A Novel Deep Clustering Method and Indicator for Time Series Soft Partitioning

Alexandre Eid, Guy Clerc, Badr Mansouri and Stella Roux
Additional contact information
Alexandre Eid: University Lyon, Université Claude Bernard Lyon 1, INSA Lyon, École Centrale de Lyon CNRS, Ampère, UMR5005, 69622 Villeurbanne, France
Guy Clerc: University Lyon, Université Claude Bernard Lyon 1, INSA Lyon, École Centrale de Lyon CNRS, Ampère, UMR5005, 69622 Villeurbanne, France
Badr Mansouri: Safran Electronics & Defense, 91344 Massy, France
Stella Roux: Grenoble INP—Ensimag, UGA, 38400 Saint-Martin-d’Hères, France

Energies, 2021, vol. 14, issue 17, 1-19

Abstract: The aerospace industry develops prognosis and health management algorithms to ensure better safety on board, particularly for in-flight controls where jamming is dreaded. For that, vibration signals are monitored to predict future defect occurrences. However, time series are not labeled according to severity level, and the user can only assess the system health from the data mining procedure. To that extent, a clustering algorithm using a deep neural network core is developed. Time series are encoded into pictures to be fed into an artificially trained neural network: U-NET. From the segmented output, one-dimensional information on cluster frontiers is extracted and filtered without any parameter selection. Then, a kernel density estimation finally transforms the signal into an empirical density. Ultimately, a Gaussian mixture model extracts the latter independent components. The method empowered us to reveal different degrees of severity faults in the studied data, with their respective likelihoods, without prior knowledge. It was then compared to state-of-the-art machine learning algorithms. However, internal clustering results evaluation for time series is an open question. As the state-of-the-art indexes were not producing relevant results, a new indicator was built to fulfill this task. We applied the whole method to an actuator consisting of an induction machine linked to a ball screw. This study lays the groundwork for future training of diagnosis and prognosis structures in the health management framework.

Keywords: semantic segmentation; time series; clustering; deep learning; kernel density estimation; electromechanical actuator; data labeling; prognosis and health management; aeronautics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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