Statistical models are often used in industrial and manufacturing plants to monitor equipment operations and asset performance, predicting possible behavior patterns to anticipate failures and schedule precautionary maintenance. Although effective, this approach might not be enough in the Industry 4.0 scenario, as a close-to-zero fault tolerance is now required to meet new productivity and efficiency standards.

Innovative machine learning techniques are nowadays available to improve asset management performance by deploying the most precise issue detection and failure recognition, alerting owners well before the problem gets real. The idea behind it is to have more agile monitoring systems, correlating events and data coming from different sources to have a comprehensive view of plant assets and equipment.

According to independent experts, data collection and validation influences up to 80% of the success of asset monitoring processes, since relying on accurate, up-to-date and normalized information is essential for any analysis. Machine learning allows businesses to blend condition-based data with work history, that means integrating insights from past failures and related resolution to get a wider view over asset lifecycle and expected behavior.

The real news of asset performance management based on machine learning is the possibility to focus on the root cause of issues and problems. Maintenance activities are sometimes directed to the immediate origin of a certain failure, but cannot climb back to the disservice or malfunctioning that causes the damage. Machine learning brings predictive maintenance to the next level, identifying the root cause of a problem to facilitate decisions on where, when and how to take action in order to minimize risks and impacts on business processes.

An effective asset management performance is absolutely critical in Industry 4.0 organizations, and operational integrity can be safeguarded by applying machine learning models which allow to directly and early address the root cause of possible issues, solving it before provoking unplanned and onerous downtime.

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Author: Sabis Chu, IT Technology Evangelist at KRIU