Advanced Compliance Risk Assessment Using Big Data from Leverage Fleet Intelligence

In-Use compliance regulations globally mandate that machines meet emission standards in the field, beyond dyno certification. For engine manufacturers, understanding compliance risks early is crucial for technology selection, calibration strategies, and validation routines. This study focuses on developing analytical and statistical methods for compliance risk assessment using Leverage Fleet Intelligence (LFI) data, which includes high-frequency telematics data from over 500K machines, reporting 1300 measures at 1Hz frequency. Traditional analytical methods are inadequate for handling such big data, necessitating advanced methods. We developed data pipelines to query measures from the Enterprise Data Lake (A Structured Data storage system), address big data challenges, and ensure data quality. Regulatory requirements were translated into software logic and applied to pre-processed data for compliance assessment. The resulting reports provide actionable insights on NOx sensor activity, engine warmup operations, high-risk drive cycles, and load profiles across different operation regimes. This approach significantly reduces the reliance on costly and labor-intensive physical testing with Portable Emissions Measurement Systems (PEMS) by integrating advanced analytical methods into the workflow. By leveraging high-frequency telematics data, this method enables engineers to identify failed machines in the field more efficiently. It also provides valuable insights and reasoning behind these failures, facilitating quicker and more informed decision-making. This not only enhances compliance monitoring but also optimizes resource allocation and reduces overall regulatory risks. In summary, the developed methods enable effective compliance monitoring, reduce regulatory risks, and help optimize calibration strategies by understanding customer usage patterns. These methods are scalable for various emission regulations.