How to improve identification of outliers for removal I have many datasets where the measured value is either "normal" (i.e. the process is running" or abnormal (i.e. process is not running).

Unfortunately, I don't have a measurement that clearly indicates that the process is running, so I have to make the determination from the measured value data itself. Visually, the outliers are obvious (see below).

I have had success with several different techniques


IQR based outlier identification
3 sigma rejection
simple cutoff
modified z-score


These all work some of the time, but I am looking for a better solution for my data.

If you look at the image below, the outliers are all of the points near zero. In this specific case a threshold of 0.01 easily eliminates all of the bad data. Unfortunately, the threshold required for a given dataset can vary significantly.

I would appreciate any thoughts.
You can try a quantile-quantile plot: you fit your data to a given distribution and plot your empirical distribution quantile against the theoretical quantiles. 

This can help you infer outliers in your sample as outliers for the fitted distribution.