Statistical analysis of a dataset - Operating units
Budget: $250 – $750 USD
I have a dataset that contains "Warnings" from equipment control system. For example, when a unit gets too hot, a warning will be sent. Each warning has a code. Each warning is classified as 700 or 800.
This equipment runs all year long but is used much more frequently in the summer months.
The objective of this analysis is to perform a statistical analysis on Unit, Code, Date, and Wave to determine if information/decision making can be performed based on the warnings.
I am looking for answers to the questions below plus any other analyses you may believe is useful.
Interaction between Codes for the same unit. Seasonality is helpful. Max/Min/Ave/Etc are useful. Plus time between warnings for each unit is helpful.
1. Highest frequency code by Unit number
2. Seasonality? Which months see the highest of what type of code? What units have this issue? What units have no seasonality?
3. Common codes among Unit numbers with first digit same (e.g., OB1, OB2, …, OB5….are there codes that are common among these same unit numbers?)
4. Were there warnings that appeared and then stopped? For example unit OB1 had codes 1218 and 1220 begin in March 2021 and end in July 2021, which would indicate a performance issue began in March and was fixed in July. --OR, that the unit was damaged in March and only corrected in July. This analysis will be used for "scenarios" with mechanics trying to determine what happened.
5. Which Units and Codes are increasing and decreasing over time?
6. What are averages and standard deviations I can use to predict issues in future?
7. Is there anything to learn from day of week?
8. How much time between warnings for each engine? Max/Min/Mean/Etc
9. There are 20 units in this data set. I'd like an analysis of each unit
10. There are 4 unit sets in this data set. I'd like an analysis on each unit set. A unit set is OB1 - OB5, for example.
11. Is there a way to determine cause and effect? Let's say code XXXX occurs, then the next code is YYYY. Can we determine if YYYY is caused by XXXX?
This equipment runs all year long but is used much more frequently in the summer months.
The objective of this analysis is to perform a statistical analysis on Unit, Code, Date, and Wave to determine if information/decision making can be performed based on the warnings.
I am looking for answers to the questions below plus any other analyses you may believe is useful.
Interaction between Codes for the same unit. Seasonality is helpful. Max/Min/Ave/Etc are useful. Plus time between warnings for each unit is helpful.
1. Highest frequency code by Unit number
2. Seasonality? Which months see the highest of what type of code? What units have this issue? What units have no seasonality?
3. Common codes among Unit numbers with first digit same (e.g., OB1, OB2, …, OB5….are there codes that are common among these same unit numbers?)
4. Were there warnings that appeared and then stopped? For example unit OB1 had codes 1218 and 1220 begin in March 2021 and end in July 2021, which would indicate a performance issue began in March and was fixed in July. --OR, that the unit was damaged in March and only corrected in July. This analysis will be used for "scenarios" with mechanics trying to determine what happened.
5. Which Units and Codes are increasing and decreasing over time?
6. What are averages and standard deviations I can use to predict issues in future?
7. Is there anything to learn from day of week?
8. How much time between warnings for each engine? Max/Min/Mean/Etc
9. There are 20 units in this data set. I'd like an analysis of each unit
10. There are 4 unit sets in this data set. I'd like an analysis on each unit set. A unit set is OB1 - OB5, for example.
11. Is there a way to determine cause and effect? Let's say code XXXX occurs, then the next code is YYYY. Can we determine if YYYY is caused by XXXX?