Sequential Harmonic Component Tracking for Underdetermined Blind Source Separation in a Multitarget Tracking Framework

Smart factories are composed of heterogeneous cyber-physical systems. In light of their complexity and the lack of transparency in their design, monitoring the health of these machines in real time is made possible by the use of nonintrusive sensors. Such sensors produce mixed signals capturing component-specific signatures. Retrieving the activation statuses of the components (over the different operating modes of a machine) is essential for estimating their associated performance indicators. This is a special case of underdetermined blind source separation (UBSS), yet a sensor fusion perspective is adopted in this chapter. A harmonic component detector produces observations in the time-frequency (TF) domain, inherently entailing noise-induced false alarms. The main contribution of this chapter consists of a clutter-resilient multiharmonic component tracking algorithm, based on the sequential Monte Carlo probability hypothesis density (SMC-PHD) filter. Additionally, this chapter presents a track association algorithm adapting the results obtained in the multitarget tracking framework for unsupervised multilabel classification. The combination of the two algorithms mitigates typical difficulties encountered in traditional UBSS problems, such as nonstationary and partially coupled mode decomposition. The performance of the proposed technique is assessed on synthetic data.