Unexpected Equipment Failures Led to Unplanned Downtime
A large manufacturing company with multiple plants and production lines generated vast amounts of machine and sensor data during production. Despite collecting detailed information—including mold, material, and over 500 sensor readings per machine cycle—the company was not leveraging this data for advanced analytics. Without machine learning or data science models, it was difficult to predict machine downtime and perform predictive maintenance. As a result, unexpected equipment failures led to unplanned production stoppages and increased scrap, impacting operational efficiency and costs.
We Built Predictive Maintenance Pipelines
Python and Databricks were used to build a predictive maintenance pipeline. After extensive exploratory data analysis (EDA), a global dataset was created, aggregating data from multiple production lines. Each machine cycle was categorized by operational state, and dimensionality reduction (PCA) was applied to high-dimensional sensor groups. Feature engineering included rates of change, rolling statistics, interaction, and cyclical features. Multiple prediction horizons and multiclass labels were defined to capture different failure windows. Feature selection was performed using a Random Forest classifier and greedy search, and the final models were trained with hyperparameter tuning. The models achieved F1 scores between 0.70 and 0.85, with a precision of 0.90 and recall of 0.60.
Identifying Opportunities In The Data
A key insight was that downtime patterns were consistent across different production lines, enabling the use of global models tailored to each plant. This approach simplified deployment and maximized the value of existing sensor data, demonstrating that scalable predictive maintenance is achievable even in complex manufacturing environments.
Building A Predictive Maintenance Solutions
The predictive maintenance solution enabled maintenance teams to anticipate machine downtimes with high confidence. The F1 score (0.70–0.85) balanced precision (0.90) and recall (0.60), meaning that 90% of predicted downtimes were correct and 60% of actual downtimes were detected in advance. This allowed teams to act on reliable alerts, reducing unnecessary interventions and catching a significant portion of failures before they caused disruptions. Early detection also led to a significant reduction in scrap, as malfunctioning machines were identified and addressed sooner, preventing prolonged periods of faulty production.
Achieving AHigh Precision (0.90) And Recall (0.60) In Downtime Prediction
- Significant reduction in unplanned downtime and scrap generation.
- More efficient maintenance scheduling and resource allocation.
- Increased trust in data-driven decision-making on the plant floor.
- Established a scalable framework for future analytics initiatives.
The Models Were Deployed On Existing Systems
The models were deployed on Databricks and integrated with existing data systems. Maintenance teams and plant managers received clear alerts and dashboards showing which machines were at risk of downtime. Each plant used its own tailored model, and staff were trained to use the new tools as part of their regular maintenance routines. This allowed teams to act quickly on reliable predictions, reducing both unplanned downtime and scrap.
