Cross-Machine Anomaly Detection Leveraging Pre-trained Time-series Model
arXiv:2604.05335v1 Announce Type: new 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 gener
arXiv:2604.05335v1 Announce Type: new 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.
Executive Summary
The paper introduces a novel cross-machine anomaly detection framework for manufacturing, addressing the challenge of inconsistencies in nominally identical machines. By integrating a domain-invariant feature extractor with an unsupervised anomaly detection module, the authors leverage the pre-trained foundation model MOMENT to disentangle machine-specific and condition-specific features. Random Forest Classifiers refine these embeddings, enabling downstream detectors to generalize across unseen machines. Validation on an industrial dataset from three machines performing the same operation demonstrates superior performance over raw-signal and MOMENT-embedding baselines, showcasing enhanced cross-machine generalization. This work advances data-driven quality control in manufacturing by mitigating inter-machine variability through invariant feature learning.
Key Points
- ▸ Proposes a cross-machine anomaly detection framework for nominally identical machines in manufacturing, addressing inter-machine behavioral differences.
- ▸ Leverages the pre-trained time-series foundation model MOMENT to extract domain-invariant features, using Random Forest Classifiers to disentangle machine-related and condition-related embeddings.
- ▸ Demonstrates superior performance over raw-signal and MOMENT-embedding baselines on an industrial dataset from three machines, validating the framework's cross-machine generalization capabilities.
Merits
Innovative Feature Disentanglement
The paper introduces a novel approach to disentangle machine-specific and condition-specific features using a pre-trained foundation model (MOMENT) and Random Forest Classifiers, enabling robust cross-machine generalization in anomaly detection.
Practical Relevance
The proposed framework addresses a critical real-world challenge in manufacturing, where nominally identical machines exhibit behavioral differences, making it highly applicable to industrial quality control and predictive maintenance.
Empirical Validation
The approach is validated on an industrial dataset from three machines, outperforming baseline methods, which strengthens the credibility and practical utility of the proposed solution.
Demerits
Limited Generalization Assessment
The study evaluates the framework on only three machines performing the same operation, leaving open the question of its effectiveness across a broader range of machines, processes, or operating conditions.
Dependence on Foundation Model
The framework's performance hinges on the pre-trained MOMENT model, which may introduce biases or limitations inherited from the model's training data, potentially constraining its applicability in certain scenarios.
Complexity and Interpretability
The use of Random Forest Classifiers for feature disentanglement adds computational complexity and may reduce interpretability, which could be a drawback in high-stakes industrial applications requiring explainable AI.
Expert Commentary
This paper presents a compelling solution to a longstanding challenge in industrial anomaly detection: generalizing across nominally identical machines with heterogeneous behaviors. The integration of a pre-trained foundation model (MOMENT) with a domain-invariant feature extractor is particularly innovative, as it addresses the core issue of machine-specific variability without requiring extensive retraining for each new machine. The use of Random Forest Classifiers to disentangle features is a clever heuristic, though it introduces additional complexity that may warrant further investigation into its scalability and interpretability. The empirical validation on an industrial dataset is robust, but the study's scope is limited to three machines performing the same operation, which raises questions about its generalizability to more diverse scenarios. Nonetheless, the paper's contributions are highly relevant to both academic research and industrial practice, particularly in the context of Industry 4.0 and the growing reliance on AI-driven quality control. A deeper exploration of the framework's robustness to adversarial conditions or concept drift would further strengthen its applicability.
Recommendations
- ✓ Expand the evaluation to include a larger and more diverse set of machines and operations to assess the framework's generalizability and robustness across broader industrial scenarios.
- ✓ Investigate alternative feature disentanglement methods (e.g., variational autoencoders or adversarial training) to reduce complexity and improve interpretability, particularly for high-stakes applications.
- ✓ Collaborate with industry partners to deploy the framework in real-world manufacturing environments, gathering longitudinal data to assess its long-term performance and reliability under operational conditions.
Sources
Original: arXiv - cs.LG