MATLAB Predictive Maintenance Toolbox™ User's Guide

Description

Manage System Data Data Ensembles for Condition Monitoring and Predictive Maintenance Data Ensembles Ensemble Variables Ensemble Data in Predictive Maintenance Toolbox Convert Ensemble Data into Tall Tables Processing Ensemble Data Generate and Use Simulated Data Ensemble File Ensemble Datastore With Measured Data File Ensemble Datastore Using Data in Text Files Using Simulink to Generate Fault Data Multi-Class Fault Detection Using Simulated Data Preprocess Data Data Preprocessing for Condition Monitoring and Predictive Maintenance Basic Preprocessing Filtering Time-Domain Preprocessing Frequency-Domain (Spectral) Preprocessing Time-Frequency Preprocessing Identify Condition Indicators Condition Indicators for Monitoring, Fault Detection, and Prediction Signal-Based Condition Indicators Time-Domain Condition Indicators Frequency-Domain Condition Indicators Time-Frequency Condition Indicators Model-Based Condition Indicators Static Models Dynamic Models State Estimators Condition Indicators for Gear Condition Monitoring Extract Gear Condition Metrics Evaluate Features and Train Model Motor Current Signature Analysis for Gear Train Fault Detection Reconstruct Phase Space and Estimate Condition Indicators Using Live Editor Tasks Detect and Diagnose Faults Decision Models for Fault Detection and Diagnosis Feature Selection Statistical Distribution Fitting Machine Learning Regression with Dynamic Models Control Charts Changepoint Detection Rolling Element Bearing Fault Diagnosis Fault Diagnosis of Centrifugal Pumps Using Steady State Experiments Fault Diagnosis of Centrifugal Pumps Using Residual Analysis Fault Detection Using an Extended Kalman Filter Fault Detection Using Data Based Models Detect Abrupt System Changes Using Identification Techniques Chemical Process Fault Detection Using Deep Learning Predict Remaining Useful Life Feature Selection for Remaining Useful Life Prediction Models for Predicting Remaining Useful Life RUL Estimation Using Identified Models or State Estimators RUL Estimation Using RUL Estimator Models Choose an RUL Estimator Similarity Models Degradation Models Survival Models Update RUL Prediction as Data Arrives Similarity-Based Remaining Useful Life Estimation Wind Turbine High-Speed Bearing Prognosis Condition Monitoring and Prognostics Using Vibration Signals Nonlinear State Estimation of a Degrading Battery System Deploy Predictive Maintenance Algorithms Deploy Predictive Maintenance Algorithms Specifications and Requirements Design and Prototype Implement and Deploy Software and System Integration Production Diagnostic Feature Designer Explore Ensemble Data and Compare Features Using Diagnostic Feature Designer Perform Predictive Maintenance Tasks with Diagnostic Feature Designer Convert Imported Data into Unified Ensemble Dataset Visualize Data Compute New Variables Generate Features Rank Features Export Features to the Classification Learner Generate MATLAB Code for Your Features Prepare Matrix Data for Diagnostic Feature Designer Interpret Feature Histograms in Diagnostic Feature Designer Interpret Feature Histograms for Multiclass Condition Variables Generate and Customize Feature Histograms Organize System Data for Diagnostic Feature Designer Data Ensembles Representing Ensemble Data for the App Data Types and Constraints for Dataset Import Analyze and Select Features for Pump Diagnostics Isolate a Shaft Fault Using Diagnostic Feature Designer Model Description Import and Examine Measurement Data Perform Time-Synchronous Averaging Compute TSA Difference Signal Isolate the Fault Without a Tachometer Signal Extract Rotating Machinery Features Extract Spectral Features Rank Features Perform Prognostic Feature Ranking for a Degrading System Using Diagnostic Feature Designer Model Description Import and View the Data Separate Daily Segments by Frame Perform Time-Synchronous Averaging Extract Rotating Machinery Features Extract Spectral Features Rank Features with Prognostic Ranking Methods Automatic Feature Extraction Using Generated MATLAB Code Generate a Function for Features Generate a Function for Specific Variables, Features, and Ranking Tables Save and Use Generated Code Change Computation Options for Generated Code Generate a MATLAB Function in Diagnostic Feature Designer Import the Transmission Model Data Compute a TSA Signal Extract Features from the TSA Signal Generate a MATLAB Function Validate Function with the Original Data Apply Generated MATLAB Function to Expanded Data Set Anatomy of App-Generated MATLAB Code Basic Function Flow Inputs Initialization Member Computation Loop Outputs Ranking Ensemble Statistics and Residues Parallel Processing Frame-Based Processing Diagnostic Feature Designer Help Import Single-Member Datasets Selection — Select Data to Import Configuration — Configure Ensemble Variables Review — Review and Import Variables Import Multimember Ensemble Import Multimember Ensemble Ensemble View Preferences Group By Ensemble Representation by Members or Statistics Computation Options Data Handling Mode Results Return Location Use Parallel Computing Remove Harmonics Source Signal Filter Settings Time-Synchronous Signal Averaging Additional Information Filter Time-Synchronous Averaged Signals Signal Signals to Generate Speed Settings Filter Settings Additional Information Ensemble Statistics Interpolation Subtract Reference Order Spectrum Source Signal Rotation Information Window Settings Additional Information Power Spectrum Source Signal and Frequency Settings Algorithm Additional Information Signal Features Basic Statistics Higher-Order Statistics Impulsive Metrics Signal Processing Metrics Additional Information Rotating Machinery Features Signals to Use Metrics Using TSA Signals Metrics Using Difference Signals Metrics Using Regular Signals Metrics Using Mix of Signals Additional Information Nonlinear Features Phase-Space Reconstruction Parameters Approximate Entropy Correlation Dimension Lyapunov Exponent Additional Information Spectral Features Spectrum Spectral Peaks Modal Coefficients Band Power Frequency Band for All Features Additional Information Group Distances Additional Information Feature Selector Export Features to MATLAB Workspace More Information Export Features to Classification Learner More Information Export a Dataset to the MATLAB Workspace More Information Generate Function for Features