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3 Ways to Enrich a Failure Prediction Model With Process Domain Knowledge

Machine learning models may be the realm of a data scientist, but the insight of a process expert is crucial to their success

The ability to determine when a failure is going to occur has emerged as a radical shift from reacting when it does. In the process manufacturing industry, predicting failures before they happen helps engineers improve performance and schedule timely maintenance. Local process experts play a critical role in creating failure prediction models that are reliable.

Failure prediction is a machine learning technique that allows operational experts to take proactive measures and mitigate potential risks. By analyzing historical time-series and contextual data for patterns and other indicators, failure prediction models let plant personnel know when there is about to be a problem. Their uses include detecting anomalies from established monitors or even for forecasting events to schedule preventive maintenance with prescriptive instructions.

Collaborating for Failure Prediction Models

While the development of a failure prediction model predominantly falls within the realm of data scientists and central data teams, operational experts have an intricate understanding of the plant’s manufacturing processes, equipment, operating conditions, and failure modes. This domain knowledge is instrumental in identifying the relevant data sources, features, and variables that affect equipment performance and failure events.

When collaborating on a data science project, the engineer’s role then is to ensure that the data being used will achieve the desired outcome. As such, there are three areas where the expertise of the operational expert is necessary for a reliable failure prediction model.

  • Data Collection

    By collecting unprocessed, pre-processed, and processed data, engineers ensure that data scientists have access to comprehensive and accurate information for modeling. Operational experts assist in integrating this data into a consistent and standardized format. They provide missing values, select criteria for feature importance, and provide captured patterns or relationships that put failure events in context.

  • Model Interpretability and Validation

    Engineers know whether the predicted failure events align with practical considerations and operational constraints. This validation step ensures that the models accurately capture the nuances of the manufacturing environment and produce reliable predictions. Engineers also can collaborate with data scientists to visualize and explore the data. Advanced industrial analytics provides the opportunity to identify correlations, trends, and anomalies. These visualizations help both engineers and data scientists gain a deeper understanding of the patterns and relationships that influence failure events. Engineers then provide valuable insights into potential indicators or triggers that are not apparent from the data alone.

  • Continuous Improvement and Model Optimization

    Operational experts are well-versed in continuous improvement methodologies. They have a strong grasp of key performance indicators (KPIs) and process optimization techniques. This experience lends itself well to optimizing the failure prediction models. They are in a position to provide feedback on real-world model performance, suggest additional features or variables to consider, and collaborate with data scientists to fine-tune the models. Striking the right balance between accuracy, interpretability, and practicality in the model design ultimately leads to more reliable and actionable predictions.

Data Accessibility and Scalability

In the era of big data, engineers play a crucial role in managing it and ensuring solutions are scalable. As the volume, variety, and velocity of data continue to increase, operational experts will work with information technology and operational technology teams to design and implement data infrastructure that can efficiently handle the growing demands. This includes industrial storage solutions to process large datasets that are required for failure prediction.

Cloud solutions also are replacing the on-premises historian and legacy third-party systems. These solutions offer greater access to data from anywhere but also allow companies to collect data from previously disconnected sites. The result is even more data to consider for possible failure points. As companies move their operational data to the cloud, the insights from engineers work to ensure these modern historians and data lakes provide data integrity, adhere to security standards, and meet compliance requirements.

Conclusion

In the process manufacturing industry, failure prediction holds immense potential for enhancing operational efficiency and minimizing downtime. As the manufacturing landscape continues to evolve, local experts and central data teams must forge strong partnerships and leverage each other’s strengths to drive innovation and success. By combining their expertise, engineers and data scientists can unlock the true potential of using machine learning techniques to improve overall production.

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