main bearing of previously designed on EQ6100 model gasoline engine is diagnosed by means of vibration monitoring mechanism. Breakdown signals of main test on different speed, clearance of main bearing, test spot and weather were analyzed by Spectral Analysis method and compared with normal and abnormal vibration signals. As a result, the characteristic parameters and the identifying methods of breakdown are given. In addition, the problems of fault detection are pointed out.

Download Full-text

Power Transformers Health Index Calculation Method Based on Cloud Model and Fuzzy Evidential Reasoning

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1070-1072.1021 ◽

2014 ◽

Vol 1070-1072 ◽

pp. 1021-1028

Author(s):

De Hua Cai ◽

Xi Yang ◽

Rui Chuang Wang ◽

Cheng Zhi Ma ◽

Jin Cheng ◽

...

Keyword(s):

Calculation Method ◽

Cloud Model ◽

Evidential Reasoning ◽

Evaluation Index ◽

Power Transformers ◽

Health State ◽

Health Index ◽

Degree Of Belief ◽

Fuzzy Evidential Reasoning ◽

Index Calculation

Transformers health index calculation method based on cloud model and fuzzy evidential reasoning is proposed. According to the multi-level and multifactor of evaluation index information of power transformers, a layered evaluation index model is established. In order to deal with the ambiguity and uncertainty information of evaluation index, a normal cloud model is introduced, inferred the fuzzy degree of belief in the health state of evaluation index. Then use the fuzzy evidential reasoning method merge information of evaluation Index, inferred the degree of belief in the health state-level of transformer, calculated the health index of transformer. The results of an example analysis test its rationality and effectiveness.

Download Full-text

A stepwise emission clustering analysis method for analyzing the effects of heavy metal emissions from multiple income groups

The Science of The Total Environment ◽

10.1016/j.scitotenv.2021.152472 ◽

2021 ◽

pp. 152472

Author(s):

Boyue Zheng ◽

Guohe Huang ◽

Lirong Liu ◽

Jizhe Li ◽

Yu Li

Keyword(s):

Heavy Metal ◽

Clustering Analysis ◽

Analysis Method ◽

Income Groups ◽

Heavy Metal Emissions

Download Full-text

Novel Convolutional Restricted Boltzmann Machine manifold learning inspired dynamic user clustering hybrid precoding for millimeter-wave massive multiple-input multiple-output systems

International Journal of Distributed Sensor Networks ◽

10.1177/15501477211055376 ◽

2021 ◽

Vol 17 (11) ◽

pp. 155014772110553

Author(s):

Xiaoping Zhou ◽

Haichao Liu ◽

Bin Wang ◽

Qian Zhang ◽

Yang Wang

Keyword(s):

Millimeter Wave ◽

Manifold Learning ◽

Multiple Input Multiple Output ◽

Restricted Boltzmann Machine ◽

Boltzmann Machine ◽

Hybrid Precoding ◽

User Clustering ◽

Multiple Input ◽

Input Multiple Output ◽

Low Dimensional

Millimeter-wave massive multiple-input multiple-output is a key technology in 5G communication system. In particular, the hybrid precoding method has the advantages of being power efficient and less expensive than the full-digital precoding method, so it has attracted more and more attention. The effectiveness of this method in simple systems has been well verified, but its performance is still unknown due to many problems in real communication such as interference from other users and base stations, and users are constantly on the move. In this article, we propose a dynamic user clustering hybrid precoding method in the high-dimensional millimeter-wave multiple-input multiple-output system, which uses low-dimensional manifolds to avoid complicated calculations when there are many antennas. We model each user set as a novel Convolutional Restricted Boltzmann Machine manifold, and the problem is transformed into cluster-oriented multi-manifold learning. The novel Convolutional Restricted Boltzmann Machine manifold learning seeks to learn embedded low-dimensional manifolds through manifold learning in the face of user mobility in clusters. Through proper user clustering, the hybrid precoding is investigated for the sum-rate maximization problem by manifold quasi-conjugate gradient methods. This algorithm avoids the traditional method of processing high-dimensional channel parameters, achieves a high signal-to-noise ratio, and reduces computational complexity. The simulation result table shows that this method can get almost the best summation rate and higher spectral efficiency compared with the traditional method.

Download Full-text

Health State Evaluation of Continuous Girder Bridge Based on Fuzzy Analytic Hierarchy Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.178-181.2285 ◽

2012 ◽

Vol 178-181 ◽

pp. 2285-2289

Author(s):

Hai Tao Li

Keyword(s):

Analytic Hierarchy Process ◽

Technical State ◽

Fuzzy Analytic Hierarchy Process ◽

Health Evaluation ◽

Health State ◽

Fuzzy Evaluation ◽

Analytic Hierarchy ◽

State Evaluation ◽

Degree Of Membership ◽

Hierarchy Process

Based on fuzzy analytic hierarchy process, The model of bridge health evaluation is established using the quantification relations between the bridge technical state evaluation grade and degree of membership function of bridge health evaluation, making use of the computed result of various index of degree of membership value and weight, obtains all levels of fuzzy evaluation collection. According to the maximum membership principles to evaluate the technical state grade of bridge structure the corresponding level, and with its result to instruct the decision-making of bridge maintenance and strengthening.

Download Full-text