EDA Report: Sensor Readings Analysis

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Step 4: Key Findings Summary

1. Dataset Overview

Dimensions & Quality:

• Size: 6,000 sensor readings × 4 variables
• Memory: 187.63 KB (lightweight, efficient storage)
• Data Quality: Excellent - zero missing values (100% complete)
• Duplicates: None detected
• Data Types: All numeric (float64) - ready for quantitative analysis

Columns:

• temperature - Sensor temperature readings
• humidity - Environmental humidity measurements
• vibration - Mechanical vibration levels
• error_count - Equipment error frequency

2. Key Patterns & Insights

Temperature Characteristics

• Mean: 72.0°F (range: 62.3°F to 83.8°F)
• Distribution: Nearly perfect normal distribution (skewness: 0.01)
• Stability: Low variance (σ = 3.0°F) indicates stable operating conditions
• Outliers: 52 readings (0.87%) outside normal range - minimal concern

Humidity Characteristics

• Mean: 44.9% (range: 13.6% to 73.2%)
• Distribution: Symmetric and well-behaved
• Variability: Moderate spread (σ = 8.0%) suggests environmental fluctuations
• Outliers: 54 readings (0.90%) at extreme humidity levels

Vibration Characteristics

• Mean: 0.041 units (median: 0.029)
• Distribution: Right-skewed (skewness: 2.04) - most readings are low, with occasional spikes
• Heavy-tailed: Kurtosis = 6.20 indicates more extreme values than normal
• Outliers: 305 readings (5.08%) exceed normal vibration threshold of 0.12 units
• Critical: High vibration readings may indicate mechanical issues

Error Count Analysis

• Error Rate: 14.23% of readings show at least one error
• Distribution: Highly right-skewed (skewness: 3.13) - most sensors error-free
• Pattern: 854 records (14.23%) flagged as outliers by IQR method
• Severity: Errors range from 1-10, with 85.77% of readings error-free
• Breakdown:
  • 0 errors: 5,146 records (85.77%)
  • 1-3 errors: 523 records (8.72%)
  • 4-10 errors: 331 records (5.52%)

Correlation Analysis

• Finding: No strong correlations detected (all |r| < 0.5)
• Independence: Variables operate independently - no multicollinearity concerns
• Implication: Errors are NOT driven by temperature, humidity, or vibration
• Insight: Errors may be random or caused by external factors not captured in this dataset

3. Data Quality Issues

Strengths

• Perfect completeness (0% missing data)
• No duplicate records
• Consistent data types
• Appropriate value ranges for all sensors

Concerns

1. Vibration Spikes (Priority: HIGH)

• 305 outliers (5.08%) with elevated vibration levels
• Right-skewed distribution suggests occasional mechanical stress
• Values exceed 0.12 units threshold (normal upper bound)
• May indicate equipment wear or environmental interference

2. Error Count Distribution (Priority: MEDIUM)

• 14.23% error rate may be acceptable depending on industry standards
• Some sensors show up to 10 errors (extreme outliers)
• No correlation with other variables complicates root cause analysis
• Need to investigate: Are errors random or sensor-specific?

3. Temperature/Humidity Outliers (Priority: LOW)

• ~1% outliers each - minimal impact
• May represent calibration drift or environmental extremes
• Within reasonable bounds for most applications

4. Missing Context

• No timestamps (can’t analyze temporal patterns)
• No sensor IDs (can’t identify problematic units)
• No location data (can’t detect spatial patterns)
• No metadata about normal operating ranges

4. Recommendations

Immediate Actions

1. Investigate Vibration Outliers

   • Review the 305 high-vibration readings (>0.12 units)
   • Check for mechanical issues: loose mounting, bearing wear, imbalance
   • Consider adding vibration threshold alerts to monitoring system
   • May need log transformation for modeling due to right-skew

2. Analyze Error Patterns

   • Add timestamp and sensor ID columns to identify if errors are:
     • Random across all sensors (acceptable)
     • Concentrated in specific units (requires maintenance)
     • Temporal (time-of-day, day-of-week patterns)
   • Investigate the 331 sensors with 4+ errors (top 5.5%)

3. Address Distribution Skewness

   • Vibration: Apply log transformation: log(vibration + 0.001) for modeling
   • Error Count: Consider Poisson or negative binomial models rather than linear regression
   • Both variables violate normality assumptions for parametric tests

Data Collection Improvements

4. Enhance Dataset for Future Analysis

   • Add sensor_id column to track individual unit performance
   • Add timestamp for time-series analysis and trend detection
   • Add location or zone for spatial pattern analysis
   • Include sensor_age or maintenance_date to assess wear patterns
   • Record operating_mode or load_level for context

5. Set Monitoring Thresholds

   • Temperature: Alert if outside [64°F, 80°F] (based on outlier bounds)
   • Humidity: Alert if outside [23%, 67%]
   • Vibration: Alert if >0.12 units (95th percentile threshold)
   • Error count: Alert if ≥4 errors per reading (top 5%)

Modeling Considerations

6. If Building Predictive Models:

   • Variables are independent (low correlations) - good for feature selection
   • Use tree-based models (Random Forest, XGBoost) for skewed distributions
   • Consider anomaly detection (Isolation Forest) for outlier identification
   • Error prediction: Use classification (error/no error) rather than regression
   • Feature engineering: Create high_vibration flag, error_rate_rolling_avg

7. Statistical Testing:

   • Avoid parametric tests (t-tests, ANOVA) due to non-normality
   • Use non-parametric alternatives: Mann-Whitney U, Kruskal-Wallis
   • For error count analysis: Chi-square test for independence
   • Bootstrap methods for confidence intervals on skewed distributions

Summary

Your sensor monitoring system generates high-quality data with excellent completeness. The 6,000 readings reveal stable temperature/humidity conditions but concerning vibration spikes (5% of readings) and a 14% error rate. The lack of correlation between variables suggests errors are driven by unmeasured factors. Priority actions: investigate high vibration readings, add sensor IDs and timestamps for deeper analysis, and consider log transformations for skewed variables before modeling.

Report file: /workspace/webserver/lib/smartvox/skill_hub/sensor_analysis.html

The HTML report contains all visualizations (correlation heatmap, distribution plots with KDE overlays, missing data charts) and detailed statistical tables for deeper exploration.