Using Data Science to Enhance Reliability: Four Real-World Applications

The rise in computational power over the last decade has begged the question of if and to what extent quantitative methods such as data science have in improving reliability programs. While data science has the power to revolutionize the reliability industry, it will only be able to do so with strong guidance and review from subject matter experts (SMEs).

The ability to make better decisions by leveraging data continues to be a theme across the industry and will help decision-makers make more informed strategic decisions at a faster pace. This article highlights the efficacy of a combined SME and data science approach by showing four example applications:

1. Using equipment data and associated corrosion rates across multiple reformer units to show how predictive models using data science compare to traditional industry templates and expertise-driven models.
2. Leveraging Bayesian statistics to introduce uncertainty into remaining life calculations and probability of failure, empowering the expert to define variables better to identify and reduce uncertainty, improve equipment remaining life estimations, and reduce overall risk.
3. Leveraging data science to quantify the confidence of damage detection, including driving benefit to cost for taking readings on or omitting particular condition monitoring locations (CMLs).
4. Leveraging natural language processing on CMMS and IDMS data to identify anomalies for equipment that should have been flagged for positive material identification but were not.