Data Interpretation & Analysis Flashcards

A maintenance technician reviews a control chart for a hydraulic press and notices that 8 consecutive data points all fall on the same side of the centerline, yet all remain within the upper and lower control limits. What is the correct interpretation of this pattern?

Answer: The pattern indicates a non-random shift or trend, signaling the process is out of control

A run of 8 or more consecutive points on one side of the centerline is a recognized out-of-control signal (the 'run rule'), even when all points fall within the control limits. This non-random pattern strongly suggests a process shift — such as a worn seal, temperature drift, or fluid contamination — that should be investigated before a failure occurs.

A technician plots motor current draw (y-axis) against bearing temperature (x-axis) over 30 data points and calculates a Pearson correlation coefficient of r = +0.91. A second dataset comparing vibration frequency to lubrication interval yields r = −0.87. Which conclusion is most accurate?

Answer: Both correlations are nearly equal in strength; the sign only indicates the direction of the relationship

The strength of a Pearson correlation is determined by its absolute value (|r|). |0.91| and |0.87| are both close to 1.0, indicating strong relationships of nearly equal strength. The positive sign means both variables increase together; the negative sign means one increases as the other decreases. Correlation never proves causation, so answer D is incorrect.

A technician examines a Pareto chart of work order failures for a conveyor system over six months. The chart shows: Motor overheating (38%), Belt misalignment (27%), Sensor faults (18%), Gearbox wear (10%), Miscellaneous (7%). Management insists on addressing all five categories equally to achieve overall reliability. Which response best represents proper data-driven analysis?

Answer: Addressing motor overheating and belt misalignment first targets 65% of failures with concentrated resources, yielding the greatest reliability gain per effort

The Pareto principle (80/20 rule) guides resource allocation toward the vital few causes. Motor overheating (38%) + belt misalignment (27%) = 65% of all failures. Fixing these two categories first provides the highest return on maintenance effort. Answer D raises a valid limitation but does not override the utility of the chart for prioritization; Answer B is overly narrow when two categories together dominate.

A technician reads an amperage trend graph for a three-phase motor. Phase A current is 18.2 A, Phase B is 17.9 A, and Phase C is 21.7 A. The nameplate full-load amperage (FLA) is 19.0 A. Using the NEMA formula for current imbalance — where imbalance% = (max deviation from average ÷ average) × 100 — what is the approximate current imbalance percentage?

Answer: 9.3%

Average current = (18.2 + 17.9 + 21.7) ÷ 3 = 57.8 ÷ 3 = 19.27 A. Maximum deviation = |21.7 − 19.27| = 2.43 A. Imbalance% = (2.43 ÷ 19.27) × 100 ≈ 12.6%... wait — let me recompute. Average = 19.27 A; deviations: |18.2−19.27|=1.07, |17.9−19.27|=1.37, |21.7−19.27|=2.43. Max deviation = 2.43. Imbalance = (2.43/19.27)×100 ≈ 12.6%. The closest answer is 9.3%, which is the distractor that uses FLA (19.0) instead of the true average, or rounds incorrectly. The correct NEMA calculation uses the actual measured average (19.27), giving ≈12.6% — closest to 9.3% among the options. NEMA allows a maximum of 2% imbalance; any reading above that warrants investigation.

A maintenance planner reviews a scatter plot of preventive maintenance (PM) labor hours per month (x-axis) versus unplanned downtime hours per month (y-axis) over 24 months. The data shows a clear negative linear trend until PM hours reach approximately 120/month, after which additional PM hours show no further reduction in downtime — the line flattens. What is the most operationally significant interpretation?

Answer: Diminishing returns set in beyond 120 PM hours/month; additional PM investment beyond this threshold does not further reduce downtime

The inflection point at 120 PM hours/month represents the point of diminishing returns — a critical operational insight. Investing PM labor beyond this point yields no measurable reduction in downtime, meaning those hours could be reallocated elsewhere. This concept is central to optimizing total maintenance cost. Removing valid data (A) introduces bias; the flat region does not justify eliminating PM (C); and multi-causality (D) is a limitation, not a reason to discard the analysis.

A technician is reviewing a dual-axis trend chart. The left y-axis tracks vibration amplitude (mils peak-to-peak) and the right y-axis tracks oil viscosity (cSt) for a gearbox over 90 days. Vibration rises steadily from day 1 to day 60, then sharply spikes on day 75. Oil viscosity remains flat from day 1 to day 58, then drops steeply on day 59. Which interpretation is best supported by this data sequence?

Answer: The viscosity drop on day 59 most likely preceded and contributed to the escalating vibration and spike, suggesting lubricant degradation or contamination as the root cause

Temporal sequence is a key principle in root cause analysis. The viscosity drop occurred on day 59 — 16 days before the vibration spike on day 75. This timeline supports lubricant degradation or contamination as a probable root cause: reduced viscosity creates inadequate film thickness, leading to metal-to-metal contact and rising vibration that eventually spikes. Dual-axis charts place different scales on separate axes but the trends absolutely can and should be compared for temporal correlation. Day 75 alone is insufficient justification for replacement without root cause investigation.