Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Natalia Wojak-Strzelecka; Szymon Bobek; Grzegorz J. Nalepa; Jerzy Stefanowski

cs.LGMar 9, 2026

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Authors: Natalia Wojak-Strzelecka, Szymon Bobek, Grzegorz J. Nalepa, Jerzy Stefanowski

Organizations: 1Jagiellonian Human-Centered AI Lab, Mark Kac Center for Complex Systems Research, Institute of Applied Computer Science, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, ul. prof. Stanisława Łojasiewicza 11, 30-348 Krakow, Poland · Institute of Computing Science, Poznań University of Technology, 60-965 Poznań, Poland

Abstract

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such ''healthy'' changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory.

Figures & tables

Figure 1: The flow chart for differentiating between failures and domain shifts. The changepoint detection algorithm marks possible domain shifts and failures. After that, the domain adaptation algorithm updates the model that detects anomalies. Simultaneously human operator accompanied by the XAI algorithm decides if the change in the data represents a healthy domain shift or a failure.

Panel of Figure 2 — Figure 2: Raw signal for current_2 on the left, SHAP values plotted in batches for current_2 on the right.

Figure 3: The schematic diagram of four stand cold rollling mill.

Feature	Description	Unit
ithick/othick	coil thickness at the beginning/end of rolling	$\mathrm{mm}$
width	coil width	$\mathrm{mm}$
ys0/ys1	yield strength at the beginning/end of rolling	$\mathrm{MPa}$
work_roll_diam	diameter of roll	$\mathrm{mm}$
work_roll_mileage	mileage of roll	$\mathrm{mm}$
reduction	reduction on specific stand	%

Table 1: Description of cold rolling mill dataset.

Panel of Figure 4 — Figure 4: Anomaly detection algorithms evaluation on target products on important signals from rolling stand 2.

Figure 5: SHAP values median for: a) all parameters (on left), b) for important parameters for anomaly detection (on right).

Figure 6: Detected changepoints in the dataset.

Panel of Figure 7 — Figure 7: Boxplots of SHAP values important for bearing diagnosis.

Industrial Anomaly Detection

Industrial anomaly detection finds defective products and abnormal equipment behavior from visual, sensor, audio, and other production data.

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Open paper AskPDF arXiv

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.

Abstract

Industrial Anomaly Detection

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