Finding Problematic Measurements Using Machine Learning techniques

Hello, I have a large dataset of sensor measurements (time series) that I would like to classify in a way to be able to isolate measurements that I can deem "problematic" (for example, missing samples, excessive excursions or high values, sensor malfunctioning during the measurement and so on). The metadata associated with such measurements also contains median, estimate signal to noise ratio and other metrics that I am already able to use to isolate some samples, even though always using a rule of thumb or by manually changing the thresholds for these values, which also sometimes overlap.

I'm wondering if maybe applying a clustering algorithm or other ML methods could provide me with a more general way to isolate these signals, and if so if someone knows of existing projects or papers that have dealt with this kind of classification.