CIVEMSA 2022

Machine learning methods have shown a high impact on machine health prognostics solutions. However, most studies stop after building a model on a server or pc, without deploying it to embedded systems close to the machinery. Bringing machine learning models to small embedded systems with a small energy budget does require adapted models and raw time series data processing to handle resource constraints while maintaining high model performance. Feature selection plays a crucial role in this process. One of the most common methods for machinery data feature is its spectral information, that are extracted via digital filters. Calculating spectral features on microcontrollers has a great impact on the computational requirements of the overall estimations. In this paper, we analyze mel-spectrogram and infinite impulse response (IIR) based spectral feature extractors regarding their estimation performances and their computational requirements. The goal is to evaluate possible trade-offs when selecting one feature extractor over the other. To achieve this, we study the cost of both methods theoretically and via run-time measurements after analyzing the feature design space to ensure good model performance. Our results show that by selecting an appropriate filter to the problem, its feature space dimensionality and with it the computational load can be reduced.