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.

翻译：信号质量评估（SQA）是监测数据采集系统可靠性的必要环节，尤其在基于人工智能的预测性维护（PMx）应用场景中。SQA 对于识别数据采集硬件与软件的“静默故障”至关重要——此类故障若未被察觉，将误导数据使用者，导致决策错误，甚至引发意外或灾难性后果。我们开发了一套用于时间序列数据信号质量指标（SQIs）分析的开源软件工具。该工具整合了多种 SQIs，并通过公开基准数据验证了其在信号质量评估中的有效性。同时，我们研究了旨在提升已劣化信号质量的时间序列数据去噪替代方法，并在相关真实数据集上进行了经验性评估。据我们所知，我们的软件工具包是首个提供广泛信号质量评估与提升技术的开源实现，这些技术均通过公开基准数据验证，便于结果复现。该框架具有良好的通用性，可轻松扩展至复杂系统中任意时间序列测量值的可靠性评估——尤其当下游分析重点关注波形形态模式与信号周期特征时。