In-depth Analysis:

• This study introduces a novel method for fault detection tailored specifically for synthetic data, demonstrating significant advancements in model drift detection and independence testing between random variables.
• Key Highlights:
  • Application of a mutual information-based scheme suitable for a wide range of system models.
  • Empirical validation with synthetic and real-world data.
  • Theoretical properties such as strong consistency and control of test significance levels.

Importance: This method provides a robust framework for fault detection without the need for prior faulty examples, indicating its potential for wide application in various practical settings. The methodology could lead to developments in automated systems for industries like aviation and heavy machinery, where preventive maintenance is crucial.