Abstract
Aircraft hydraulic systems can function very competently in challenging in-flight circumstances and are used throughout the functioning of critical flight components. Health monitoring of the hydraulic system parameters is the need of the hour. The objective of this paper is to investigate and propose a noise-invariant machine learning model for health monitoring of the hydraulic system using multivariate sensor parameter time series data. Identifying the health of the hydraulic system from sampled sensor parameter values is modeled as a multivariate time series classification problem. Our contribution is twofold: 1. identification of highly significant statistical, spectral and temporal features specific to the sensor parameter value. These feature vectors are mapped to a low-dimensional feature space using a feature selector package using Benjamini Hochberg process. 2. Train and build a machine learning model highly robust and invariant to various levels of random, squeal and impulse noise. In this paper, we evaluate and examine the models performance on the noise-free original and noise-induced multivariate time series data. Experimental results show that identified and selected statistical features for each sensor parameter are more robust to low, medium and high noise levels compared to spectral features and temporal features.
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Sirisha, B., Goli, S.G., Balram, J., Manoj, A.V.S.S., Praneeth, R., Sandhya, B. (2022). A Featurized Learning Approach for Health Monitoring in Hydraulic Systems Using Multivariate Time Series Data. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Intelligent Computing and Applications, Volume 2. Smart Innovation, Systems and Technologies, vol 283. Springer, Singapore. https://doi.org/10.1007/978-981-16-9705-0_7
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DOI: https://doi.org/10.1007/978-981-16-9705-0_7
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