Anomaly detection on multidimensional time series
One option is to treat it as a "supervised learning" problem.

Since 


  a set of n data, measured every x ms


You have a time series. This can be treated as a supervised problem. 

Say that you have observations for 10 minutes. In supervised learning, each sample would be :


X : Set of last t-1 , t-2,  t-3 and t-N observations (N is fixed, say 300)
Y : Observation t 


You can train a model to predict next observation, given last 300 observations. If actual value is beyond a  certain % from the prediction, it is an anomaly.

Example : https://machinelearningmastery.com/multivariate-time-series-forecasting-lstms-keras/