Reasoning & Knowledge
EngineMT-QA: large-scale multi-task Time-Series QA over N-CMAPSS aero-engine sensor signals (33 channels). Items pair a time-series window with an NL question across Understanding, Perception, Reasoning and Decision-Making tasks. Built subset covers the closed multiple-choice Perception and Reasoning items.
Response matrix
Fit to width. Hover for subject & item; click a cell for details.
Scale: 1 = correct · 0 = incorrect
Sample items
What is the health status of fan of the given engine signal in one cycle? <ts> a: Health b: Fault
[Engine time-series sample: 95140.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
a
What is the health status of HPC of the given engine signal in one cycle? <ts> a: Health b: Fault
[Engine time-series sample: 40002.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
a
What is the health status of HPT of the given engine signal in one cycle? <ts> a: Health b: Fault
[Engine time-series sample: 79432.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
a
What is the potential health state of the given engine signal in one cycle? <ts> a: Operating Normally b: Abnormal Degradation c: Imminent Failure
[Engine time-series sample: 35002.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
What is the potential health state of the given engine signal in one cycle? <ts> a: Operating Normally b: Abnormal Degradation c: Imminent Failure
[Engine time-series sample: 118702.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
What is the potential health state of the given engine signal in one cycle? <ts> a: Operating Normally b: Abnormal Degradation c: Imminent Failure
[Engine time-series sample: 59450.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
Given the time series signal <ts>, by perceiving the engine signal across 10 cycles to reflect different health states and performing temporal inference to predict the health decline trend, what is the qualitative condition of the engine? a: Good Condition b: Moderate Condition c: Poor Condition d: Bad Condition e: Extremely Poor Condition
[Engine time-series sample: cycles=72925.csv,72947.csv,72990.csv,73013.csv,73032.csv,73052.csv,73067.csv,73096.csv,73123.csv,73162.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
Given the time series signal <ts>, by perceiving the engine signal across 10 cycles to reflect different health states and performing temporal inference to predict the health decline trend, what is the qualitative condition of the engine? a: Good Condition b: Moderate Condition c: Poor Condition d: Bad Condition e: Extremely Poor Condition
[Engine time-series sample: cycles=17718.csv,17724.csv,17732.csv,17738.csv,17746.csv,17756.csv,17765.csv,17773.csv,17782.csv,17788.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
What is the potential health state of the given engine signal in one cycle? <ts> a: Operating Normally b: Abnormal Degradation c: Imminent Failure
[Engine time-series sample: 29187.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
Given the time series signal <ts>, by perceiving the engine signal across 10 cycles to reflect different health states and performing temporal inference to predict the health decline trend, what is the qualitative condition of the engine? a: Good Condition b: Moderate Condition c: Poor Condition d: Bad Condition e: Extremely Poor Condition
[Engine time-series sample: cycles=41531.csv,41546.csv,41554.csv,41576.csv,41588.csv,41600.csv,41612.csv,41629.csv,41641.csv,41654.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
c
Given the time series signal <ts>, by perceiving the engine signal across 10 cycles to reflect different health states and performing temporal inference to predict the health decline trend, what is the probability range of engine failure? a: 1%-10% b: 10%-30% c: 30%-50% d: 50%-70% e: 70%-100%
[Engine time-series sample: cycles=5266.csv,5285.csv,5310.csv,5340.csv,5369.csv,5390.csv,5415.csv,5448.csv,5477.csv,5499.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
b
Given the time series signal <ts>, by perceiving the engine signal across 10 cycles to reflect different health states and performing temporal inference to predict the health decline trend, what is the probability range of engine failure? a: 1%-10% b: 10%-30% c: 30%-50% d: 50%-70% e: 70%-100%
[Engine time-series sample: cycles=30518.csv,30538.csv,30552.csv,30567.csv,30582.csv,30596.csv,30621.csv,30635.csv,30653.csv,30665.csv; N-CMAPSS aero-engine multivariate sensor signals, 33 channels.]
How subjects answered
b