Rotating Machinery

Apply predictive maintenance development tools to rotating machinery applications, such as gearboxes, bearings, and motors

Apply predictive maintenance development tools to rotating machinery applications, such as gears, bearings, and motors. Physics-based rotating machinery features use knowledge of rotation rate and machine harmonics to isolate specific hardware degradation and faults.

Apps

Diagnostic Feature Designer

Interactively extract, visualize, and rank features from measured or simulated data for machine diagnostics and prognostics

Live Editor Tasks

Extract Spectral Features

Interactively extract spectral fault band metrics in the Live Editor (Since R2021a)

Functions

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Signal Processing for Rotating Machinery

`tsa`	Time-synchronous signal average
`tsadifference`	Difference signal of a time-synchronous averaged signal
`tsaregular`	Regular signal of a time-synchronous averaged signal
`tsaresidual`	Residual signal of a time-synchronous averaged signal
`ordertrack`	Track and extract order magnitudes from vibration signal
`rpmtrack`	Track and extract RPM profile from vibration signal
`convertVibration`	Compute baseline-corrected and filtered acceleration, velocity, and displacement signals from vibration measurements using a single sensor output from either an accelerometer, velocity sensor, or displacement sensor (Since R2024a)

Spectral Processing for Rotating Machinery

`pspectrum`	Analyze signals in the frequency and time-frequency domains
`envspectrum`	Envelope spectrum for machinery diagnosis
`orderspectrum`	Average spectrum versus order for vibration signal
`modalfrf`	Frequency-response functions for modal analysis
`bearingFaultBands`	Generate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction (Since R2019b)
`gearMeshFaultBands`	Construct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction (Since R2019b)
`faultBands`	Generate fault frequency bands for spectral feature extraction (Since R2019b)

Rotating Machinery Features

`bearingFaultBands`	Generate frequency bands around the characteristic fault frequencies of ball or roller bearings for spectral feature extraction (Since R2019b)
`gearMeshFaultBands`	Construct frequency bands around the characteristic fault frequencies of meshing gears for spectral feature extraction (Since R2019b)
`faultBands`	Generate fault frequency bands for spectral feature extraction (Since R2019b)
`faultBandMetrics`	Spectral metrics for the specified fault frequency bands of the power spectral density (PSD) (Since R2019b)
`gearConditionMetrics`	Standard metrics for gear condition monitoring
`rpmtrack`	Track and extract RPM profile from vibration signal

Topics

Condition Indicators for Gear Condition Monitoring
Follow this workflow to identify and evaluate condition indicators for gear condition monitoring.
Isolate a Shaft Fault Using Diagnostic Feature Designer
This example shows how to isolate a shaft fault from simulated measurement data for machines with varying rotation rates.

Featured Examples

Rolling Element Bearing Fault Diagnosis

Perform fault diagnosis of a rolling element bearing based on acceleration signals.

Open Live Script

Rolling Element Bearing Fault Diagnosis Using Deep Learning

Perform fault diagnosis of a rolling element bearing using a deep learning approach.

Open Live Script

Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data

Detect anomalies in industrial-machine vibration data using machine learning and deep learning.

Open Live Script

Broken Rotor Fault Detection in AC Induction Motors Using Vibration and Electrical Signals

Use the Diagnostic Feature Designer app to detect broken rotor faults in AC induction motors using vibration and electrical signals.

Open Live Script

Remaining Useful Life Estimation Using Convolutional Neural Network

Predict the RUL of engines using deep convolutional neural networks (CNN).

Open Live Script

Live RUL Estimation of a Servo Gear Train Using ThingSpeak

Estimate the Remaining Useful Life (RUL) of a servo motor gear train through real-time streaming of servo motor data from an Arduino-based data acquisition system to ThingSpeak™, and from ThingSpeak to an RUL estimation engine running in MATLAB®.

Open Live Script

Analyze Gear Train Data and Extract Spectral Features Using Live Editor Tasks

Use Live Editor tasks to construct fault bands and extract spectral features for fault detection and identification.

Open Live Script