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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.

Here is how the plot of values 'sensor 2' from run-to-failure

Snapshot of data

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.

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  • $\begingroup$ Do you have a time series for the performance measurement as well? $\endgroup$ – Louis T Nov 8 '17 at 21:04
  • $\begingroup$ No, all that I have is run-to-failure data of each machine. There are about 100 machines. Max hours of operation is 386 and min is 127. $\endgroup$ – Katherine Nov 8 '17 at 21:21
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    $\begingroup$ Are you trying to predict when the machine is going to fail then? If you don't have performance measurement then how do you define the "deviation" in performance in the first place? $\endgroup$ – Louis T Nov 8 '17 at 21:28
  • $\begingroup$ Eventually my goal is to predict failure time but the data is synthesized and has unrealistic run time. In the sense, from the plot of hours of operation, degradation is linear till the failure. I have gone through few research papers which use piece-wise linear approximation to have a maximum limit of time to failure till there is step change. So, I would to run a model which could learn from the features to find when there is a deviation/step change (after x hours). So, I could set maximum operating hours to x hours till that point is reached. $\endgroup$ – Katherine Nov 8 '17 at 21:36
  • $\begingroup$ "degradation is linear till the failure". if you don't have any measure of performance, how are you measuring the degradation here? Can you include a snapshot of your dataset to make this clearer? $\endgroup$ – Louis T Nov 8 '17 at 23:45
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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.

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  • $\begingroup$ Thank you for sharing the links. I have tried the AnomalyDetection package, however, it doesn't seem to work for my case. My data has no timestamp column. The only time related variable is in terms of hours of operation. $\endgroup$ – Katherine Nov 13 '17 at 22:14
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You could model the process as a Weibull distribution which is common for survival analysis and reliability engineering. There has been work on monitoring the "health" of systems using it, examples are here and here.

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I would try using Google's CausalImpact package. Your use-case isn't causal inference exactly, but CausalImpact relies on bayesian structural time series models (using the bsts package) and has some good defaults that keep you from needing to dive into bsts immediately.

Basically, you fit a model to the first part of your data, then forecast the rest. You see where your model deviates from the forecast. Using bayesian models means you can get error bounds - so you can have a degree of confidence in the deviation. In your case, you'd set your 'intervention' point to wherever timestamp you want to separate your modeling data from your forecasting data. Then compare the forecast to the actual data (look up 'nowcasting').

Here is a tutorial to get you started, and here's an introductory video.

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