  Signal quality assessment (SQA) is required for monitoring the reliability of
data acquisition systems, especially in AI-driven Predictive Maintenance (PMx)
application contexts. SQA is vital for addressing "silent failures" of data
acquisition hardware and software, which when unnoticed, misinform the users of
data, creating the risk for incorrect decisions with unintended or even
catastrophic consequences. We have developed an open-source software
implementation of signal quality indices (SQIs) for the analysis of time-series
data. We codify a range of SQIs, demonstrate them using established benchmark
data, and show that they can be effective for signal quality assessment. We
also study alternative approaches to denoising time-series data in an attempt
to improve the quality of the already degraded signal, and evaluate them
empirically on relevant real-world data. To our knowledge, our software toolkit
is the first to provide an open source implementation of a broad range of
signal quality assessment and improvement techniques validated on publicly
available benchmark data for ease of reproducibility. The generality of our
framework can be easily extended to assessing reliability of arbitrary
time-series measurements in complex systems, especially when morphological
patterns of the waveform shapes and signal periodicity are of key interest in
downstream analyses.
