I'm working with degradation data and are trying do use change detection methods to detect repairs. Since I'm looking for repairs I'm only interested in positive changes. Between the repairs the data has a negative trend, differing between the periods.
I'm currently applying lowess as a first step since the data is rather noisy. The detection is done by computing the one step gradient and detecting points with a gradient higher than a set limit.
This approach is working relatively well byt I would like to compare with some other approaches. Due to the negative trend between repairs I'm not sure which methods are appropriate. Any sugestions would be much appreciated :)
An additional note is that repairs have a minimum interval which means after detection there shouldn't be any other detections within a set period. If multiple detections are made some sort of ranking would be suitable. My current idea is the largest gradient but I have a feeling that the performance won't be what I'm looking for.
Thank you in advance. Cheers!