You might be able to try outlier detection to identify unusual test cycles. Though I've heard that it's often better if you're able to label even a small amount of data for whether it's anomalous or not, because an outlier detection method doesn't know which features are important or not, and labeled data can teach ML which features are important.

Feature representation might be tricky but a simple way to start is min, max, avg, stddev of each sensor.

To segment test cases, you could make it into a machine learning problem by predicting whether time T is the start of a cycle, trained from some labeled data. I imagine that getting good results will depend on how you represent the features of "before time T" and "after time T"

Not my area of expertise but I hope this helps!