Skill Guide

Signal processing fundamentals: FFT, wavelet transforms, envelope analysis, and spectral kurtosis

A core set of signal decomposition techniques (FFT, Wavelet, Envelope Analysis, Spectral Kurtosis) used to extract deterministic frequency content, non-stationary features, bearing fault frequencies, and impulsive/transient signal components from time-domain sensor data.

Enables predictive maintenance, condition monitoring, and root cause failure analysis by converting raw vibration/acoustic data into actionable diagnostic intelligence. Directly impacts operational uptime, safety, and capital expenditure by preventing catastrophic equipment failure.

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How to Learn Signal processing fundamentals: FFT, wavelet transforms, envelope analysis, and spectral kurtosis

Focus on: 1) Understanding the physics of vibration (displacement, velocity, acceleration sensors). 2) Mastering the discrete Fourier Transform (DFT/FFT): spectral leakage, windowing (Hanning, Flat-Top), and interpreting amplitude/phase spectra. 3) Basics of digital filtering (low-pass, high-pass, band-pass) and aliasing.

Focus on: 1) Applying Wavelet Transforms (CWT, DWT) for time-frequency analysis of non-stationary signals (e.g., startup/shutdown). 2) Implementing Envelope (Demodulation) Analysis for bearing fault detection (BPFI, BPFO, BSF). 3) Using Spectral Kurtosis (SK) to optimally design bandpass filters for impulsive signals. Common mistake: Applying FFT blindly to transient signals without pre-processing.

Focus on: 1) Integrating techniques into automated diagnostic pipelines (e.g., condition-based maintenance systems). 2) Adapting methods to complex, high-noise industrial environments (gearboxes, reciprocating machines). 3) Mentoring teams on interpretation and validating results with physical inspection data. Strategic alignment with reliability-centered maintenance (RCM) programs.

Practice Projects

Beginner

Project

FFT Spectrum Analysis of a Rotating Shaft

Scenario

You have time-domain vibration data (accelerometer) from a motor-pump assembly. The suspected fault is a shaft imbalance or misalignment.

How to Execute

1. Import the data into MATLAB/Python (SciPy). 2. Apply an FFT with a Hanning window. 3. Identify the dominant peaks in the spectrum (1x, 2x, 3x shaft speed). 4. Compare amplitudes to ISO 10816 vibration severity standards.

Intermediate

Project

Bearing Fault Detection Using Envelope Analysis

Scenario

Vibration data from a gearbox shows a broad-spectrum noise increase. A bearing inner race fault is suspected.

How to Execute

1. Apply a high-pass filter to remove low-frequency shaft vibration. 2. Compute the Hilbert transform of the filtered signal to get the analytical signal. 3. Calculate the envelope (magnitude of the analytical signal). 4. Perform an FFT on the envelope to find peaks at the bearing's characteristic fault frequencies (BPFI and harmonics).

Advanced

Project

Spectral Kurtosis-Based Filter for Gear Tooth Crack Detection

Scenario

A high-speed gearbox signal is contaminated by broadband noise. A localized gear tooth crack generates impulsive, cyclostationary signals.

How to Execute

1. Compute the Short-Time Fourier Transform (STFT) to get the spectrogram. 2. Calculate the Spectral Kurtosis (SK) for each frequency bin. 3. Identify the frequency band with the highest SK (indicating impulsiveness). 4. Design and apply a bandpass filter in that optimal band, then analyze the resulting signal using traditional time-synchronous averaging or envelope analysis.

Tools & Frameworks

Software & Platforms

MATLAB (Signal Processing Toolbox)Python (NumPy, SciPy, PyWavelets)NI LabVIEW (Sound & Vibration Toolkit)Brüel & Kjær PULSE, Siemens Simcenter Testlab

MATLAB/Python for algorithm prototyping and research. Commercial platforms (B&K, Siemens) for turnkey industrial data acquisition, processing, and reporting with calibrated sensor inputs.

Key Methodological Frameworks

ISO 10816 / ISO 20816 (Vibration Severity)Envelope Analysis (High-Frequency Resonance Technique)Cyclostationary Analysis (Second-Order)Order Tracking (for RPM-varying signals)

ISO standards provide quantitative acceptance/rejection criteria. Envelope Analysis is the industry-standard for bearing faults. Order Tracking is critical for analyzing machinery during run-up/coast-down.