Objective:Bring data into MATLAB and organize it for analysis, including handling missing values. Process raw imported data by extracting and manipulating portions of data.

• Store data using MATLAB data types
• Import with datastores
• Process data with missing elements
• Process big data with tall arrays

Objective:Use unsupervised learning techniques to group observations based on a set of condition indicators and discover natural patterns in a data set.

• Find natural clusters within data
• Perform dimensionality reduction
• Evaluate and interpret clusters within data

Objective:Use supervised learning techniques to perform predictive modelling for classification problems. Evaluate the accuracy of a predictive model.

• Classify with the Classification Learner app
• Train classification models from labeled data
• Validate trained classification models
• Improve performance with hyperparameter optimization

Objective:Interactively explore and visualize signal processing features in data.

• Import, visualize, and browse signals to gain insights
• Make measurements on signals
• Compare multiple signals in the time and frequency domains
• Perform interactive spectral analysis
• 提取regions of interest
• Generate MATLAB scripts for automation

Objective:Learn techniques to clean signal sets with operations such as resampling, removing outliers, and filling gaps. Interactively generate and rank features.

• Use resampling to handle nonuniformly sampled signals
• Fill gaps in uniformly sampled signals
• 进行重采样,确保共同的时基开头ss signals
• Use the Signal Analyzer app to design and apply filters
• Use File Ensemble Datastore to import data
• Use the Diagnostic Feature Designer app to automatically generate and rank features
• Perform machinery diagnosis using envelope spectrum
• Locate outliers and replace with acceptable samples
• Detect changepoints and perform automatic signal segmentation

Objective:Explore data to identify features and train decision models to predict remaining useful life.

• Select condition indicators
• Use lifespan data to estimate remaining useful life using survival models
• Use run-to-threshold data to estimate remaining useful life using degradation models
• Use run-to-failure data to estimate remaining useful life using similarity models