Predict industrial equipment failure with time windows and transfer learning

Sensors, while more widely implemented in industry, have generated a large number of high-dimension unlabeled time series data during the process of the complicated producing. If putting these data to use, we can predict and preclude malfunctions of specific industrial facilities so that there will be less pecuniary lost. In this paper, we propose a malfunction predicting algorithm based on transfer learning. We use time windows due to the periodicity of industrial data, targeting at transfer learning among pieces of equipment with different sampling rate to address the problem of learning from unlabeled data. Rationale proofs and experiments indicate the efficacy of the algorithm and the prediction accuracy reaches 97%.