Digital Signal Processing

Introduction to Digital Signal Processing (1 day)

Digital Signal Processing (DSP) takes real-world signals like vibration, audio, voice, temperature, pressure, or position to manipulate and analyze these signals to extract information, detect patterns, and make real-time decisions. It encompasses techniques such as filtering, noise reduction, and signal analysis to enhance data quality and accuracy. DSP is crucial for tasks like sensor data processing, condition monitoring, adaptive control, and optimization in various domains, including smart factories. An understanding of digital signal processing fundamentals and techniques is essential for anyone whose work is concerned with signal processing applications.

This course introduces the basic concepts and principles underlying digital signal processing. Topics include resampling techniques, preprocessing techniques of continuous signals, spectral analysis with FFT, denoising and filtering techniques, wavelets, feature detection & extraction, and techniques to execute variability analysis of signals.

All concepts will be illustrated using real-world datasets from industrial applications (e.g. condition monitoring with vibration sensors).

Course Outline

  • Introduction to digital signal processing
  • Fields of application in smart factories
  • Resampling Techniques
    • Upsampling
    • Downsampling
    • Interpolation
    • Extrapolation
    • Spectral interpolation
  • Data Preprocessing
    • Window Sliding
    • Window Functions
  • Spectral Analysis
    • Fast Fourier Transform
    • Welch’s method
    • Detecting multiple repetitions
    • Feature extraction (frequency domain features)
  • Denoising
    • Gaussian filtering
    • Median filtering
    • Linear and non-linear trend filtering
    • Linear and non-linear trend filtering
  • Filtering Techniques
    • Low-pass filter
    • High-pass filter
    • Narrow-band filter
    • Butterworth filter
  • Wavelets
    • Introduction to time-frequency analysis
    • Continuous wavelet transforms
    • Discrete wavelet transforms
    • Feature detection and extraction using wavelets
  • Feature Detection and Feature Extraction
    • Local maxima and minima (peak detection)
    • Peak to Peak
    • Other band power spectrum features
  • Variability Analysis
    • Signal-to-noise ratio
    • Total and windowed variance
    • RMS
    • Entropy
    • Coefficient of variation

Who should attend

Maintenance and quality engineers, data scientists, signal processing engineers, statisticians, mathematicians, computer scientist and information technology professionals who need to get started with Digital Signal Processing and want to make better use of their data. Consultants whose objective is to put additional value on the latest innovative technology in the smart factory sector.

What you need to succeed

Basic programming skills in Python (e.g. our training courses Python for Smart Factory Analytics or Getting started with Python).

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