Detect the time at which deviation occurs in time series data I working on multivariate time series data. I have sensor data generated by a machine every time it is operated. Data set consists of machine_ID(machines of same model), hours_ operated, measurements from various sensors. The machine starts to degrade after operating for certain hours. I would like to find the hours after which there is step change after which the performance starts to degrade. 

I want to do this using machine learning approach preferably and would like to plot the graph marking the deviation. Which ML techniques could be used for this approach.





I have performed exploratory data analysis where I could find the point at which there is deviation occurrence. Now, I want to confirm this by running a model to detect the occurrence of step change. In the figure above, the decline starts somewhere at 100 and decline gradually. Now, is there any way I could find this pint through models. 

I greatly appreciate any links or suggestion to deal with this.

Thanks in advance.
AnomalyDetection is an open-source R package to detect anomalies
  which is robust, from a statistical standpoint, in the presence of
  seasonality and an underlying trend.


A blogpost that introduces the package can be found here and more formal paper can be found here.