UWA
Friday 3 May 2024
Approaches to analysing time series ultimately follow the same process; identify the pattern in the data then use that pattern to draw a conclusion.
As a result there exists a plethora of methods for extracting these patterns, both for simple systems and more complex systems through dynamical system analysis.
These methods work generally well for time series data sampled with sufficient frequency, however issues begin to arise when the data we have is sampled infrequently or sporadically, and indeed at first glance it appears these methods can no longer be applied.
I will discuss how we can still utilise these pattern extraction techniques with data about important events alone.
I will focus on one specific dynamical system method which has shown impressive results in analysing financial and environmental event series, and I will highlight the direction of work in these fields.
Finally, I will bring these ideas back to maintenance by relating them to a common event series in our context; maintenance work orders.
This analysis facilitates finding patterns between failing assets, relating sensor data to asset health, and the eventual prediction and prevention of future failures.