# Cox Proportional Hazards Model Results

The Cox Proportional Hazards model is a statistical technique used to analyze the effect of several variables on the time until an event occurs, such as equipment failure in this case. The model assumes that the hazard ratio between two individuals is constant over time.

## Key Insights

The month of July (month_7) has the strongest protective effect against equipment failure, with a coefficient of -1.15. This means that equipment starting operation in July is less likely to fail compared to other months.

Higher maximum pressure is associated with an increased risk of equipment failure (coefficient: 0.07). Focusing on reducing maximum pressure could improve equipment reliability.

Certain equipment IDs, such as C9478, B1775, and D3468, have notably higher risk of failure compared to others. Prioritizing maintenance and monitoring of these specific equipment could yield better performance.

## Model Interpretation and Limitations

The coefficients in the model represent the log hazard ratios. A positive coefficient indicates an increased risk of failure, while a negative coefficient suggests a protective effect. The model's concordance index of 0.59 indicates moderate predictive power, suggesting there is room for improvement by including additional relevant variables.

It is important to note that the Cox model assumes proportional hazards over time. If this assumption is violated, the model may not provide accurate insights. Additionally, the model does not account for potential interactions between variables, which could be explored further.

## Recommendations

Based on the model results, consider the following actions to improve maintenance performance:

- Schedule more intensive maintenance for equipment starting operation in months other than July.
- Investigate ways to reduce maximum pressure, as it is associated with higher failure risk.
- Focus maintenance efforts on high-risk equipment, such as those with IDs C9478, B1775, and D3468.
- Collect additional data on factors that could influence equipment failure to improve the model's predictive power.