Since unexpected machine failures are huge losses for users, maintenance activities are essential. If the failures can be predicted in advance using a supervised learning, the machines can be maintained before they break down and some failures can be prevented. However, although a large number of failure data are required to predict failures using a supervised learning, failures rarely occur in the actual field. In this study, we propose to detect the failure of a hydraulic excavator using an autoencoder, which is an unsupervised learning. By using the autoencoder to model normal state data, the failure can be predicted in advance. This paper shows the results of evaluating failure predictions using the LSTM (Long Short-Term Memory) autoencoder model for actual failure of hydraulic excavators.