arxiv: 2604.05335 · v1 · submitted 2026-04-07 · 💻 cs.LG · eess.SP

Recognition: no theorem link

Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model

Yangmeng Li , Kei Sano , Toshihiro Kitao , Ryoji Anzaki , Yukiya Saitoh , Hironori Moki , Dragan Djurdjanovic

Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:52 UTC · model grok-4.3

classification 💻 cs.LG eess.SP

keywords anomaly detectioncross-machine generalizationtime seriespre-trained modelfeature disentanglementrandom forestmanufacturingdomain invariance

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The pith

Disentangling time-series embeddings with random forests yields machine-invariant features for cross-machine anomaly detection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper seeks to make anomaly detection work when the training data comes from different but nominally identical machines that run the same process. It takes embeddings from a pre-trained time-series model and uses random forest classifiers to split them into features that depend on the particular machine and features that depend only on the operating condition. The condition features are fed to the anomaly detection module, which can then be applied to a new machine. A reader would care because this reduces the data collection burden in real-world manufacturing settings where each machine has its own quirks.

Core claim

The paper claims that applying Random Forest Classifiers to disentangle embeddings from a pre-trained time-series model into machine-related and condition-related features creates representations invariant to machine differences, so that anomaly detectors trained this way generalize to unseen target machines, as shown by superior performance over baselines on an industrial dataset from three machines.

What carries the argument

The domain-invariant feature extractor consisting of Random Forest Classifiers applied to embeddings from the pre-trained MOMENT model to isolate condition-related features.

If this is right

Downstream anomaly detectors can generalize effectively to unseen target machines.
The method outperforms raw-signal-based anomaly detection and direct use of MOMENT embeddings.
Cross-machine generalization is enhanced for nominally identical machines performing the same operations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Factories with fleets of similar machines could deploy a single trained detector rather than one per machine.
The technique might transfer to other time-series tasks involving domain shifts, such as predictive maintenance across equipment variants.
Further work could explore whether other classifiers or unsupervised disentanglement methods yield even better invariance.

Load-bearing premise

The random forest classifiers succeed in separating machine-specific information from condition-specific information within the pre-trained embeddings.

What would settle it

Train the system on data from two machines and test anomaly detection performance on the third; if the disentangled features do not yield higher accuracy than the raw embeddings, the core separation step would be falsified.

read the original abstract

Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among different individual machines which are nominally the same and are executing the same processes. To address the problem of detecting anomalies in a machine using sensory data gathered from different individual machines executing the same procedure, this paper proposes a cross-machine time-series anomaly detection framework that integrates a domain-invariant feature extractor with an unsupervised anomaly detection module. Leveraging the pre-trained foundation model MOMENT, the extractor employs Random Forest Classifiers to disentangle embeddings into machine-related and condition-related features, with the latter serving as representations which are invariant to differences between individual machines. These refined features enable the downstream anomaly detectors to generalize effectively to unseen target machines. Experiments on an industrial dataset collected from three different machines performing nominally the same operation demonstrate that the proposed approach outperforms both the raw-signal-based and MOMENT-embedding feature baselines, confirming its effectiveness in enhancing cross-machine generalization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a workable pipeline for cross-machine anomaly detection by splitting MOMENT embeddings with random forests, but the invariance of the kept features is not guaranteed.

read the letter

The paper takes MOMENT embeddings from time-series sensor data and uses random forest classifiers to split the dimensions into machine-related and condition-related groups, then feeds only the condition features into an unsupervised anomaly detector. This lets the detector work on a new machine without retraining on its data. The combination is a straightforward extension of existing tools to the cross-machine setting, and it is applied to real industrial data from three machines doing the same task. The leave-one-machine-out tests show gains over raw signals and plain MOMENT embeddings, which is the main empirical result. That part is useful because it tackles a common factory problem where nominally identical machines still differ enough to break standard detectors. Using a pre-trained model also keeps the approach lightweight. The soft spot is the disentanglement step itself. Random forest feature importance does not enforce zero mutual information with machine identity, so some machine-specific signal can stay in the retained features. With only three machines in the dataset, any leftover correlation would not be visible in the reported results and could hurt performance on a fourth unseen machine. The abstract also skips dataset sizes, anomaly definitions, and statistical tests, which makes the size of the improvement harder to judge. The stress-test concern about leakage holds up. This is the sort of applied work that would interest people building monitoring systems in manufacturing or working on domain adaptation for time series. It is coherent on its own terms and deserves a serious referee to check the full experiments and perhaps ask for stronger validation of the separation step.

Referee Report

2 major / 2 minor

Summary. The paper proposes a cross-machine time-series anomaly detection framework for manufacturing machines performing the same nominal operation. It extracts embeddings from the pre-trained MOMENT foundation model, applies Random Forest classifiers to disentangle them into machine-related and condition-related features, retains only the latter as domain-invariant representations, and feeds them to an unsupervised anomaly detector. Experiments on an industrial dataset from three machines demonstrate outperformance over raw-signal and full MOMENT-embedding baselines, supporting improved cross-machine generalization.

Significance. If the RF-based disentanglement reliably isolates machine-invariant condition features, the work could meaningfully advance practical anomaly detection in industrial settings by combining foundation models with lightweight domain adaptation. The approach is conceptually straightforward and leverages an existing pre-trained model, which is a positive attribute. However, the empirical support rests on a small number of machines with limited methodological details, so the significance remains moderate pending stronger validation of the invariance claim.

major comments (2)

[Abstract / Experiments] Abstract and Experiments section: The central empirical claim states that the method outperforms two baselines on data from three machines, yet provides no information on dataset size, number of samples per machine, anomaly definitions or labeling process, choice of unsupervised detector, or any statistical testing. This absence leaves the reported generalization gains difficult to interpret or reproduce and directly weakens support for the cross-machine effectiveness conclusion.
[Method] Method section (feature extractor): The Random Forest procedure for partitioning MOMENT embeddings into condition-related features does not enforce orthogonality or zero mutual information with machine identity. Standard RF importance or selection steps can retain dimensions that still carry machine-specific variance; with only three machines and leave-one-machine-out evaluation, any such leakage would be invisible in the reported results yet would invalidate the invariance assumption required for true generalization to unseen machines.

minor comments (2)

[Abstract] The abstract refers to an 'unsupervised anomaly detection module' without naming the specific algorithm (e.g., isolation forest, autoencoder) or its hyperparameters; this should be stated explicitly for reproducibility.
[Method] Notation for the disentangled feature subsets (machine-related vs. condition-related) is introduced informally; a short mathematical definition or diagram in the method section would improve clarity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We sincerely thank the referee for the constructive and detailed feedback on our manuscript. We have carefully reviewed the major comments and will make targeted revisions to improve the clarity, reproducibility, and rigor of the empirical and methodological sections. Point-by-point responses follow.

read point-by-point responses

Referee: [Abstract / Experiments] Abstract and Experiments section: The central empirical claim states that the method outperforms two baselines on data from three machines, yet provides no information on dataset size, number of samples per machine, anomaly definitions or labeling process, choice of unsupervised detector, or any statistical testing. This absence leaves the reported generalization gains difficult to interpret or reproduce and directly weakens support for the cross-machine effectiveness conclusion.

Authors: We appreciate this observation and agree that additional quantitative details are necessary for interpretability and reproducibility. While the manuscript describes the industrial dataset collected from three machines performing the same nominal operation, we acknowledge that specific sample counts, anomaly labeling criteria, the exact unsupervised detector, and statistical tests were not elaborated sufficiently. In the revised manuscript we will expand the Experiments section to report: total and per-machine sample sizes, a precise description of how anomalies were defined and labeled (including the process used), the specific unsupervised anomaly detection method and its hyperparameters, and statistical significance tests (e.g., paired Wilcoxon tests with p-values) comparing our approach against the baselines. These additions will directly strengthen the support for the cross-machine generalization claims. revision: yes
Referee: [Method] Method section (feature extractor): The Random Forest procedure for partitioning MOMENT embeddings into condition-related features does not enforce orthogonality or zero mutual information with machine identity. Standard RF importance or selection steps can retain dimensions that still carry machine-specific variance; with only three machines and leave-one-machine-out evaluation, any such leakage would be invisible in the reported results yet would invalidate the invariance assumption required for true generalization to unseen machines.

Authors: We acknowledge that the Random Forest importance-based selection is a heuristic and does not formally enforce orthogonality or zero mutual information with machine identity. Residual machine-specific variance could therefore remain, and with only three machines the leave-one-machine-out protocol may not detect such leakage. In the revision we will add: (1) explicit computation and reporting of mutual information between the retained condition-related features and machine labels, (2) a clearer discussion of this methodological limitation, and (3) a sensitivity analysis showing how performance varies with different importance thresholds. While these steps do not convert the method into a provably invariant representation, they will provide quantitative evidence on the degree of disentanglement achieved and allow readers to assess the strength of the invariance assumption. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical method validated on held-out machines

full rationale

The paper presents an applied framework that extracts MOMENT embeddings, applies Random Forest classifiers to partition dimensions into machine-related versus condition-related subsets, and feeds the latter into an unsupervised anomaly detector. The load-bearing claim is the empirical result that this pipeline outperforms raw-signal and full-embedding baselines under leave-one-machine-out evaluation on three industrial machines. No derivation, equation, or 'prediction' is offered that reduces by construction to the training labels or fitted parameters; the invariance property is asserted as a modeling choice whose effectiveness is tested rather than assumed tautologically. Self-citation of MOMENT is not load-bearing for the cross-machine result. The method therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The framework rests on the assumption that MOMENT embeddings contain separable machine and condition information that random forests can isolate without additional supervision.

axioms (1)

domain assumption MOMENT embeddings contain disentangleable machine-specific and condition-specific information.
Invoked when the random forest classifiers are applied to separate the two types of features.

pith-pipeline@v0.9.0 · 5489 in / 1133 out tokens · 44522 ms · 2026-05-10T19:52:30.986470+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

44 extracted references · 7 canonical work pages

[1]

out-of-the-box

Introduction With the increasing demand for product quality and manufacturing system resilience, efficient data-driven anomaly detection methods have become increasingly important [1]. The failure to detect even minor deviations from normal operating conditions may result in significant quality and financial losses due to production downtime and product d...
[2]

MOMENT Model MOMENT is a family of open-source large-scale pre-trained time-series models [23], which can serve as a foundational component for various time-series analysis tasks

Related Work 2.1. MOMENT Model MOMENT is a family of open-source large-scale pre-trained time-series models [23], which can serve as a foundational component for various time-series analysis tasks. Specifically, one of the functions MOMENT can do is to generate embeddings of length 1024 from multivariate time series data by its encoder. The encoder compon...
[3]

Formally, we observe source datasets 𝒟𝑆 = {𝒟𝑆1, 𝒟𝑆2, … , 𝒟𝑆𝑚} and an unlabeled target-domain dataset 𝒟𝑇

Methodology This study addresses cross-machine anomaly detection, where knowledge extracted using solely data from one set of machines is transferred to accomplishing anomaly detection on an unseen target machine. Formally, we observe source datasets 𝒟𝑆 = {𝒟𝑆1, 𝒟𝑆2, … , 𝒟𝑆𝑚} and an unlabeled target-domain dataset 𝒟𝑇. Each source-domain dataset 𝒟𝑆𝑘 = {(𝐗, ...
[4]

The top 𝑁𝐼 most important features for this task constitute the feature set 𝐹𝑐𝑑

Machine Condition Classification, which identifies normal versus abnormal machine states. The top 𝑁𝐼 most important features for this task constitute the feature set 𝐹𝑐𝑑
[5]

The top 𝑁𝐼 most important features for this task constitute the feature set 𝐹𝑚𝑎

Machine Identity Classification, which determines from which source machine an embedding originates. The top 𝑁𝐼 most important features for this task constitute the feature set 𝐹𝑚𝑎. If the overlap between 𝐹𝑐𝑑 and 𝐹𝑚𝑎 is small (e.g., less than 10% of 𝑁𝐼), then 𝐹𝑐𝑑 is considered domain-invariant because it predominantly relates to machine condition rather t...
[6]

Dataset The proposed methodology was evaluated using data collected from in-house testing equipment that transports loads via a motor-driven conveyor belt system

Experiment Setup 4.1. Dataset The proposed methodology was evaluated using data collected from in-house testing equipment that transports loads via a motor-driven conveyor belt system. During each operational cycle, angular torque signals from the motor and angular velocity signals from the load were simultaneously recorded at a high sampling rate. Data f...
[7]

Time-shifting augmentation: Normal signals are shifted forward by five time steps, generating temporally altered but label-preserving variants
[8]

Given two time-series samples 𝒙1 and 𝒙2, a new sample 𝒙′ is created as: 𝒙′ = 𝜆𝒙1 + (1 − 𝜆)𝒙2, 𝜆 ∈ [0,1]

Mix-up augmentation: New signal records are synthesized by linearly combining existing ones. Given two time-series samples 𝒙1 and 𝒙2, a new sample 𝒙′ is created as: 𝒙′ = 𝜆𝒙1 + (1 − 𝜆)𝒙2, 𝜆 ∈ [0,1]. Time-shifting augmentation simulates observations at different operational starting points while maintaining signal alignment. The Mix-up augmentation efficien...
[9]

Results The performance of experimental results is reported using precision, recall, F1-score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), and Area Under Precision-Recall Curve (AUPRC) [36]. Precision, recall, and F1-score are defined respectively as Precision = 𝑇𝑃 𝑇𝑃 + 𝐹𝑃 , Recall = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 , F1 score = 2 ∙ Precision ∙ Recall Pre...
[10]

Conclusion and Future Work This paper presents a cross-machine time-series anomaly detection framework that integrates a newly proposed domain-invariant feature extractor with the downstream unsupervised anomaly detector. The feature extractor employs two Random Forest Classifiers (RFCs) to disentangle the features derived from the MOMENT embeddings into ...
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