scholarly journals Characteristics of Valuable Microseismic Events in Heading Face of an Underground Coal Mine Using Microseismic System

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenlong Zhang ◽  
Tianhong Huo ◽  
Chen Li ◽  
Cunwen Wang ◽  
Xiaocheng Qu ◽  
...  
Keyword(s):  
Rock Burst ◽  
Maximum Amplitude ◽  
Vibration Signal ◽  
Geological Structure ◽  
Signal Acquisition ◽  
Monitoring Method ◽  
Event Duration ◽  
Underground Coal Mine ◽  
Basic Characteristics ◽  
Monitoring Technologies

Rock burst monitoring of heading face is a weak aspect of rock burst monitoring in China; acoustic emission (AE) monitoring is one of the few monitoring technologies used in heading face, but its target signals are small energy events which are easy to be disturbed. Researchers usually focus on the weak AE events but ignore the microseismic (MS) events (different from AE event and caused by a larger scale of coal fracture), while this kind of events can also reflect the pressure situation of heading face and have higher energy value which may become a better indicator for rock burst monitoring of heading face. So, the basic characteristics of MS events in heading face are studied based on a running vibration signal acquisition system, including the occurrence position, main frequency range, maximum amplitude (MA) range, event duration, and relationship with geological structure. This paper provides a development basis of the monitoring method for rock burst monitoring of heading face by using MS events.

scholarly journals Failure Diagnosis System for a Ball-Screw by Using Vibration Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Won Gi Lee ◽  
Jin Woo Lee ◽  
Min Sung Hong ◽  
Sung-Ho Nam ◽  
YongHo Jeon ◽  
...  
Keyword(s):  
Manufacturing Systems ◽  
Vibration Signal ◽  
Failure Diagnosis ◽  
Ball Screw ◽  
Signal Acquisition ◽  
Frequency Range ◽  
Monitoring Method ◽  
Diagnosis System ◽  
Time Frequency ◽  
Defect Position

Recently, in order to reduce high maintenance costs and to increase operating ratio in manufacturing systems, condition-based maintenance (CBM) has been developed. CBM is carried out with indicators, which show equipment’s faults and performance deterioration. In this study, indicator signal acquisition and condition monitoring are applied to a ball-screw-driven stage. Although ball-screw is a typical linearly reciprocating part and is widely used in industry, it has not gained attention to be diagnosed compared to rotating parts such as motor, pump, and bearing. First, the vibration-based monitoring method, which uses vibration signal to monitor the condition of a machine, is proposed. Second, Wavelet transform is used to analyze the defect signals in time-frequency domain. Finally, the failure diagnosis system is developed using the analysis, and then its performance is evaluated. Using the system, we estimated the severity of failure and detect the defect position. The low defect frequency (≈58.7 Hz) is spread all over the time in the Wavelet-filtered signal with low frequency range. Its amplitude reflects the progress of defect. The defect position was found in the signal with high frequency range (768~1,536 Hz). It was detected from the interval between abrupt changes of signal.

scholarly journals Intelligent Fault Diagnosis and Forecast of Time-Varying Bearing Based on Deep Learning VMD-DenseNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7467
Author(s):  
Shih-Lin Lin
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Image Feature ◽  
Signal Acquisition ◽  
Time Varying ◽  
Intelligent Fault Diagnosis ◽  
Image Block ◽  
Wide Range

Rolling bearings are important in rotating machinery and equipment. This research proposes variational mode decomposition (VMD)-DenseNet to diagnose faults in bearings. The research feature involves analyzing the Hilbert spectrum through VMD whereby the vibration signal is converted into an image. Healthy and various faults show different characteristics on the image, thus there is no need to select features. Coupled with the lightweight network, DenseNet, for image classification and prediction. DenseNet is used to build a model of motor fault diagnosis; its structure is simple, and the calculation speed is fast. The method of using DenseNet for image feature learning can perform feature extraction on each image block of the image, providing full play to the advantages of deep learning to obtain accurate results. This research method is verified by the data of the time-varying bearing experimental device at the University of Ottawa. Through the four links of signal acquisition, feature extraction, fault identification, and prediction, a mechanical intelligent fault diagnosis system has established the state of bearing. The experimental results show that the method can accurately identify four common motor faults, with a VMD-DenseNet prediction accuracy rate of 92%. It provides a more effective method for bearing fault diagnosis and has a wide range of application prospects in fault diagnosis engineering. In the future, online and timely diagnosis can be achieved for intelligent fault diagnosis.

The Assessment of Material Characteristic Using Vibration Signal Analysis during Drilling Process

2014 ◽  
Vol 598 ◽  
pp. 3-7
Author(s):  
Othman Inayatullah ◽  
Faizal Hamid ◽  
Hew Wei Hon ◽  
Nordin Jamaludin ◽  
Shahrum Abdullah
Keyword(s):  
Signal Analysis ◽  
Maximum Amplitude ◽  
Vibration Signal ◽  
Damping Capacity ◽  
Drilling Process ◽  
Cnc Machine ◽  
Material Characteristic ◽  
Signal Parameters ◽  
Stainless Steel 304 ◽  
Vibration Signal Analysis

The purpose of this paper is to assess the material characteristic by using vibration signal analysis during drilling process. Generally, material with high mechanical properties exhibits low damping capacity and vice versa. The main objective of this paper is to develop a relationship between the signal parameters and the strengths of materials. Aluminum alloy 1100, stainless steel 304, and mild steel were selected as the specimens to be drilled using CNC machine. The vibration signal was captured using a transducer and recorded using a DAQ system. The signal parameters such as maximum amplitude, vibration energy, and the RMS value were extracted using MATLAB software. From the results obtained, the graphs of signal parameters versus strength of each specimen are plotted to show their relationship. It was found that the signal parameters increased exponentially as the strengths of materials increased. Besides that, the vibration signal of the specimens are analysed and compared based on their mechanical characteristics.

scholarly journals Multistage identification method for real-time abnormal events of streaming data

2019 ◽  
Vol 15 (12) ◽  
pp. 155014771989454
Author(s):  
Hao Luo ◽  
Kexin Sun ◽  
Junlu Wang ◽  
Chengfeng Liu ◽  
Linlin Ding ◽  
...  
Keyword(s):  
Real Time ◽  
Coal Mine ◽  
Early Warning ◽  
Sliding Window ◽  
Geological Structure ◽  
Streaming Data ◽  
Event Monitoring ◽  
Monitoring Method ◽  
Identification Method ◽  
Abnormal Events

With the development of streaming data processing technology, real-time event monitoring and querying has become a hot issue in this field. In this article, an investigation based on coal mine disaster events is carried out, and a new anti-aliasing model for abnormal events is proposed, as well as a multistage identification method. Coal mine micro-seismic signal is of great importance in the investigation of vibration characteristic, attenuation law, and disaster assessment of coal mine disasters. However, as affected by factors like geological structure and energy losses, the micro-seismic signals of the same kind of disasters may produce data drift in the time domain transmission, such as weak or enhanced signals, which affects the accuracy of the identification of abnormal events (“the coal mine disaster events”). The current mine disaster event monitoring method is a lagged identification, which is based on monitoring a series of sensors with a 10-s-long data waveform as the monitoring unit. The identification method proposed in this article first takes advantages of the dynamic time warping algorithm, which is widely applied in the field of audio recognition, to build an anti-aliasing model and identifies whether the perceived data are disaster signal based on the similarity fitting between them and the template waveform of historical disaster data, and second, since the real-time monitoring data are continuous streaming data, it is necessary to identify the start point of the disaster waveform before the identification of the disaster signal. Therefore, this article proposes a strategy based on a variable sliding window to align two waveforms, locating the start point of perceptual disaster wave and template wave by gradually sliding the perceptual window, which can guarantee the accuracy of the matching. Finally, this article proposes a multistage identification mechanism based on the sliding window matching strategy and the characteristics of the waveforms of coal mine disasters, adjusting the early warning level according to the identification extent of the disaster signal, which increases the early warning level gradually with the successful result of the matching of 1/ N size of the template, and the piecewise aggregate approximation method is used to optimize the calculation process. Experimental results show that the method proposed in this article is more accurate and be used in real time.

Rotor Crack Detection Based on Multi-Vibration Signal Fusion Collected from the Basement of Machinery Using SVM and Statistical Characteristics Methodology

2010 ◽  
Vol 34-35 ◽  
pp. 1000-1004
Author(s):  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang ◽  
T. Zhang
Keyword(s):  
Fault Diagnosis ◽  
Crack Detection ◽  
Feature Fusion ◽  
Vibration Signal ◽  
Statistical Characteristics ◽  
Support Vector ◽  
Signal Acquisition ◽  
Multiple Sensors ◽  
Signal Fusion ◽  
Diagnosis Method

As the poor generability of special sensor support frame and the inconvenience of signal acquisition in the process of common fault diagnosis for cracked rotor, a new fault diagnosis method is presented in this paper. this method takes the basement of rotor test rig as the monitoring objects and makes feature fusion for time-domain statistics of multiple sensors using SVM (support vector machine). The result of experiment showed that the method using the multi-sensor signal fusion technology collected from the basement of machinery has the advantages of better diagnostic precision for rotor crack diagnosis, furthermore, it supplies a new way for rotor fault diagnosis.

A new method to access information for underground omni-dimensional vibration vector

Sensor Review ◽  
2015 ◽  
Vol 35 (1) ◽  
pp. 125-132 ◽  
Author(s):  
Jian Li ◽  
Ying Liu ◽  
Yan Han ◽  
Xianhui Chen
Keyword(s):  
Vibration Signal ◽  
Vibration Sensor ◽  
New Method ◽  
Underground Explosion ◽  
Sensor Calibration ◽  
Signal Acquisition ◽  
Element Analysis ◽  
Content Type ◽  
Sensor Structure ◽  
Vector Signal

Purpose – The purpose of this paper is to propose a new method to achieve omni-directional vibration vector signal acquisition, and use this method to improve the accuracy of the underground explosion point localization. Design/methodology/approach – Following an introduction, this paper describes the design principle of a sensor structure, and discusses the rationality of the spherical structure of the sensor through finite element analysis. The sensor prototype is designed according to the above method, and its performance is tested by the sensor calibration experiment. Finally, applications are also discussed. Findings – This paper shows that the method for underground omni-directional vibration signal acquisition is reasonable and feasible. The vibration sensor, designed by this method, of which the triaxial dynamic characteristics are consistent, and the three-dimensional vibration information acquired by this sensor can achieve high-precision localization for an underground explosion point. Originality/value – The paper describes a new method for omni-directional vibration vector signal acquisition. The vibration sensor is developed based on this method, which has a broad application prospect in the positioning of an underground explosion point, the evaluation of explosive power and other underground projects.

Data Acquisition and Vibration Analysis Based on LabVIEW

2012 ◽  
Vol 239-240 ◽  
pp. 468-472
Author(s):  
Li Li Kang ◽  
Ze Zhang ◽  
Jian Ming Yu
Keyword(s):  
Fourier Transform ◽  
Data Acquisition ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Analysis Methods ◽  
Data Acquisition Card

According to the characteristics and analysis methods of the vibration signal, this paper analyses and processes the vibration signal with the combination of the INV9822A sensor, the PCI-4472 data acquisition card and the signal acquisition system using Fourier Transform. The vibration law is analysed effectively to explain the effectiveness and practicality of the method due to the compressor vibration signal.

Study of a New Real-Time Monitoring Method on the Underground Mining Activities

2012 ◽  
Vol 229-231 ◽  
pp. 1402-1405
Author(s):  
Xiu Zhi Meng ◽  
Zeng Zhi Zhang ◽  
Zong Sheng Wang
Keyword(s):  
Real Time ◽  
Underground Mining ◽  
Energy Eigenvalue ◽  
Mining Area ◽  
P Wave ◽  
Signal Acquisition ◽  
Mining Activities ◽  
Real Time Monitoring ◽  
P Waves ◽  
Monitoring Method

This paper presents a new real-time monitoring method based on the explosion source location technique on the underground mining activities in the situation the state can not achieve the full uninterrupted supervision because of the backward monitoring tools and equipment. The supervise mode results in some small coal mines in the profit-driven to ultra-layer or cross-border mining which causes a many of safety accidents. The five acceleration vibration sensors buried underground in the mining area pick up blasting vibration waves coming from blasting tunneling. Every signal acquisition sub-station deals with the according sensor output signals by using wavelet transform to identify the P waves and using energy eigenvalue method to determine the arriving time of P wave to the sensor, then translates the sensor’s spatial and temporal parameters to the principal computer. The principal computer locates the explosion source by the Geiger algorithm and displays the explosion source’s spatial message in the mine’s electronic map. The method is feasible and the positioning horizontal error is less than 10m by field-proven.

An Improved Random Decrement Algorithm and Applications

2011 ◽  
Vol 121-126 ◽  
pp. 4372-4376
Author(s):  
Qing Wei Ye ◽  
Zhi Min Feng ◽  
Hai Gang Hu
Keyword(s):  
Response Function ◽  
Vibration Signal ◽  
Recognition Algorithm ◽  
Mode Analysis ◽  
Time Signal ◽  
Signal Acquisition ◽  
Free Response ◽  
Time Frequency ◽  
Random Decrement Technique ◽  
Random Decrement

The free response function is the foundation of mode analysis and recognition of vibration signal, and random decrement algorithm is the commonly used classical algorithm of extracting the free response function. But under the restriction of engineering conditions, it may be impossible for long-time signal acquisition, which makes the number of sample points fail to meet the requirements of the random decrement algorithm, causing the extracted free response signals to contain strong noise and other influencing factors. Aiming at the shortcomings of the existing random decrement technique, this paper proposes an improved random decrement algorithm based on multi-secant method, which can get satisfactory free response signals with short vibration response signals to provide excellent basis of analysis for the vibration mode recognition algorithm of various time-frequency domains. Actual engineering tests confirm that the improved algorithm greatly improves the precision of extracting free response signals while basically keeping the computation speed unchanged, it has high application value.

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