# Maintenance Performance Determinants

## 1. Summary

The analysis of maintenance performance across various plants reveals several environmental and equipment factors significantly impacting maintenance needs. Notably, high humidity levels increase maintenance issues by 28%, suggesting that moisture leads to equipment wear and inefficiency, particularly in sensitive machinery like gas compressors. Dust levels also play a role; as they rise, the risk of maintenance needs slightly increases, likely due to wear and potential heating issues. Conversely, optimal temperature conditions correlate with reduced maintenance needs, emphasizing the importance of maintaining equipment at suitable operational levels. Specific equipment types, particularly older and less maintained units, exhibit up to a 62% increase in maintenance demands, underscoring the effects of wear, outdated parts, and potential failure points in aging machinery. This analysis underscores the need for targeted strategies to manage environmental conditions and system reliability in maintenance planning.

## 2. Key Findings

- Humidity Levels: High humidity increases maintenance risks by 28%, leading to operational inefficiencies and equipment failures.
- Dust Accumulation: Higher dust levels correlate with increased maintenance needs; they cause wear and cooling system blockages.
- Temperature Conditions: Improved temperature conditions lower maintenance requirements; optimal flow rates in compressors help prevent failures.
- Equipment Age: Older equipment, specifically units like EQ029 and grinders, has up to a 62% higher likelihood of experiencing issues due to worn parts and outdated technology.
- Type of Equipment: Variability among equipment types shows that machines like robot arms experience fewer maintenance issues, whereas grinders exhibit higher risks.

## 3. Method

The analysis utilizes a survival model, specifically Cox's proportional hazard model, to evaluate how different covariates affect maintenance performance over time. This model examines the relationship between several factors—such as humidity, dust, temperature, and equipment type—and maintenance outcomes. It estimates the risk of maintenance issues based on the presence of these covariates, providing risk multipliers to indicate how much more likely maintenance issues are based on specific conditions. While this model is effective in identifying significant relationships, it's important to note that it does not provide a full causal framework, and external factors beyond the analyzed covariates may also influence maintenance challenges. Furthermore, the model's conclusions are limited to the data available, and outcomes could differ under varying operational conditions.