GopalKrishna Advanced Rural Research Foundation

GARRF Virtual Engineering & Electronics Laboratory

KALAM ZERO RESEARCH FUNDING LAB

EXPERIMENT 22 ACTIVE FILTER LOW-PASS FILTER SIGNAL CONDITIONING

πŸ”¬ Experiment 22 β€” Active Low-Pass Filter

In Experiment 21, we learned how an operational amplifier can provide controlled voltage amplification. In this experiment, we use an op-amp together with a resistor-capacitor network to create an active low-pass filter.

A low-pass filter allows lower-frequency components of a signal to pass while progressively reducing higher-frequency components.

🎯 Experiment Objectives
  • Understand the purpose of a low-pass filter.
  • Understand the role of resistance and capacitance.
  • Calculate the cutoff frequency.
  • Observe frequency-dependent attenuation.
  • Understand the βˆ’3 dB cutoff point.
  • Study the effect of amplifier gain.
  • Observe time-domain input and output signals.
  • Connect filtering to sensor and PZT signal conditioning.
πŸ’‘ Think Before You Start
  • Why does a capacitor behave differently at different frequencies?
  • Why are high frequencies attenuated?
  • What happens when the input frequency is below the cutoff frequency?
  • What happens when it is far above the cutoff frequency?

πŸ“š 1. What Is a Low-Pass Filter?

A low-pass filter is a frequency-selective circuit that passes low-frequency signals while attenuating high-frequency signals.

A simple first-order RC low-pass filter consists of a resistor and capacitor.

fc = 1 / (2Ο€RC)

where:

Important:

At the cutoff frequency, the magnitude of the simple first-order low-pass response is approximately βˆ’3 dB relative to its low-frequency gain.

βš™οΈ 2. Virtual Experiment Controls

🟒 Filter ready.

πŸ”Œ 3. Virtual Circuit

πŸ“Š 4. Live Filter Results

Input Frequency 100 Hz
Cutoff Frequency 159.15 Hz
Frequency Ratio 0.63
Filter Gain 1.63Γ—
Output Amplitude 1.63 V
Attenuation βˆ’1.78 dB
Phase Shift βˆ’32.1Β°
Region PASSBAND

πŸ“ˆ 5. Time-Domain Waveform

The input is shown together with the filtered output. As frequency increases relative to the cutoff frequency, the output amplitude decreases and phase shift increases.

πŸ“‰ 6. Frequency Response β€” Bode-Style View

The vertical axis represents relative magnitude in decibels. The horizontal axis is logarithmic frequency.

πŸ“ 7. Filter Mathematics

fc = 1 / (2Ο€RC)
|H(f)| = 1 / √[1 + (f/fc)²]
Aactive = Av Γ— |H(f)|
Ο† = βˆ’tanβˆ’1(f/fc)
Engineering interpretation:

Below the cutoff frequency, the filter response is comparatively flat. Above the cutoff frequency, the response progressively decreases. For a first-order RC response, the attenuation approaches approximately βˆ’20 dB/decade at high frequency.

πŸ”¬ 8. Current Engineering Analysis

πŸ§ͺ 9. Perform the Experiment

  1. Start with the default component values.
  2. Observe the calculated cutoff frequency.
  3. Set the input frequency well below the cutoff frequency.
  4. Observe the output amplitude.
  5. Set the input frequency close to the cutoff frequency.
  6. Observe the βˆ’3 dB region.
  7. Increase the frequency substantially above the cutoff.
  8. Observe attenuation and phase shift.
  9. Change R and observe how the cutoff frequency changes.
  10. Change C and repeat the experiment.
Research Question:

If you double the resistance while keeping the capacitance constant, what should happen to the cutoff frequency?

🎯 10. Cutoff-Frequency Investigation

The cutoff frequency is controlled by the product RC. Try the following combinations and compare their cutoff frequencies.

Trial R C Expected Relationship
1 10 kΞ© 100 nF Reference
2 20 kΞ© 100 nF Cutoff decreases
3 10 kΞ© 200 nF Cutoff decreases
4 5 kΞ© 100 nF Cutoff increases

🧠 11. Design Challenge

Design a filter with a cutoff frequency as close as possible to 160 Hz.

Try to achieve this using approximately:

Then set the input frequency approximately equal to the cutoff frequency and investigate the response.

🎯 Challenge not yet evaluated.

πŸ§ͺ 12. Student Observation Table

Trial Frequency R C Cutoff Gain Output Attenuation

πŸ“‘ 13. PZT & Structural Health Monitoring Connection

Why is filtering important for PZT sensors?

A PZT sensor can respond to a wide range of mechanical and electromagnetic disturbances. A measurement system may need to retain the frequency range containing useful structural information while reducing unwanted high-frequency noise.

A low-pass filter can therefore form part of an analog signal-conditioning chain before digitization.

PZT β†’ Signal Conditioning β†’ Filter β†’ ADC β†’ Computer β†’ AI/ML β†’ SHM Decision

The filter does not "understand" the structure. It simply performs frequency-selective processing. The subsequent digital processing or AI system can then work with a cleaner signal.

🌍 14. Real-World Applications

❓ 15. Student Quiz

Q1. A low-pass filter primarily allows:
Q2. The cutoff frequency of an RC low-pass filter is:
Q3. At approximately the cutoff frequency, the first-order filter magnitude is:
Q4. Increasing R while keeping C constant will:
Q5. A filter can be useful in PZT sensor systems for:

πŸ€– CHITTI

GARRF Robotics & Engineering AI Mentor

Use CHITTI to explore filters, electronics, sensors, robotics, signal conditioning and engineering applications.

πŸ’‘ Try asking:

"Why does a capacitor block high-frequency signals in this circuit?"

"How do I calculate the cutoff frequency?"

"What happens at βˆ’3 dB?"

"How can I design a filter for a PZT sensor?"

"What is the difference between analog and digital filtering?"

⚠️ 16. Physical Laboratory Safety

Important:

This is an educational virtual simulation. When constructing a physical filter, verify component ratings, op-amp supply voltage, wiring and measurement equipment before applying power.

Never connect experimental electronics directly to mains voltage.

πŸŽ“ 17. Experiment Conclusion

In this experiment, we used resistance and capacitance to create a frequency-selective low-pass filter and investigated its behaviour with an active amplifier stage.

The experiment demonstrates one of the central ideas in signal processing: different frequencies can be treated differently by an engineered system.

This principle is fundamental to instrumentation, robotics, communications, vibration analysis and structural health monitoring.

From analog filtering to intelligent signal analysis β€” this is the beginning of the signal-processing pathway.