Operational telemetry can be jointly anomalous while every individual stream stays inside its familiar range. TRACE-C is an auditable strictly-prior rank-calibrated detector for aligned multi-stream telemetry: same-regime rolling median/MAD residuals feed three window channels -- a maximum normalized local sum, a Gaussian copula-form dependence contrast on robust-z residuals, and a worst standardized AR(1) innovation -- whose channel ranks are Fisher-aggregated and ranked against earlier aggregates. We evaluate six Great Britain grid streams with a January-April 2019 fit, July-December 2019 development evidence, and a 2020 hold-out frozen before inspection. TRACE-C ranks Storm Atiyah first among 2019 test windows, but a disclosed channel ablation attributes that rank to the local channel, not the copula-form channel: copula-only ranks Atiyah 59th. The short 9 August frequency event is ranked far lower by the fused detector (143) than by the temporal channel alone (40), and reconstruction baselines rank it first. In 2020 no window is selected, which is consistent with record-rule saturation rather than an uneventful year; the highest-ranked frozen window was later interpreted as Storm Ellen. Three interpretive limits carry throughout. The resulting p-values are selection quantities, not event probabilities. The copula-form channel is not a literal copula density: the method applies no probability-integral or normal-score transform. Empirical rank counts are diagnostics, not coverage or false-discovery proofs. Every table and figure in this paper is generated from committed machine-readable reports.
Figures & tables
Figure 1: Empirical rank diagnostics under the shared scoring envelope: observed counts of outer rank values at thresholds .05 and .01 , expressed as a ratio to the arithmetic benchmark αn (dashed line). Values are read directly from the committed reports. Descriptive only: autocorrelation, drift, reused references, and known events preclude reading these as uniform p -value or false-discovery guarantees (Section 5 ).
Figure 2: Frozen 2020 segment: aggregate score S for each of the 4392 two-hour windows, scored with strictly-prior references under the configuration committed before 2020 data was examined. Shaded bands mark the post-hoc event annotations (Storms Ciara and Dennis; the COVID-19 lockdown onset). The record rule selected no 2020 window (Section 5 ); event windows rank in the upper tail without crossing the record threshold.
Segment and threshold
Observed/reference
2019 development, p≤.05
120/110.4
2019 development, p≤.01
23/22.1
2020 pre-COVID, p≤.05
50/45.0
2020 pre-COVID, p≤.01
3/9.0
2020 full year, p≤.05
205/219.6
Table 1: Empirical rank-p diagnostics. Expected counts are exchangeable-score references, not time-series coverage guarantees.
Annotation
Segment
Best rank
Best p
Opportunity windows
GB frequency event
2019 development
143
0.060420
5
Storm Atiyah
2019 development
1
0.000350
12
Storm Ciara
2020 frozen hold-out
44
0.012058
24
Storm Dennis
2020 frozen hold-out
137
0.033217
24
Lockdown transition (14 days; best 2020-03-28 06:00)
2020 frozen hold-out
45
0.012559
168
Table 2: TRACE-C event-window ranks. Labels are used only for post-hoc evaluation.
External interpretation
Rank
Timestamp
p
Storm Ellen (post-ranking interpretation)
1
2020-08-20 03:00
0.000504
Storm Alex (post-ranking interpretation)
5
2020-10-03 23:00
0.001539
Table 3: Externally interpreted, top-ranked 2020 windows. Neither name was a detector annotation and no 2020 window passed the frozen record rule.
Variant
Channels
Atiyah rank
Blackout rank
Alerts
full (L+G+T)
local+copula+temporal
1 (alert)
143
2
drop copula
local+temporal
2
69
1
drop local
copula+temporal
3
119
0
drop temporal
local+copula
5
338
1
local only
local
3
241
0
copula only
copula
59
323
0
Table 4: 2019 development-year channel ablation, not a hold-out. The full detector’s Atiyah lead channel was inspected before these reruns. Lower rank is better.
Detector
Blackout
Atiyah
Ciara
Dennis
Lockdown window
Pre-COVID p≤.05
TRACE-C
143
1
44
137
45 (2020-03-28)
50/45.0
Convolutional autoencoder
1
34
1
29
46 (2020-04-05)
27/45.0
PCA reconstruction
1
12
12
34
22 (2020-04-05)
82/45.0
Isolation Forest
2
3
10
178
1 (2020-04-05)
49/45.0
Spectral Residual
2
26
12
38
6 (2020-03-28)
93/45.0
Table 5: Post-hoc detector comparison. Lower event rank is better. The lockdown column searches a 168-window interval; parenthesized dates show each detector’s best window. Protocols are not identical and only TRACE-C was frozen before 2020 inspection.
A reading departs from what a monitoring system expects for one of three reasons: the physical process changed, the instrument is failing, or the value was corrupted between the instrument and the software. The three call for different responses and look alike in a single sample. We state the three-cause problem, prove which pairs of causes one channel cannot separate, and propose a bank of online detectors - a Kalman tracker with CUSUM and EWMA charts on its innovation, a Hampel median with generalized-ESD confirmation, a robust noise statistic with ADWIN, exact-value checks and an optional two-channel complementary filter - combined by a deterministic, inspectable attribution rule. The detectors were merged into a public algorithm library. On 2,942 injected events of thirteen fault types the method detects 96.3% (95% interval 95.7-97.0) at 0.22 false alarms per hour and attributes 91.2% (90.6-91.8) of detected events correctly from one channel and 99.0% (98.6-99.4) with a second, rate channel; from one channel only 2 of 207 detected sensor drifts are attributed to the instrument, as the problem statement predicts. Injected into recorded industrial signals, the same catalogue is detected at 80.8% and attributed correctly at 84.5%, at 3.6 false alarms per hour. On the Skoltech Anomaly Benchmark the method detects 32 of 33 experiments with a median delay of 15 s, at 46 false alarms per hour against 11 for a rolling z-score.
Operational anomaly detection in spacecraft telemetry typically requires labeled historical anomalies or extended warm-up periods. These requirements are rarely met in practice. We propose an unsupervised, deployment-ready framework that produces predictions from the second month of operation without any labels, prior fault knowledge, or mission-specific tuning. The approach combines incremental monthly retraining, statistical model selection, and adaptive Extreme Value Theory (EVT) thresholding for false alarm control. On the ESA Anomalies Dataset (ESA-AD), it achieves F0.5=0.700 on Mission1 and F0.5=0.698 on Mission2 under strict chronological evaluation.
Óscar Alcarria, Rafael Sánchez, Javier Sempere +5
Universidad de Castilla-La Mancha, Albacete, Spain · GMV GmbH, Darmstadt, Germany · GMV, Madrid, Spain
EDF relies on continuous monitoring of its power plants to detect anomalies as soon as they occur. Given the absence of a universally optimal streaming method in unsupervised settings, we compare streaming methods with state-of-the-art TSAD models deployed online on a real nuclear power plant dataset. This work also evaluates Automated Anomaly Detection in a streaming context. Results show higher consistency for online TSAD and strong robustness from ensembling strategies.