Predictive Maintenance - Exploring strategies for Remaining Useful Life (RUL) prediction

In the current technological context where signals can assist the functionality of the engines in operation and the correct functionality can be monitored. Therefore, patterns of utilization can be identified for predictive and preventive maintenance of such engines, thus predicting the Remaining Useful Life (RUL). For this reason, developing strategies to extract knowledge from recorded signals for preventing flaws is necessary and it opens an entire research direction. This paper presents the development of a generic strategy for exploring, analyzing and predicting the value of RUL and identifying techniques for specific data modeling. We defined and experimented a deep learning model, with a LSTM (Long Short-Term Memory) architecture. The identified strategies are tested and validated on a synthetic C-MAPSS data set which contains information from aircraft engines monitored and collected during several operating cycles. We defined 7 hypotheses, tested them and confirmed or unconfirmed each of them. We defined and presented: 6 architectural models, 3 sampling strategies on the original data set, presenting 18 representative experiments.