Affordable Shake Table: Shakebot for Engineering Research

shakebot

Shake tables provide a critical tool for simulating earthquake events and testing the response of structures to seismic forces. However, most existing shake tables are either prohibitively expensive or proprietary, limiting their accessibility for educational and research purposes. To bridge this gap, we present Shakebot, a low-cost, open-source shake table designed specifically for engineering research and education.

Key Features of Shakebot

The Shakebot is built using affordable, high-precision components. This approach not only keeps the cost down but also ensures the reliability and accuracy needed for vibration testing. Here are some of the standout features:

  • Open-Source Software with ROS: The shake table runs on the Robot Operating System (ROS), allowing for modular software integration and facilitating easy transition from simulation to physical experiments.
  • Accessible and User-Friendly: The Shakebot’s low cost and simple setup make it ideal for students, educators, and researchers, especially in low-resource settings.

Specifications:

  • Degree of Freedom: Single 
  • Movement Degree: Horizontal Table 
  • Dimensions: 290 x 810 mm (excluding control box)
  • Payload: 50 kg @ 1 g 
  • Velocity: 2,000 mm/s 
  • Stroke: +/-140 mm (380 mm) 
  • Max. Operating Frequency @ 1kg 10mm: 25 Hz 
  • Position Precision: 0.08 mm
  • Max. acceleration at 5 kg and 2,000 mm/s: 5 g
  • Software: Python (supported by Robot Operating System)

Seeing is Believing

Watch the demonstration video of shakebot by clicking the link below:


Application in Precariously Balanced Rocks (PBR) Research

One of the most exciting applications of the Shakebot is in the study of Precariously Balanced Rocks (PBRs) by CALTECH. PBRs serve as natural markers that indicate the absence of significant seismic activity in an area. With the Shakebot, the researchers validated the simulations through physical testing.

By reusing the control programs developed in ROS, the CALTECH researches ensured that the ground motions used in simulations are identical to those applied in the physical experiments. This consistency was vital for validating the accuracy of the simulation and ensuring reliable results in both virtual and real-world settings.

Why Choose Shakebot?

  • Affordable: Built using readily available components, Shakebot offers a high-performance solution at a fraction of the cost of traditional shake tables.
  • Open-Source: With ROS-based software, Shakebot allows for extensive customization, making it suitable for a wide range of seismic research applications.
  • Versatile: Whether you are testing small-scale structures or conducting PBR studies, Shakebot is a highly flexible tool for both education and research.

QuakeLogic Is Proud to Offer Shakebot

At QuakeLogic, we are committed to providing innovative seismic monitoring and testing solutions. Shakebot is a proud addition to our product lineup, designed to meet the needs of researchers, educators, and engineers seeking an affordable yet high-precision shake table.

About QuakeLogic

QuakeLogic is a leading provider of advanced seismic monitoring solutions, offering a range of products and services designed to enhance the accuracy and efficiency of testing, data acquisition, and analysis.

Contact Information:

For more information about our products and services, please visit our website or contact our sales team. We are here to help you with all your testing and monitoring needs.

With Shakebot, QuakeLogic continues its mission to democratize earthquake engineering tools, ensuring that vital seismic research and education are accessible to everyone.

40-Ton Uniaxial and Biaxial Hydraulic Shake Tables

QuakeLogic is proud to introduce cutting-edge 40-ton uniaxial and biaxial hydraulic shake tables, designed for a wide range of seismic testing applications. These advanced tables are engineered for precision, power, and versatility, allowing researchers and engineers to simulate earthquake forces on large structures with high fidelity. Whether you are involved in civil engineering, aerospace, automotive testing, or seismic research, QuakeLogic’s shake tables provide the reliable performance you need to push the boundaries of seismic testing.

Key Specifications

1. Load Capacity

  • Maximum Payload: 40 tons (392 kN) at 1g acceleration.
  • Table Dimensions: 4000 mm x 4000 mm.

2. Motion and Speed

  • Effective Stroke: ±350 mm for both X and Y axes, ensuring large displacement capacity for simulating real-world seismic events.
  • Maximum Operating Speed: 1000 mm/s.
  • Continuous Operating Speed: 200 mm/s.

3. Performance and Frequency

  • Maximum Operating Frequency: 20 Hz.
  • Maximum Table Acceleration: Up to 2g for payloads less than 20 tons.

4. Overturning Moment Capacity

  • Overturning Moment: 1200 kN.m (for a 40-ton payload at 3-meter height), ensuring stability and performance even during extreme seismic events.

Advanced Hydraulic System

QuakeLogic’s 40-ton biaxial shake table is powered by an advanced hydraulic system, designed to provide seamless performance during demanding tests. Key features of the hydraulic system include:

  • Hydraulic Actuators: Designed for reliability, each actuator has a force capacity of 600 kN and 525 kN, with double-ended cylinders providing ±350 mm stroke. These actuators come with integrated position transducers (0.001 mm resolution) and load cells for precise control.
  • Hydraulic Power Unit (HPU): Equipped with a 300 LPM variable displacement pump and a 3000-liter tank with 160 kW installed power. The HPU is capable of running earthquake simulations with peak performance while maintaining optimal energy efficiency.
  • Accumulator Skid: With a 450-liter oil and 1800-liter nitrogen capacity, the system ensures smooth hydraulic operation during high-speed movements and complex earthquake simulations.

Control and Simulation Capabilities

The multi-axes control system is designed to offer real-time, high-fidelity control of the shake table. With closed-loop PID control and 16-bit analog inputs/outputs, the system ensures accurate position and force control with a response time of less than 10 ms. This allows the shake table to simulate even the most demanding seismic scenarios, ensuring that the data generated during testing is both accurate and reproducible.

Key Features of the Control System:

  • Real-Time Earthquake Data Simulation: Load real earthquake data for realistic seismic testing.
  • Advanced Signal Generator: Customizable sine waves, advanced modes, and unlimited profile length ensure flexibility.
  • Data Visualization and Analysis: FFT, response spectrum, and baseline correction are integrated into the user interface for easy data analysis.
  • Advanced PID Tuning: Model-based tuning for precise control during complex testing scenarios.

Applications

The 40-ton uniaxial and biaxial shake tables are versatile enough to serve multiple industries:

  • Civil Engineering: Testing the resilience of building structures, bridges, and other critical infrastructure components under simulated earthquake conditions.
  • Aerospace and Automotive: Simulating vibrations and seismic forces on sensitive components to ensure durability and safety.
  • Energy Sector: Testing equipment used in power generation and transmission to verify their performance under seismic stress.
  • Research Institutions: Universities and labs can use these shake tables to conduct cutting-edge research on seismic behavior and new materials.

Installation and Maintenance

The system’s modular design ensures straightforward installation, even for complex configurations. Key components such as the THK linear guides offer low dust generation and noise reduction, making the system well-suited for laboratory environments. Additionally, maintenance is simplified with filter replacement and hydraulic system checks easily integrated into the operational workflow.

For more information, visit the product page by clicking HERE.

Why Choose QuakeLogic?

  1. Proven Performance: QuakeLogic’s shake tables have been installed and are in use at leading research facilities worldwide.
  2. Custom Solutions: Tailored configurations to meet specific testing needs, whether uniaxial or biaxial.
  3. Expert Support: Our team works closely with clients to ensure successful system installation, operation, and ongoing maintenance, offering full lifecycle support.

About QuakeLogic

QuakeLogic is a leading provider of advanced seismic monitoring solutions, offering a range of products and services designed to enhance the accuracy and efficiency of testing, data acquisition, and analysis.

Contact Information:

For more information about our products and services, please visit our website or contact our sales team. We are here to help you with all your testing and monitoring needs.

Handling Non-Uniformly Spaced Data Using NUFFT and Sinc Interpolation

When processing non-uniformly spaced data in fields like MRI, radar, or geophysics, it’s crucial to use methods that can handle irregular sampling. In this blog, we discussed two important techniques: Non-Uniform FFT (NUFFT) and Time-domain Sinc Interpolation after regridding the data onto a uniform grid.

Here’s how we integrate both approaches using Python for practical applications.

Step-by-Step Workflow: NUFFT and ResampleSINC Interpolation

  1. Simulate Non-Uniformly Sampled Data: First, create a sinusoidal signal on a uniform grid, then sample it non-uniformly (like in MRI k-space).
  2. Apply NUFFT: Use NUFFT to handle the non-uniform data directly.
  3. Regrid Non-Uniform Data to Uniform Grid: Use interpolation methods like interp1d to map the data to a uniform grid.
  4. Apply ResampleSINC: Use the resampleSINC function for precise interpolation onto the uniform grid.

Let’s look at the complete Python code:

import numpy as np
import matplotlib.pyplot as plt
from pynufft import NUFFT
from scipy import interpolate

# Step 1: Generate a signal on a uniform grid
N = 256  # Number of points in the signal
x = np.linspace(0, 2*np.pi, N)
signal = np.sin(5*x) + np.sin(15*x)  # A test signal with two frequencies

# Step 2: Simulate non-uniform sampling
non_uniform_grid = np.sort(np.random.rand(N) * 2 * np.pi)

# Step 3: Perform NUFFT
nufft = NUFFT()
nufft.plan(non_uniform_grid, (N,), (256,))
signal_non_uniform = np.interp(non_uniform_grid, x, signal)
nufft_transform = nufft.forward(signal_non_uniform)

# Step 4: Regrid non-uniform data onto uniform grid using cubic interpolation
uniform_grid = np.linspace(0, 2*np.pi, N)
regridded_signal = interpolate.interp1d(non_uniform_grid, signal_non_uniform, kind='cubic')(uniform_grid)

# Step 5: Apply ResampleSINC for precise interpolation
def resampleSINC(signal, x, u):
    """
    Resample a signal using sinc interpolation in time domain
    Parameters:
    - signal : original signal on non-uniform grid
    - x : non-uniform grid
    - u : uniform grid to resample onto

    Returns:
    - interpolated_signal : the resampled signal on the uniform grid
    """
    interpolated_signal = np.zeros_like(u)

    for i in range(len(u)):
        interpolated_signal[i] = np.sum(signal * np.sinc((u[i] - x) / (x[1] - x[0])))

    return interpolated_signal

# Apply ResampleSINC on regridded signal
sinc_interpolated_signal = resampleSINC(regridded_signal, uniform_grid, uniform_grid)

# Step 6: Plot the results
plt.figure(figsize=(12, 6))

# Original and Non-Uniform Sampled Signal
plt.subplot(1, 2, 1)
plt.plot(x, signal, label='Original Signal')
plt.scatter(non_uniform_grid, signal_non_uniform, color='red', label='Non-uniform Samples')
plt.legend()
plt.title('Original and Non-Uniformly Sampled Signal')

# Sinc Interpolated Signal
plt.subplot(1, 2, 2)
plt.plot(x, signal, label='Original Signal')
plt.plot(uniform_grid, sinc_interpolated_signal, label='Sinc Interpolated Signal', linestyle='dashed')
plt.legend()
plt.title('Sinc Interpolation after Regridding')
plt.show()

Workflow Breakdown

  1. Simulate Non-Uniform Sampling: We create a simple sinusoidal signal on a uniform grid and simulate non-uniform sampling, common in MRI k-space.
  2. Regrid to Uniform Grid: Using cubic interpolation (interp1d from scipy), we map the non-uniform samples onto a uniform grid to facilitate subsequent operations like FFT or interpolation.
  3. ResampleSINC: The resampleSINC function is applied after regridding to precisely interpolate the signal onto a uniform grid. This technique uses sinc interpolation, which is ideal for band-limited signals when applied to uniformly spaced data.
  4. Plot Results: We visualize the original, non-uniform samples, and the signal reconstructed using sinc interpolation. As seen in the plot, the sinc-interpolated signal closely follows the original signal, providing an accurate reconstruction.

Conclusion

When working with non-uniformly spaced data, especially in fields like MRI or geophysics, it’s essential to use the right tools to process and reconstruct the signal. This blog demonstrated two key techniques:

  • NUFFT allows direct handling of non-uniform data without interpolation, providing accurate frequency-domain transforms.
  • ResampleSINC is ideal for signal reconstruction after regridding non-uniform data to a uniform grid, ensuring minimal distortion.

These methods, when used together, offer a powerful solution for handling non-uniformly sampled signals. By first regridding and then applying sinc interpolation, you ensure your final signal is both smooth and faithful to the original data.

Reference

Dr. Erol Kalkan, P.E. (2024). Time-domain Sinc Interpolation (Resampling) (https://www.mathworks.com/matlabcentral/fileexchange/59027-time-domain-sinc-interpolation-resampling), MATLAB Central File Exchange. Retrieved September 17, 2024.

If you’re dealing with non-uniform sampling in your field, contact QuakeLogic today for advanced solutions at info@quakelogic.net.

Let us show you how our state-of-the-art technologies can transform your signal processing workflows.