HP INCFollow


We are building a failure prediction algorithm with sequences of event code data from devices

using a deep learning model called LSTM (Long Short‐Term Memory). LSTM models are widely

used to predict the sequence of words in word embedding technique. The principle idea of this

algorithm is very similar to next word prediction in your cell phone when you are sending

messages. The algorithm has the ability to predict the event codes leading up to the failure and

resulting time of the failure. The algorithm is fed a list of the last five event codes on the specific

device and can predict the next event codes leading up to the next failure by applying the LSTM

model recursively. The data used to train this algorithm are telemetry data which contains event

code data, time when the event happened and repair data, which is used to cut the sequence of

event code data. We treated one sequence leading to a failure (failure date) as one paragraph of

words. Employing this algorithm, we can accurately predict the next sequence of event codes

and the resulting time until the next failure.

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