Objective: Learn to easily import and visualize multiple signals or time series data sets to gain insights into the features and trends in the data.

• Import, visualize, and browse signals to gain insights
• Make measurements on signals
• Compare multiple signals in the time and frequency domain
• Perform interactive spectral analysis
• Extract regions of interest for focused analysis
• Recreate analysis with auto-generated MATLAB scripts

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

• Perform resampling to ensure common time base across signals
• Work with non-uniformly sampled data
• Find gaps in data and remove or fill gaps
• Remove noise and unwanted frequency content
• Perform wavelet denoising
• Use the envelope spectrum to perform fault analysis
• Locate outlier values in data and replace them with acceptable data
• Locate signal changepoints and use boundaries to automatically create signal segments

Objective: Apply different techniques in time and frequency domains to extract features. Become familiar with the spectral analysis tools in MATLAB and explore ways to bring out features for multiple signals.

• Locate peaks
• Locate desired signals from patterns in the time and spectral domains
• Use spectral analysis to extract features from signals
• Perform classification using supervised learning
• Use the Classification Learner app to interactively train and evaluate neural networks