Solution Challenge

Client was looking for a solution to predict the health of equipment and prevent equipment failure.

Summary

This use case involves leveraging machine learning and predictive analytics to anticipate potential equipment failures in advance. By analyzing various sensor data, operational logs, and maintenance records, the system can identify patterns and predict when a specific equipment component is likely to fail, enabling proactive maintenance actions and reducing unplanned downtime.

Preconditions

The following preconditions had to be met.

• Historical data on equipment performance, sensor readings, maintenance logs, and failure records are available.
• Integration with industrial control systems, Internet of Things (IoT) sensors, and maintenance management systems is established.

Solution Flow

• Hanelytics ingested historical data on equipment performance, sensor readings (e.g., vibration, temperature, pressure), maintenance logs, and failure records from various sources (IoT sensors, control systems, maintenance systems). It performs data preprocessing, cleaning, and feature engineering to prepare the data for analysis.
• Using advanced machine learning algorithms (e.g., anomaly detection, time series forecasting, classification), Hanelytics analyzes the data to identify patterns and correlations between sensor readings, operational parameters, and equipment failures. Hanelytics builds predictive models to forecast the remaining useful life (RUL) of critical equipment components and the probability of failure within a given time frame.
• When the predicted RUL or probability of failure exceeds predefined thresholds, the system would generate alerts and recommendations for proactive maintenance actions (e.g., component replacement, repair, or preventive maintenance). The maintenance manager and equipment operator review the alerts and recommendations, prioritize maintenance tasks, and schedule necessary actions to prevent equipment failures.
• Hanelytics continuously monitors equipment performance and updates the predictive models as new data becomes available, enabling iterative improvements in failure prediction accuracy.

Alternative Flows

• If the data quality is poor or incomplete, the system may request additional data or manual intervention for data cleaning and preprocessing.
• If the predicted failures or maintenance recommendations deviate significantly from historical patterns or expert knowledge, the system may trigger additional alerts and provide explanations for further analysis.

Benefits

• Potential equipment failures are anticipated in advance, enabling proactive maintenance actions.
• Unplanned downtime and production disruptions are minimized, leading to increased operational efficiency and productivity.
• Continuous monitoring and adjustment of predictive models based on updated data and maintenance records.
• Extended equipment lifespan and increased asset utilization.
• Improved operational efficiency and productivity
• Enhanced safety and regulatory compliance through proactive maintenance.

By leveraging Hanelytics, organizations can transition from reactive to proactive maintenance strategies, minimizing equipment failures, reducing downtime costs, and optimizing overall asset performance.