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1. Mahavir Education Trust’s Shah & Anchor Kutchhi Engineering College Chembur, Mumbai- 400088 (An Autonomous Institute Affiliated to University of Mumbai) Department of Electronics & Communication (Advanced Communication Technology) Signals of Progress: Unleash the Power of Advanced Communications presentation on Real-Time Digital Filtering in PLC for Process Control (Subject: Digital Signal Processing (ACCOR1PC201), V Semester) by Dignag Pakhare ( 124BATC2007 ) Vinayak Mali (23UF18409AC021) Krishna Prajapati (23UF18402AC019)

2. What is a Digital Filter? Desired Features Example of Filtering Operations Types of Digital Filters FIR Filters IIR Filters Filter Specification Content

3. What is a digital filter? Digital Filter: Numerical procedure of algorithm that transform a given sequence of numbers into a second sequence that has some more desirable properties .

4. Objective To study vibration signal characteristics of electrical motors. To apply DSP techniques for condition monitoring. To identify early signs of mechanical or electrical faults. To improve system reliability and reduce maintenance cost.

5. What is predictive Maintanence? Definition: A data- driven maintenance approach that predicts equipment failures using sensor data. Steps: Data Collection Signal Processing Fault Detection Maintenance Decision Benefit: Reduces unplanned breakdowns and saves operational costs. Predictive maintenance process diagram

6. Vibration in Motors Motors vibrate due to mechanical or electrical imbalances. Common causes: Shaft Misalignment Bearing Defects Rotor Imbalance Electrical Noise These faults create distinct frequency signatures in vibration data.

7. Signal Acquisition and Sensors Sensor Used: Piezoelectric Accelerometer. Converts mechanical vibration → electrical signal. Sampling: Follows Nyquist Rate to capture accurate vibration frequencies. Data Flow: Vibration → ADC → Digital Signal → DSP Analysis accelerometer data acquisition diagram

8. Digital Signal Processing Techniques Filtering: Removes noise using Low/High/Band- pass filters. FFT (Fast Fourier Transform): Converts time- domain signal to frequency- domain. Envelope Detection: Highlights small periodic impacts (useful for bearing faults). Wavelet Transform: For analyzing non- stationary signals. Feature Extraction: RMS, Kurtosis, Crest Factor, Skewness. Sample Representation of Frequency Spectrum after FFT Time- Domain Waveform

9. Feature RMS Kurtosis Crest Factor Skewness Meaning Energy of vibration Peak sharpness Peak- to- RMS ratio Wave symmetry Indicates Fault intensity Impulsive faults Bearing defects Unbalance Feature Analysis

10. Fault Detection and Classification Fault detection identifies abnormal vibration patterns. Techniques: Threshold- based detection using DSP. AI/ML for automated classification. Common Faults Detected: Bearing damage Rotor imbalance Shaft misalignment

11. Case Example A 3- phase induction motor was monitored using accelerometers. FFT analysis showed harmonics at bearing defect frequency. Early fault detection reduced downtime by 40% and avoided breakdown. Demonstrates real- world efficiency of DSP in predictive maintenance. bearing fault FFT spectrum Diagnosis(Sample)

12. Advantages Detects faults at early stage. Reduces machine downtime and maintenance cost. Extends equipment lifespan. Improves safety and production quality. Enables IoT- based smart monitoring.

13. Challenges Signal noise interference. Sensor placement errors. Need for high sampling and processing power. Difficulty in analyzing non- linear vibration patterns.

14. Conclusion DSP techniques enable accurate vibration analysis. Early fault detection ensures operational reliability. Predictive maintenance helps industries save time, energy, and cost. A sustainable approach aligned with modern smart manufacturing.

15. References Sakshat Virtual Labs ResearchGate Proakis , J. G. , & Manolakis , D. G. — Di g ital Si g nal Processing: Principles , Al g orithms , and Applications. IEEE Xplore: “Vibration- based Condition Monitoring using FFT.” ScienceDirect: “Predictive Maintenance of Induction Motors using Si g nal Processing.” Sprin g er: “Feature Extraction Techniques in Motor Health Monitoring.”