Tensor SPC Tool

Tensor Residual Localization

Detection tells us something changed. Localization shows where and how the structured process behavior changed across sensors and time.

Purpose

Move from anomaly detection to structural diagnosis.

A Tensor SPC alarm should not stop at a single Q statistic. Residual localization decomposes the reconstruction error so the user can see which sensor-time region drove the alarm.

Example finding: Structural anomaly detected. The dominant residual contribution occurs in the vibration and surface-finish response region between time steps 55–85.

Core calculation

𝓔 = 𝓧 − 𝓧^

The residual tensor compares the observed tensor with the model reconstruction.

E2s,t = (Xs,tX^s,t)2

Residual energy is then visualized by sensor and time to locate the structural mismatch.

Manufacturing example

Same process, clearer diagnosis.

This demonstration uses a structured manufacturing process with several related features. The fault is intentionally subtle: individual signals remain plausible, but the relationship between vibration and surface finish changes during the middle of the run.

Tensor Q18.7
Q Limit7.9
StatusReview Needed

Residual energy heatmap

sensor × time localization
Feed Rate
Spindle Load
Tool Temperature
Vibration
Surface Finish
Coolant Flow
0102030405060708090100110
low residual elevated residual high residual

The heatmap is not raw process data. It shows where the tensor reconstruction error is concentrated.

Top contributing sensors

share of residual energy
Surface Finish
42%
Vibration
37%
Tool Temperature
11%
Coolant Flow
5%
Other
5%

Time contribution profile

when the anomaly emerged
localized time windowtimeresidual energy

Observed vs reconstructed response

top contributing signal
observedreconstructed expecteddivergence regiontimesurface finish proxy

The tensor model reconstructs the expected structured response from learned sensor-time relationships. The observed response diverges during the same region highlighted by the residual map.

Interpretation

The tensor model detected a localized structural deviation. The largest mismatch occurred in the vibration and surface-finish response during the middle portion of the observation. Individual process values may still appear plausible, but the coordinated behavior no longer matches the learned sensor-time structure.

Practical meaning: Tensor Residual Localization extends the alarm from “something changed” to “this sensor-time region drove the change.”

How to use the result

Localization supports investigation, not automatic conclusion.

The localization output should guide where to investigate. It does not replace engineering review or traditional SPC; it adds a structured diagnostic layer.

OutputQuestion answeredUse
Tensor QIs structured behavior abnormal?Screen for structural process change.
Residual heatmapWhere is the mismatch concentrated?Identify the sensor-time region that drove the alarm.
Sensor contributionsWhich signals contributed most?Prioritize investigation.
Observed vs reconstructedHow did actual behavior differ from expected behavior?Explain the anomaly in process terms.