An experiment simulating coal seam under forced vibration conditions was conducted. Acceleration response and microseism signal during the experiment were collected and analyzed. It is found that, with an increasing amount of vibration, the natural frequency of the specimen decreases, and this phenomenon reflects fractures appearing in the specimen. Acceleration response signals show that peaks in shock excitation frequency and shock excitation acceleration affect the acceleration response, which reflects damage to the specimen. When shock excitation frequency nears natural frequency, the acceleration response first decreases and then increases. When resonance occurs, it reaches its maximum value. As shock excitation acceleration peaks increase, the acceleration response peak of the specimen also increases. We conclude that destruction is mainly concentrated in the coal seam evidenced by specimen destruction situation. Then shock excitation frequency and shock excitation acceleration influence on microseism signals were analyzed by Hilbert-Huang transform. By receiving these signals and analyzing their characteristics, it is beneficial to develop new methods to predict disasters underground dynamically in the future.
An unexpected and violent simultaneous ejection of a large mass of gas and coal from an underground working face during mining is identified as “instantaneous coal outburst” [
Blasting is one process liable to cause outbursts. Millisecond delay blasting, a type of sectional blasting, is the primary method of roadway development in Chinese outburst mines. Every blast produces vibration, each of which forms a P wave (particle vibration direction is similar to wave propagation) as it moves through space, potentially quite far. In many coal mines, roadways exist horizontally in the coal bed, making the direction of vibration produced by blasting likewise horizontal. Earthquakes also cause horizontal coal bed vibration. In this experiment, the horizontal vibration of the specimen was produced by a vibration table.
“In coal and rock failure processes, a variety of energies such as elastic energy, expansion energy of compressed gas, thermal energy, sound energy, and electromagnetic energy will be dissipated” [
While a variety of vibration forms exist in an underground coalface, a specific form of vibration was selected to conduct this experiment. Figure
Sketch map of forced vibration specimen.
Deformation and force of multiple-degree-of-freedom forced vibration system.
Different strata were simulated by a 10-layer steel frame located at different heights on a vibration table. The vibration table provided exciting force (with differing maximum acceleration peak and frequency sine wave load) in the horizontal direction. The rolling bearing ensures steel frame motion repetition in one direction. Figures
The one-dimensional horizontal vibration table was provided by Heibei University of Engineering. It is shown in Figure
The vibration table characters.
Size | Maximum payload | Maximum overturn moment | Maximum eccentric torque | Maximum displacement | Frequency range | Acceleration at maximum load | Maximum speed |
---|---|---|---|---|---|---|---|
3 m × 3 m | 10000 kg | 300 KN·m | 100 KN·m | ±100 mm | 0.2 Hz~50 Hz | 1.2 g | 1.0 m/s |
Vibrating table.
Steel frame of model box.
Model specimen after completion.
The model box was a 10-layer independent square steel framework stacking ensemble. Each layer of framework consisted of welded, four-groove steel. The groove steel section size was 60 mm × 30 mm × 3 mm (length × width × thickness, referred as L × W × H below). The overall internal size of the model box was 400 mm × 400 mm × 650 mm (L × W × H). Four rolling bearings were set up to form free rolling points to reduce friction and deflection between the two square steel frameworks. Clearance between the two layers was 6 mm. Among these 10 layers of independent square steel framework, 3 frameworks were selected for opening 3 square holes (45 mm × 45 mm) on the frame wall of every framework for installing sensors. In order to effectively lift the specimen after molding, steel bars (diameter 6 mm, spacing 100 mm) and hooks were welded on the bottom of the model box. According to this model box design, in-plane lateral and torsion deformation is constrained, so only shear deformation along the vibration direction is present.
For the purpose of this experiment, the specimen was divided into three parts: the coal roof, coal bed, and coal floor. These three parts consisted of a concrete mixture. Table
Material and mixing ratio.
Cement | Lime | Coal powder | Medium sand | Gravel | Water | |
---|---|---|---|---|---|---|
Coal floor | 1 | — | — | 1.65 | 2.8 | 0.55 |
Coal bed | 1 | 4 | 20 | — | — | 6 |
Coal roof | 1 | — | — | 6.9 | — | 1.6 |
500 mm of the mixture was poured in the coal roof, 80 mm in the coal bed, and 70 mm in the coal floor to simulate stress from overlying strata. The coal floor was cast by concrete and medium sand placement. The coal roof was cast layer-by-layer, where the thickness of each layer was 10~15 mm. Talc powder was added between every layer to create strata interface. The particle size of coal powder in the coal bed was 0–0.4 mm in diameter. A 100 mm long roadway (with a net sectional area of 60 mm × 60 mm) was established in the coal bed. Before the molding process, four rollers were installed along the direction of vibration between every two steel frames to reduce friction. Because the quality of the steel frame is much lower than the model, the steel frame was retained after molding. In the experiment, therefore, the shear deformation in the frame was limited and the specimen stiffness increased. This caused a vibration frequency greater than the actual, natural value in the measured model.
Acceleration, speed and microseism sensor are used in this experiment. Figures
Technical data of the acceleration sensor.
Main technical parameters | |||
---|---|---|---|
Model | YD-32T | Linear | ≤1% |
Sensitivity V/ms2 | 0.1 | Transverse sensitivity | ≤5% |
Frequency range (Hz) (+10%) | 10~6000 | Output amplitude | ±5 VP |
Install resonance point (Hz) | 23 K | Output offset voltage | 8~12 VDC |
Range m/s2 (+10%) | 20 | Supply current | 2~20 mA |
Resolution (m/s2) | 0.0002 | Excitation voltage | 9 V |
Weight (g) | 40 | Output impedance | ≤150 Ω |
Geometry (mm) | Six party 18 × 18 × 23 | Shell insulation resistance | >108 Ω |
Output mode | Top output | Discharge time constant | ≥0.2 s |
Mounting screw | M5 | Year stability | 3% |
Operating temperature range | −40°C~+80°C |
Acceleration transducer.
Calibration of acceleration transducer.
Three YD-32T acceleration sensors were used in this experiment. These sensors were, respectively, arranged on the 3 different points (the bottom, middle, and top of the specimen) on the specimen’s side. 1#, 2#, and 3# acceleration sensors correspond to the bottom of the model specimen, the middle, and the top, respectively. Acceleration sensors are attached to the steel frame by magnet.
Speed sensor.
Technical data of microseism sensor.
Index/model | Unit | SF1500S/SF1500SN |
---|---|---|
Linear output range | g peak value | ±3 |
Sensitivity (differential) | V/g | 1.2 (2.4) |
Frequency response | Hz | DC to 1500 |
Frequency response (differential signal) | Hz | DC to 5000 |
Dynamic range (100 Hz BW) | dB | 120 |
Noise (10 to 1000 Hz) | ngrms/ |
300 to 500 |
Transverse impedance | dB | >40 |
Impact of restrictions | g peak value | 1500 |
Vibration (20 Hz–2000 Hz) | g peak value | 60 |
Operating temperature | °C | 40 to +125 |
Temperature sensitivity coefficient | ppm/°C | 75 |
Drift thermal coefficient | ±100 | |
Linearity error | % full scale | ±0.1 |
Input voltage | Volts DC | ±6 to ±15 |
Static current | mA | 11.6 |
Microseism sensor.
Microseism sensor and EME sensors (electromagnetic emission sensors are used for measuring EME signals which are not discussed in this paper) distribution.
Arrangement of each sensor.
The specimen’s natural vibration frequency is affected by material, density, size, and shape factors, reflecting the natural dynamic characteristics of specimens. By taping the model specimens to the top, transverse free vibration was produced. The speed sensor fixed on top of the specimen collected vibration signals and then amplified and filtered them. Finally, the spectrum curve was obtained using the vibration signals by fast Fourier transform (FFT). In this experiment, data acquisition frequency was 1300 Hz. The results of the two experiments are as shown in Figure
Free vibration curve and spectrogram.
White noise is known as a random signal with a constant power spectral density [
Setting of experimental parameters.
Working condition | Frequency |
Input acceleration | Vibration duration (s) | Vibration |
Working condition | Frequency (hz) | Input acceleration | Vibration duration (s) | Vibration |
---|---|---|---|---|---|---|---|---|---|
1 | White noise | 0.02 g | 15.0 | — | 43 | 30 | 0.5 g | 8.0 | 240 |
2 | 1 | 0.02 g | 30.0 | 30 | 44 | White noise | 0.02 g | 15.0 | — |
3 | 5 | 0.02 g | 6.0 | 30 | 45 | 32 | 0.5 g | 7.5 | 240 |
4 | 10 | 0.02 g | 6.0 | 60 | 46 | White noise | 0.02 g | 15.0 | — |
5 | 15 | 0.02 g | 4.0 | 60 | 47 | 34 | 0.5 g | 7.1 | 240 |
6 | 20 | 0.02 g | 3.0 | 60 | 48 | White noise | 0.02 g | 15.0 | — |
7 | 24 | 0.02 g | 5.0 | 120 | 49 | 24 | 0.75 g | 10.0 | 240 |
8 | White noise | 0.02 g | 15.0 | — | 50 | White noise | 0.02 g | 15.0 | — |
9 | 1 | 0.1 g | 30.0 | 30 | 51 | 27 | 0.75 g | 8.9 | 240 |
10 | White noise | 0.02 g | 15.0 | — | 52 | White noise | 0.02 g | 15.0 | — |
11 | 5 | 0.1 g | 6.0 | 30 | 53 | 30 | 0.75 g | 8.0 | 240 |
12 | White noise | 0.02 g | 15.0 | — | 54 | White noise | 0.02 g | 15.0 | — |
13 | 10 | 0.1 g | 12.0 | 120 | 55 | 32 | 0.75 g | 7.5 | 240 |
14 | White noise | 0.02 g | 15.0 | — | 56 | White noise | 0.02 g | 15.0 | — |
15 | 15 | 0.1 g | 8.0 | 120 | 57 | 34 | 0.75 g | 7.1 | 240 |
16 | White noise | 0.02 g | 15.0 | — | 58 | White noise | 0.02 g | 15.0 | — |
17 | 20 | 0.1 g | 6.0 | 120 | 59 | 28 | 1.5 g | 8.6 | 240 |
18 | White noise | 0.02 g | 15.0 | — | 60 | White noise | 0.02 g | 15.0 | — |
19 | 24 | 0.1 g | 5.0 | 120 | 61 | 29 | 1.5 g | 8.3 | 240 |
20 | White noise | 0.02 g | 15.0 | — | 62 | White noise | 0.02 g | 15.0 | — |
21 | 27 | 0.1 g | 4.4 | 120 | 63 | 30 | 1.5 g | 8.0 | 240 |
22 | White noise | 0.02 g | 15.0 | — | 64 | White noise | 0.02 g | 15.0 | — |
23 | 30 | 0.1 g | 4.0 | 120 | 65 | 31 | 1.5 g | 7.7 | 240 |
24 | White noise | 0.02 g | 15.0 | — | 66 | White noise | 0.02 g | 15.0 | — |
25 | 32 | 0.1 g | 3.8 | 120 | 67 | 32 | 1.5 g | 7.5 | 240 |
26 | White noise | 0.02 g | 15.0 | — | 68 | White noise | 0.02 g | 15.0 | — |
27 | 34 | 0.1 g | 3.5 | 120 | 69 | 33 | 1.5 g | 7.3 | 240 |
28 | White noise | 0.02 g | 15.0 | — | 70 | White noise | 0.02 g | 15.0 | — |
29 | 2 | 0.5 g | 15.0 | 30 | 71 | 34 | 1.5 g | 7.1 | 240 |
30 | White noise | 0.02 g | 15.0 | — | 72 | White noise | 0.02 g | 15.0 | — |
31 | 5 | 0.5 g | 6.0 | 30 | 73 | 36 | 1.5 g | 6.7 | 240 |
32 | White noise | 0.02 g | 15.0 | — | 74 | White noise | 0.02 g | 15.0 | — |
33 | 10 | 0.5 g | 12.0 | 120 | 75 | 35 | 1.5 g | 57.1 | 2000 |
34 | White noise | 0.02 g | 15.0 | — | 76 | White noise | 0.02 g | 15.0 | — |
35 | 15 | 0.5 g | 8.0 | 120 | 77 | 20 | 1.5 g | 24.0 | 480 |
36 | White noise | 0.02 g | 15.0 | — | 78 | White noise | 0.02 g | 15.0 | — |
37 | 20 | 0.5 g | 6.0 | 120 | 79 | 15 | 1.2 g | 16.0 | 240 |
38 | White noise | 0.02 g | 15.0 | — | 80 | White noise | 0.02 g | 15.0 | — |
39 | 24 | 0.5 g | 10.0 | 240 | 81 | 10 | 1.5 g | 24.0 | 240 |
40 | White noise | 0.02 g | 15.0 | — | 82 | White noise | 0.02 g | 15.0 | — |
41 | 27 | 0.5 g | 8.9 | 240 | 83 | 5 | 2.4 g | 24.0 | 120 |
42 | White noise | 0.02 g | 15.0 | — |
These 83 working conditions were performed continuously. The specimen rupture process was such as (1) initial crack opening expansion, (2) derivative crack propagation, (3) crack coalescence, and (4) specimen destruction.
The natural vibration frequency of the specimen, which changes during the vibration period, reflects its natural dynamic characteristics. The random vibration method, which scans white noise, was used in this experiment to measure the specimen’s natural vibration frequency. White noise scanning acceleration was set to 0.02 g. The corresponding white noise frequency components were measured to be the same as natural vibration frequency and amplify in tandem. To obtain spectra, we collected signals in the speed sensor and processed these signals by fast Fourier transform (FFT). These spectra reveal the natural vibration frequency in detail.
Here working condition 1 is selected to measured (other working condition have similar process). The white noise signals set in vibration are shown in Figure
Natural vibration frequency of the specimen.
Work condition | Natural frequency (hz) | Work condition | Natural frequency (hz) | Work condition | Natural frequency (hz) | Work condition | Natural frequency (hz) |
---|---|---|---|---|---|---|---|
1 | 49.76 | 26 | 42.48 | 46 | 42.39 | 66 | 37.67 |
8 | 49.72 | 28 | 42.48 | 48 | 42.37 | 68 | 39.16 |
10 | 49.73 | 30 | 42.39 | 50 | 42.49 | 70 | 39.19 |
12 | 49.71 | 32 | 42.38 | 52 | 42.37 | 72 | 39.19 |
14 | 49.76 | 34 | 42.48 | 54 | 42.37 | 74 | 39.18 |
16 | 44.50 | 36 | 42.35 | 56 | 42.37 | 76 | 39.20 |
18 | 44.80 | 38 | 42.47 | 58 | 42.47 | 78 | 26.85 |
20 | 44.80 | 40 | 42.46 | 60 | 39.17 | 80 | 28.80 |
22 | 44.50 | 42 | 42.49 | 62 | 37.68 | 82 | 28.95 |
24 | 42.48 | 44 | 42.37 | 64 | 39.18 |
White noise signal of vibration table setting.
Time domain waveform and spectrogram of velocity response signal in working condition 1.
The specimen’s natural frequency decreases alongside an increase in the amount of vibration. When the coal specimen begins to display obvious failure characteristics, the natural vibration frequency decreases rapidly. It can be seen from Figure
Natural vibration frequency change trend curve.
A total of five acceleration levels, 0.02 g, 0.1 g, 0.5 g, 0.75 g, and 1.5 g, were put into the vibration table, respectively (see Table
Because measured acceleration on the vibration table was not completely consistent with input acceleration, a different value for altered shock excitation frequency was observed. To acquire shock excitation frequency influence on acceleration response, acceleration response enhancement coefficient
Extreme and enhanced coefficient of acceleration response (0.02 g).
Shock excitation frequency (Hz) |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
1 | 0.0191 | 0.00272 | 0.00274 | 0.00526 | 1.73 | 1.75 | 3.30 |
5 | 0.02435 | 0.00472 | 0.00508 | 0.00727 | 1.96 | 2.17 | 3.11 |
10 | 0.0177 | 0.00340 | 0.00376 | 0.00664 | 1.52 | 1.74 | 3.08 |
15 | 0.0179 | 0.00338 | 0.00377 | 0.00756 | 1.39 | 1.57 | 3.15 |
20 | 0.01757 | 0.00407 | 0.00443 | 0.00803 | 1.58 | 1.71 | 3.11 |
24 | 0.0193 | 0.00403 | 0.00418 | 0.00766 | 1.46 | 1.52 | 2.78 |
Extreme and enhancement coefficient of acceleration response (0.1 g).
Shock excitation frequency (Hz) |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
1 | 0.0778 | 0.0107 | 0.01 | 0.027 | 1.65 | 1.54 | 4.16 |
5 | 0.107 | 0.0187 | 0.0193 | 0.04 | 1.77 | 1.88 | 3.89 |
10 | 0.11 | 0.022 | 0.0267 | 0.0503 | 1.58 | 1.99 | 3.75 |
15 | 0.13 | 0.0247 | 0.0287 | 0.0513 | 1.40 | 1.65 | 2.95 |
20 | 0.147 | 0.0247 | 0.0303 | 0.0603 | 1.18 | 1.44 | 2.87 |
24 | 0.104 | 0.02 | 0.0177 | 0.0403 | 1.35 | 1.19 | 2.71 |
27 | 0.0953 | 0.019 | 0.0193 | 0.0407 | 1.40 | 1.42 | 2.99 |
30 | 0.076 | 0.0173 | 0.0187 | 0.038 | 1.59 | 1.72 | 3.50 |
32 | 0.089 | 0.0197 | 0.0213 | 0.042 | 1.55 | 1.68 | 3.30 |
34 | 0.0854 | 0.0193 | 0.021 | 0.0423 | 1.58 | 1.72 | 3.47 |
Extreme and enhanced coefficient of acceleration response (0.5 g).
Shock excitation frequency (Hz) |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
2 | 0.546 | 0.056 | 0.05533 | 0.0787 | 1.24 | 1.24 | 1.73 |
5 | 0.5 | 0.0543 | 0.0527 | 0.0717 | 1.10 | 1.10 | 1.49 |
10 | 0.505 | 0.0697 | 0.057 | 0.1037 | 1.09 | 0.93 | 1.68 |
15 | 0.5196 | 0.08 | 0.093 | 0.1233 | 1.13 | 1.34 | 1.77 |
20 | 0.4272 | 0.122 | 0.1747 | 0.2357 | 1.94 | 2.78 | 3.75 |
24 | 0.511 | 0.0977 | 0.1083 | 0.1393 | 1.34 | 1.48 | 1.91 |
27 | 0.4959 | 0.107 | 0.0767 | 0.0937 | 1.51 | 1.08 | 1.32 |
30 | 0.501 | 0.0883 | 0.069 | 0.1007 | 1.23 | 0.96 | 1.41 |
32 | 0.5088 | 0.089 | 0.0523 | 0.0967 | 1.22 | 0.72 | 1.33 |
34 | 0.524 | 0.0963 | 0.0477 | 0.092 | 1.29 | 0.64 | 1.23 |
Extreme and enhanced coefficient of acceleration response (0.75 g).
Shock excitation frequency (Hz) |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
24 | 0.7719 | 0.133 | 0.1893 | 0.1687 | 1.21 | 1.72 | 1.53 |
27 | 0.8053 | 0.169 | 0.1153 | 0.1747 | 1.47 | 1.00 | 1.52 |
30 | 0.81 | 0.1673 | 0.0767 | 0.145 | 1.45 | 0.66 | 1.25 |
32 | 0.7636 | 0.1247 | 0.057 | 0.11 | 1.14 | 0.52 | 1.01 |
34 | 0.7904 | 0.1253 | 0.048 | 0.11 | 1.11 | 0.43 | 0.97 |
Extreme and enhanced coefficient of acceleration response (1.5 g).
Shock excitation frequency (Hz) |
|
|
|
|
|
|
|
---|---|---|---|---|---|---|---|
28 | 1.585 | 0.3183 | 0.349 | 1.1753 | 1.41 | 1.54 | 5.19 |
29 | 1.5107 | 0.3103 | 0.2153 | 0.8097 | 1.44 | 1.00 | 3.75 |
30 | 1.4597 | 0.3043 | 0.1837 | 0.5517 | 1.46 | 0.88 | 2.65 |
31 | 1.471 | 0.2703 | 0.1393 | 0.3253 | 1.29 | 0.66 | 1.55 |
32 | 1.432 | 0.2513 | 0.1627 | 0.395 | 1.23 | 0.80 | 1.93 |
33 | 1.4934 | 0.292 | 0.1523 | 0.4393 | 1.37 | 0.71 | 2.06 |
34 | 1.4743 | 0.29 | 0.2407 | 0.623 | 1.38 | 1.14 | 2.96 |
36 | 1.5159 | 0.2917 | 0.2283 | 0.5117 | 1.35 | 1.05 | 2.36 |
35 | 1.5157 | 0.311 | 0.2237 | 0.5077 | 1.44 | 1.03 | 2.34 |
20 | 1.5256 | 0.2873 | 0.3037 | 0.8097 | 1.32 | 1.39 | 3.72 |
15 | 1.2108 | 0.2117 | 0.2387 | 0.6663 | 1.22 | 1.38 | 3.85 |
10 | 1.6187 | 0.214 | 0.483 | 0.7983 | 0.93 | 2.09 | 3.45 |
Enhancement coefficient curve of the acceleration response (0.02 g).
Enhancement coefficient curve of the acceleration response (0.1 g).
Enhancement coefficient curve of the acceleration response (0.5 g).
Enhancement coefficient curve of the acceleration response (0.75 g).
Enhancement coefficient curve of the acceleration response (1.5 g).
Destruction of the specimen side figure.
Finally, when shock excitation acceleration reached 2.4 g, severe destructive shaking occurred under shock excitation frequency of 5 Hz. After the experiment, the steel frame was demolished. Damage was mainly concentrated in the coal seam (Figure
Coal burst figure.
To summarize, the specimen stayed in the elastic deformation stage when the shock excitation acceleration peak was inputted at less than 0.5 g. If the shock excitation acceleration peak was inputted beyond 0.5 g, the specimen entered the inelastic deformation stage. Once shock excitation acceleration was determined, the shock excitation frequency’s influence on acceleration response became obvious. When shock excitation frequency was close to the specimen’s natural vibration, the acceleration response increased and a resonance phenomenon occurred once the shock excitation frequency and natural vibration were equal. During that time, the specimen grew prone to fracture due to the force of inertia.
To effectively study shock excitation acceleration peak influence on acceleration response, data measured under working conditions 7, 19, 39, and 49 were selected for analysis. For this set of data, shock excitation frequency is 24 Hz, and acceleration peaks are 0.02 g, 0.1 g, 0.5 g, and 0.75 g, respectively. The results are shown in Table
Peak value of acceleration response.
Working condition |
|
|
|
|
---|---|---|---|---|
7 | 0.0193 | 0.0677 | 0.056 | 0.1797 |
19 | 0.104 | 0.14 | 0.1239 | 0.2821 |
39 | 0.511 | 0.6839 | 0.7581 | 0.9751 |
49 | 0.7719 | 0.931 | 1.3251 | 1.1809 |
Relationship curve of acceleration response and shock excitation acceleration peak value.
Eight microseism signal sensors were installed in the specimen, detailed in Section
Effective microseism signals began to appear in working condition 29, in which 0.5 g shock excitation acceleration and 2 Hz frequency were inputted to the vibration table, and vibration cycle was 30.
Microseism signals were concentrated at 9 s–11 s from Figure
Microseism signals from different channels in working condition 29 (CH indicates the sensors’ serial numbers).
Resonance occurred in working condition 37, evidenced by analyzing acceleration response data. In this condition, a 0.5 g shock excitation acceleration and 20 Hz frequency were inputted to the vibration table, and the vibration cycle was 120. The specimen’s natural vibration frequency matched the result from Chapter 5.1 (which measured white noise and a shock excitation acceleration of 0.02 g) 42 Hz. When shock excitation acceleration changed, the state of destruction in the specimen also changed, causing the natural vibration frequency of the specimen to fluctuate.
Figure
Microseism signals of working condition 37.
In working condition 29, the first original microseism signal appeared. The IMF component of the 4# sensor can be obtained by decomposing this original signal using the EEMD method. The following section aims to introduce the HHT method used in this experiment by processing microseism signal from the 4# sensor.
The original microseism signal decomposed into 17 IMF components. Shown in Table
IMF component percentage of energy of original microseism signals by EEMD in working condition 29.
IMF component | Energy percentage (%) |
---|---|
IMF_h1 | 51.1 |
IMF_h2 | 19.3 |
IMF_h3 | 12.4 |
IMF_h4 | 12.4 |
IMF_h5 | 3 |
IMF_h6 | 0.795 |
IMF_h7 | 0.324 |
IMF_h8 | 0.639 |
IMF_h9 | 0.273 |
IMF_h10 | 11.6 |
IMF_h11 | 0.0243 |
IMF_h12 | 0.02 |
IMF_h13 | 0.0191 |
IMF_h14 | 0.00991 |
IMF_h15 | 0.00378 |
IMF_h16 |
|
IMF_h17 |
|
EEMD analysis results of original microseism signals from 4# sensor in working condition 29.
An effective microseism signal, shown in Figure
Effective microseism signals from 4# sensor in condition 29.
17 new IMF components were obtained from the effective microseism signal (shown in Figure
IMF component energy percentages of effective microseism signals by EEMD in working condition 29.
IMF component | Energy percentage (%) |
---|---|
IMF_h1 | 66.8 |
IMF_h2 | 15.6 |
IMF_h3 | 14.2 |
IMF_h4 | 2.88 |
IMF_h5 | 0.222 |
IMF_h6 | 0.115 |
IMF_h7 | 0.0434 |
IMF_h8 | 0.0249 |
IMF_h9 | 0.0356 |
IMF_h10 | 0.0218 |
IMF_h11 | 0.0113 |
IMF_h12 | 0.00279 |
IMF_h13 | 0.000226 |
IMF_h14 | 0.000126 |
IMF_h15 | 0.00023 |
IMF_h16 | 0.000115 |
IMF_h17 | 0.000104 |
EEMD analysis results of effective microseism signals from 4# sensor in working condition 29.
The FFT method was used to obtain spectra of IMF components. Shown in Figure
IMF components spectrum of effective microseism signals in working condition 29.
Hilbert energy spectrum and marginal spectrum are acquired by processing IMF components using Hilbert transform.
Figure
Hilbert energy spectrum of effective microseism signals in working condition 29.
The Hilbert marginal spectrum (Figure
Hilbert marginal spectrum of effective microseism signals in working condition 29.
In conclusion, effective microseism has a frequency range between 200 and 4200 Hz while main frequencies are 500, 1700, and 2470 Hz under shock excitation acceleration 0.5 g and 2 Hz vibration input to the vibration table.
IMF component energy percentage for selected effective microseism signals by EEMD.
IMF components | Working condition 29 |
Working condition 35 |
Working condition 37 |
Working condition 41 |
Working condition 47 |
---|---|---|---|---|---|
IMF_h1 | 66.8 | 80.3 | 10 | 85.4 | 55.9 |
IMF_h2 | 15.6 | 10.4 | 11.3 | 13.6 | 42.4 |
IMF_h3 | 14.2 | 5.74 | 61.2 | 0.49 | 1.23 |
IMF_h4 | 2.88 | 3.05 | 16.3 | 0.311 | 0.293 |
IMF_h5 | 0.222 | 0.374 | 0.959 | 0.0857 | 0.0826 |
IMF_h6 | 0.115 | 0.0826 | 0.146 | 0.0317 | 0.0314 |
IMF_h7 | 0.0434 | 0.0449 | 0.0343 | 0.016 | 0.0161 |
IMF_h8 | 0.0249 | 0.0198 | 0.0222 | 0.0088 | 0.00861 |
IMF_h9 | 0.0356 | 0.00868 | 0.00808 | 0.00421 | 0.0042 |
IMF_h10 | 0.0218 | 0.00266 | 0.00393 | 0.00286 | 0.00213 |
IMF_h11 | 0.0113 | 0.000972 | 0.00388 | 0.00128 | 0.0012 |
IMF_h12 | 0.00279 | 0.000577 | 0.00114 | 0.000586 | 0.000455 |
IMF_h13 | 0.000226 | 0.000359 | 0.00086 | 0.000305 | 0.000322 |
IMF_h14 | 0.000126 | 0.000242 | 0.00114 | 0.000217 | 0.000105 |
IMF_h15 | 0.00023 | 0.000248 |
|
|
0.000184 |
IMF_h16 | 0.000115 |
|
— |
|
— |
IMF_h17 | 0.000104 | — | — | — | — |
Effective microseism signals for certain working conditions (4# sensor).
Effective signals in working condition 29
Effective signals in working condition 35
Effective signals in working condition 37
Effective signals in working condition 41
Effective signals in working condition 47
HHT analysis results in working condition 35.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
HHT analysis results in working condition 37.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
HHT analysis results in working condition 41.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
HHT analysis results in working condition 47.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
In working condition 37 (with a shock excitation frequency of 20 Hz) the IMF3 component accounts for most of the energy percentage, where the frequency range is 200 Hz–2000 Hz and the main frequencies are 300 and 1500 Hz. 37 differs from other working conditions in that IMF1 has the maximum energy percentage and a dominant microseism signal frequency range of 200 Hz–4500 Hz and 500, 1700, and 2740 Hz as main frequencies. The microseism signal frequency shifts from high to low likely due to the increased stress caused by inertia effect when resonance occurs.
In summary, shock excitation frequency has a clear influence on microseism signals. When shock excitation frequency is far from natural vibration frequency, coal fractures randomly occur once stress reaches a critical point. The specific cause of fractures or the amplitude of rupture duration time are unknown, demonstrating that microseism possesses the same characteristics. If shock excitation is close to natural vibration frequency, a large number of fractures are produced. Obvious microseism signals appear in every vibration cycle where frequency shifts lower.
Effective microseism signals in certain working conditions.
Effective signals in working condition 43
Effective signals in working condition 53
Effective signals in working condition 63
As shown in Figure
From Figures
IMF component energy percentages of effective microseism signals by EEMD.
IMF components | Working condition 43 (%) | Working condition 53 (%) | Working condition 63 (%) |
---|---|---|---|
IMF_h1 | 70.7 | 64.1 | 64.5 |
IMF_h2 | 27.4 | 29.4 | 32.8 |
IMF_h3 | 1.31 | 5.03 | 1.9 |
IMF_h4 | 0.384 | 1.07 | 0.414 |
IMF_h5 | 0.0921 | 0.207 | 0.157 |
IMF_h6 | 0.0296 | 0.0651 | 0.0773 |
IMF_h7 | 0.0149 | 0.0285 | 0.0465 |
IMF_h8 | 0.00777 | 0.0172 | 0.027 |
IMF_h9 | 0.00509 | 0.00983 | 0.0136 |
IMF_h10 | 0.0021 | 0.00427 | 0.00626 |
IMF_h11 | 0.00103 | 0.00192 | 0.00445 |
IMF_h12 | 0.000539 | 0.000885 | 0.00212 |
IMF_h13 | 0.000197 | 0.000562 | 0.000543 |
IMF_h14 | 0.000227 | 0.000847 | 0.001 |
IMF_h15 |
|
0.000524 | 0.000759 |
IMF_h16 |
|
|
|
HHT analysis results of working condition 43.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
HHT analysis results of working condition 53.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
HHT analysis results of working condition 63.
IMF components
IMF components spectrum
Hilbert energy spectrum
Hilbert marginal spectrum
Based on these observations, we conclude that as shock acceleration and excitation peaks rise, the following phenomena occur: microseism signal density rises, amplitude enlarges, energy increases, number of fractures increases, dominant frequency shifts lower, and frequency range expands.
Vibration, which can cause coal destruction, reduce coal strength, and induce coal outbursts, is a very common phenomenon inherent to coal mining processes. When vibration occurs, energy signals such as microseism and EMR signals are generated and propagate. By receiving these signals and analyzing their characteristics, it is possible to evaluate damage and, ultimately, to dynamically predict disaster conditions.
Because of the complexity of geological conditions, it is difficult to simulate site environments accurately. In this paper, a simple experiment specimen was designed to effectively simulate a coal seam. A destruction situation within the specimen was simulated, and its microseism signal characteristics were gathered by acceleration and microseism sensor. The experimental data was then thoroughly analyzed. It was observed that, (1) with increased vibration, the specimen’s natural frequency decreases and fractures begin to appear. (2) Shock excitation frequency significantly affected the coal specimen acceleration when the shock excitation acceleration peak remained unchanged. When shock excitation frequency nears the specimen’s natural vibration frequency, the acceleration response peak increases and reaches its maximum when resonance occurs. (3) As shock excitation peak increases, the specimen’s acceleration response peak also increases until the specimen enters an inelastic deformation stage. (4) When shock excitation acceleration is 0.5 g and input frequency is 2 Hz, effective microseism signals in the frequency range 200–4200 Hz begin to appear, while the main frequencies are 500, 1700, and 2470 Hz. (5) When shock vibration acceleration is 0.5 g and input frequency is less than 20 Hz, microseism is relatively sparse. The microseism signals begin to vary wildly, and maximum amplitude appears at random. When shock excitation frequency is 20 Hz, microseism signals become dense and amplitude increases. When shock excitation frequency is more than 20 Hz, microseism signals become sparse again and amplitude decreases. (6) When the input frequency is 30 Hz, microseism signal density rises, amplitude enlarges, energy increases, number of fractures grows, dominant frequency shifts low, and frequency range expands as shock acceleration and excitation acceleration peaks increase.
Notably, these results were all obtained in experimental conditions—in-site situations are more complex. In a real-life scenario, signals are affected by many other relevant factors, such as distance and varying vibration forms that would likely alter microseism signals significantly.
The authors declare that there is no conflict of interests regarding the publication of this paper.
This research is supported by the National Natural Science Foundation of China (Grant no. 51274206) and Fundamental Research Funds for the Central Universities (Grant no. 2010YZ05). The authors would like to express their gratitude to these foundations.