unsupervised Time series anomaly detection

Question

I have 3D printer that working exactly 400 second for printing element X [0-400].

The printer produce 30 signals (features like VOLT,X,Y,Z,TEMP etc') in frequency of 50HZ (every sample 0.02 ms) ,for every print I monitor this features in log with time tag (0-400) .

The features are not stationary during the activation (changed ) ,the features also depends. The features value can be change from one printing to another (not exactly ), the duration of printing can change for example coloring mission can take more time in one print than other .

I want to create automate system that will detect if there anomaly (problem) during the printing process without human check .

I have more than 100 logs (printing logs of element of type X) that I sure the are good.

I am looking machine learning algorithm for unsupervised feature learning for time series that will give me the ability to classify if the printing is good or not, I think about LSTM or DBN (deep belief Net) but something say to me their another option that may I don't know .

Thanks. MAK

Answer 1

you need not to have a time series algoritm for anomaly detection-

First of all Identify- "WHAT IS ANOMLAY IN YOUR APPLICATION", there is no algorithm that will give u direct abnormality. they are focused on outlier detection.

1) If you can generate some data at abnormality, build a classification model.

2) Can you build a relation among variables present in data. Find a relation by NN or regression, any deviation from known relation is abnormality.

3) If outliers are abnormalities in your application. I can share some links like- https://machinelearningstories.blogspot.com/2018/07/anomaly-detection-anomaly-detection-by.html