Table of Contents


The first section references where we got this data, and the second shows how to manipulate the source data into something Python likes better. This dataset was then loaded online for public acces. So you can skip these sections and jump straight to plotting the time series data (which will reload the now "cleaned" data).

Data Source

Data was downloaded from the Case Western Reserve Bearing Data Center where they did some seeded fault testing and shared the data. This data can be downloaded here. An overview of the procedure is here.

For simplicity we will look at 4 data files:

All of these were when the motor was running at approximately 1772 RPM. The numbers in the fault file names signify the fault diameter in thousands of an inch. For example Fault_014 is a fan end bearing fault in the inner race measuring 0.014" in diameter.

In the appendix I show you how to pull out the data of interest from the source .MAT files. But I've uploaded the processed data into a CSV file which can be loaded directly from online. So we can skip those earlier steps (but they've been included for reference).

Plot Time Series Data

First let's plot the full dataset but render it as a SVG because it is so big.

Now let's get the full benefit of Plotly and render a the middle 1 second of the file as an interactive element. We'll also subtract 4.5 seconds from the time domain data so that these middle plots start at 0 and look good.