- Statistical Methods You Should Use (Specific to Your Dataset)
A. Descriptive Statistics (For Each Sensor Column)
Apply to these columns only:
Air temperature (K)
Process temperature (K)
Rotational speed (rpm)
Torque (Nm)
Tool wear (min)
Include:
Mean
Median
Standard deviation
Minimum
Maximum
Range
25th, 50th, 75th percentiles
Purpose: Understand how machines normally operate before failure occurs.
B. Temperature Difference Analysis
Your dataset specifically allows:
Temperature Difference = Process Temperature – Air Temperature
This is an important engineered statistical feature.
Questions it answers:
Does a larger temperature difference indicate heat-related failures?
At what difference threshold do failures start to appear?
C. Correlation Analysis (Sensor-to-Failure Relationships)
Compute Pearson correlation for:
Tool wear vs Target
Torque vs Target
RPM vs Target
Temperature difference vs Target
This shows which measurements contribute most to failure.
D. Group-Based Statistical Comparison
Use "groupby statistics" for these:
Groups:
Failure vs No Failure (Target)
Failure Types (Heat, Tool Wear, Overstrain, etc.)
Machine Type (L, M, H)
Compare their:
Average torque
Average RPM
Temperature difference
Tool wear levels
These comparisons reveal patterns specific to your dataset.
E. Outlier Detection (Distribution-Based)
Check outliers for:
Torque
RPM
Tool wear
Process temperature
Your dataset typically shows:
Torque has extreme peaks
Tool wear increases linearly but failure cases cluster at high values
This is essential for understanding machine behavior.
F. Hypothesis Testing (Specifically for Your Data)
- T-test
Compare two groups:
Mean torque in Failure vs Non-Failure rows
Mean tool wear in Failure vs Non-Failure rows
- ANOVA
Compare:
Mean tool wear across different failure types
Mean torque across machine types L/M/H
- Chi-square Test
For categorical patterns:
Is failure type dependent on machine type?
These tests directly validate the relationships in your dataset.
- Statistical Questions You Should Answer (SPECIFIC to Your Dataset)
Below is the exact question set for your predictive_maintenance.csv file.
A. Sensor Behavior Questions
What is the average air temperature during machine operation?
What is the typical process temperature, and how much higher is it than air temperature?
What is the average rotational speed (rpm) of the machines?
How much does torque fluctuate across production cycles?
What is the distribution of tool wear values?
B. Failure-Specific Statistical Questions
Do failed machine cycles show higher torque than normal cycles?
How different is tool wear in failure rows compared to non-failure rows?
Do failures occur at higher RPM ranges?
Does the temperature difference increase significantly before heat-related failure?
C. Machine Type (L/M/H) Statistical Questions
Do machine types (L, M, H) operate at different average torque levels?
Do some machine types show higher average tool wear?
Are certain machine types more prone to failure?
D. Failure Type Statistical Questions
Using the "Failure Type" column:
What is the average torque for each failure type (Heat, Tool Wear, Overstrain)?
What are the temperature patterns associated with heat-related failures?
Does overstrain failure occur at high RPM levels?
How much tool wear occurs before tool-wear failure?
E. Outlier and Distribution Questions
Are torque outliers linked to mechanical failure?
Do extremely high process temperatures precede heat dissipation failures?
Is tool wear heavily right-skewed, indicating gradual degradation?
F. Hypothesis Testing Questions
Is the mean tool wear significantly higher in failure cases compared to normal cycles?
Do different failure types have statistically different torque levels?
Is failure occurrence independent of machine type, or related?
A. Descriptive Statistics (For Each Sensor Column)
Apply to these columns only:
Air temperature (K)
Process temperature (K)
Rotational speed (rpm)
Torque (Nm)
Tool wear (min)
Include:
Mean
Median
Standard deviation
Minimum
Maximum
Range
25th, 50th, 75th percentiles
Purpose: Understand how machines normally operate before failure occurs.
B. Temperature Difference Analysis
Your dataset specifically allows:
Temperature Difference = Process Temperature – Air Temperature
This is an important engineered statistical feature.
Questions it answers:
Does a larger temperature difference indicate heat-related failures?
At what difference threshold do failures start to appear?
C. Correlation Analysis (Sensor-to-Failure Relationships)
Compute Pearson correlation for:
Tool wear vs Target
Torque vs Target
RPM vs Target
Temperature difference vs Target
This shows which measurements contribute most to failure.
D. Group-Based Statistical Comparison
Use "groupby statistics" for these:
Groups:
Failure vs No Failure (Target)
Failure Types (Heat, Tool Wear, Overstrain, etc.)
Machine Type (L, M, H)
Compare their:
Average torque
Average RPM
Temperature difference
Tool wear levels
These comparisons reveal patterns specific to your dataset.
E. Outlier Detection (Distribution-Based)
Check outliers for:
Torque
RPM
Tool wear
Process temperature
Your dataset typically shows:
Torque has extreme peaks
Tool wear increases linearly but failure cases cluster at high values
This is essential for understanding machine behavior.
F. Hypothesis Testing (Specifically for Your Data)
Compare two groups:
Mean torque in Failure vs Non-Failure rows
Mean tool wear in Failure vs Non-Failure rows
Compare:
Mean tool wear across different failure types
Mean torque across machine types L/M/H
For categorical patterns:
Is failure type dependent on machine type?
These tests directly validate the relationships in your dataset.
Below is the exact question set for your predictive_maintenance.csv file.
A. Sensor Behavior Questions
What is the average air temperature during machine operation?
What is the typical process temperature, and how much higher is it than air temperature?
What is the average rotational speed (rpm) of the machines?
How much does torque fluctuate across production cycles?
What is the distribution of tool wear values?
B. Failure-Specific Statistical Questions
Do failed machine cycles show higher torque than normal cycles?
How different is tool wear in failure rows compared to non-failure rows?
Do failures occur at higher RPM ranges?
Does the temperature difference increase significantly before heat-related failure?
C. Machine Type (L/M/H) Statistical Questions
Do machine types (L, M, H) operate at different average torque levels?
Do some machine types show higher average tool wear?
Are certain machine types more prone to failure?
D. Failure Type Statistical Questions
Using the "Failure Type" column:
What is the average torque for each failure type (Heat, Tool Wear, Overstrain)?
What are the temperature patterns associated with heat-related failures?
Does overstrain failure occur at high RPM levels?
How much tool wear occurs before tool-wear failure?
E. Outlier and Distribution Questions
Are torque outliers linked to mechanical failure?
Do extremely high process temperatures precede heat dissipation failures?
Is tool wear heavily right-skewed, indicating gradual degradation?
F. Hypothesis Testing Questions
Is the mean tool wear significantly higher in failure cases compared to normal cycles?
Do different failure types have statistically different torque levels?
Is failure occurrence independent of machine type, or related?