Fault detection and diagnosis (FDD) is a crucial task for ensuring the safety and efficiency of industrial processes. We propose a novel FDD methodology for the Tennessee Eastman Process (TEP), a widely used benchmark for chemical process control. The model employs two separate Transformer branches, enabling independent processing of input data and potential extraction of diverse information. A novel attention mechanism, Gated Dynamic Learnable Attention (GDLAttention), is introduced which integrates a gating mechanism and dynamic learning capabilities. The gating mechanism modulates the attention weights, allowing the model to focus on the most relevant parts of the input. The dynamic learning approach adapts the attention strategy during training, potentially leading to improved performance. The attention mechanism uses a bilinear similarity function, providing greater flexibility in capturing complex relationships between query and key vectors. In order to assess the effectiveness of our approach, we tested it against 21 and 18 distinct fault scenarios in TEP, and compared its performance with several established FDD techniques. The outcomes indicate that the method outperforms others in terms of accuracy, false alarm rate, and misclassification rate. This underscores the robustness and efficacy of the approach for FDD in intricate industrial processes.

Rolling bearing fault detection has developed rapidly in the field of fault diagnosis technology, and it occupies a very important position in this field. Deep learning-based bearing fault diagnosis models have achieved significant success. At the same time, with the continuous improvement of new signal processing technologies such as Fourier transform, wavelet transform and empirical mode decomposition, the fault diagnosis technology of rolling bearings has also been greatly developed, and it can be said that it has entered a new research stage. However, most of the existing methods are limited to varying degrees in the industrial field. The main ones are fast feature extraction and computational complexity. The key to this paper is to propose a lightweight bearing fault diagnosis model DKDL-Net to solve these challenges. The model is trained on the CWRU data set by decoupling knowledge distillation and low rank adaptive fine tuning. Specifically, we built and trained a teacher model based on a 6-layer neural network with 69,626 trainable parameters, and on this basis, using decoupling knowledge distillation (DKD) and Low-Rank adaptive (LoRA) fine-tuning, we trained the student sag model DKDL-Net, which has only 6838 parameters. Experiments show that DKDL-Net achieves 99.48% accuracy in computational complexity on the test set while maintaining model performance, which is 0.58% higher than the state-of-the-art (SOTA) model, and our model has lower parameters. Our code is available at Github link: https://github.com/SPBU-LiPengyi/DKDL-Net.git.

Synthetic datasets generated by structural causal models (SCMs) are commonly used for benchmarking causal structure learning algorithms. However, the variances and pairwise correlations in SCM data tend to increase along the causal ordering. Several popular algorithms exploit these artifacts, possibly leading to conclusions that do not generalize to real-world settings. Existing metrics like $\operatorname{Var}$-sortability and $\operatorname{R^2}$-sortability quantify these patterns, but they do not provide tools to remedy them. To address this, we propose internally-standardized structural causal models (iSCMs), a modification of SCMs that introduces a standardization operation at each variable during the generative process. By construction, iSCMs are not $\operatorname{Var}$-sortable, and as we show experimentally, not $\operatorname{R^2}$-sortable either for commonly-used graph families. Moreover, contrary to the post-hoc standardization of data generated by standard SCMs, we prove that linear iSCMs are less identifiable from prior knowledge on the weights and do not collapse to deterministic relationships in large systems, which may make iSCMs a useful model in causal inference beyond the benchmarking problem studied here.

In this paper, we present a data-driven model for estimating optimal rework policies in manufacturing systems. We consider a single production stage within a multistage, lot-based system that allows for optional rework steps. While the rework decision depends on an intermediate state of the lot and system, the final product inspection, and thus the assessment of the actual yield, is delayed until production is complete. Repair steps are applied uniformly to the lot, potentially improving some of the individual items while degrading others. The challenge is thus to balance potential yield improvement with the rework costs incurred. Given the inherently causal nature of this decision problem, we propose a causal model to estimate yield improvement. We apply methods from causal machine learning, in particular double/debiased machine learning (DML) techniques, to estimate conditional treatment effects from data and derive policies for rework decisions. We validate our decision model using real-world data from opto-electronic semiconductor manufacturing, achieving a yield improvement of 2 - 3% during the color-conversion process of white light-emitting diodes (LEDs).

The service conditions of wheelset bearings has a direct impact on the safe operation of railway heavy haul freight trains as the key components. However, speed fluctuation of the trains and few fault samples are the two main problems that restrict the accuracy of bearing fault diagnosis. Therefore, a cross-machine transfer diagnosis (pyDSN) network coupled with interpretable modulated differentiable short-time Fourier transform (STFT) and physics-informed balanced spectrum quality metric is proposed to learn domain-invariant and discriminative features under time-varying speeds. Firstly, due to insufficiency in extracting extract frequency components of time-varying speed signals using fixed windows, a modulated differentiable STFT (MDSTFT) that is interpretable with STFT-informed theoretical support, is proposed to extract the robust time-frequency spectrum (TFS). During training process, multiple windows with different lengths dynamically change. Also, in addition to the classification metric and domain discrepancy metric, we creatively introduce a third kind of metric, referred to as the physics-informed metric, to enhance transferable TFS. A physics-informed balanced spectrum quality (BSQ) regularization loss is devised to guide an optimization direction for MDSTFT and model. With it, not only can model acquire high-quality TFS, but also a physics-restricted domain adaptation network can be also acquired, making it learn real-world physics knowledge, ultimately diminish the domain discrepancy across different datasets. The experiment is conducted in the scenario of migrating from the laboratory datasets to the freight train dataset, indicating that the hybrid-driven pyDSN outperforms existing methods and has practical value.

One of the most significant obstacles in bearing fault diagnosis is a lack of labeled data for various fault types. Also, sensor-acquired data frequently lack labels and have a large amount of missing data. This paper tackles these issues by presenting the PTPAI method, which uses a physics-informed deep learning-based technique to generate synthetic labeled data. Labeled synthetic data makes up the source domain, whereas unlabeled data with missing data is present in the target domain. Consequently, imbalanced class problems and partial-set fault diagnosis hurdles emerge. To address these challenges, the RF-Mixup approach is used to handle imbalanced classes. As domain adaptation strategies, the MK-MMSD and CDAN are employed to mitigate the disparity in distribution between synthetic and actual data. Furthermore, the partial-set challenge is tackled by applying weighting methods at the class and instance levels. Experimental outcomes on the CWRU and JNU datasets indicate that the proposed approach effectively addresses these problems.

To address the need for robust positioning, navigation, and timing services in lunar and Martian environments, this paper proposes a novel fault detection framework for satellite constellations using inter-satellite ranging (ISR). Traditional fault monitoring methods rely on intense monitoring from ground-based stations, which are impractical for lunar and Martian missions due to cost constraints. Our approach leverages graph-rigidity theory to detect faults without relying on precise ephemeris. We model satellite constellations as graphs where satellites are vertices and inter-satellite links are edges. By analyzing the Euclidean Distance Matrix (EDM) derived from ISR measurements, we identify faults through the singular values of the geometric-centered EDM (GCEDM). A neural network predictor is employed to handle the diverse geometry of the graph, enhancing fault detection robustness. The proposed method is validated through simulations of constellations around Mars and the Moon, demonstrating its effectiveness in various configurations. This research contributes to the reliable operation of satellite constellations for future lunar and Martian exploration missions.

Machine learning and statistical methods can be used to enhance monitoring and fault prediction in marine systems. These methods rely on a dataset with records of historical system behaviour, potentially containing periods of both fault-free and faulty operation. An unexpected change in the underlying system, called a concept drift, may impact the performance of these methods, triggering the need for model retraining or other adaptations. In this article, we present an approach for detecting overheating in stator windings of marine propulsion motors that is able to successfully operate during concept drift without the need for full model retraining. Two distinct approaches are presented and tested. All models are trained and verified using a dataset from operational propulsion motors, with known, sudden concept drifts.

With the integration of advanced technologies and automation systems in manufacturing, available data formats are evolving to include complex data streams, such as sequences of images and videos, which can provide valuable information on the state of the machines and their components. The timely identification and diagnosis of faults can prevent equipment failure, reduce maintenance costs, improve system performance, and enhance safety. In recent years, Machine Learning (ML) algorithms, including Deep Learning (DL) models, have shown great promise in automating fault detection and diagnosis tasks. However, these models are often viewed as black boxes, making it challenging to understand how they arrived at their predictions. This lack of transparency can pose a significant barrier to adopting machine learning in safety-critical applications, where the interpretability and trustworthiness of the model are essential.

Text watermarking algorithms for large language models (LLMs) can effectively identify machine-generated texts by embedding and detecting hidden features in the text. Although the current text watermarking algorithms perform well in most high-entropy scenarios, its performance in low-entropy scenarios still needs to be improved. In this work, we opine that the influence of token entropy should be fully considered in the watermark detection process, $i.e.$, the weight of each token during watermark detection should be customized according to its entropy, rather than setting the weights of all tokens to the same value as in previous methods. Specifically, we propose \textbf{E}ntropy-based Text \textbf{W}atermarking \textbf{D}etection (\textbf{EWD}) that gives higher-entropy tokens higher influence weights during watermark detection, so as to better reflect the degree of watermarking. Furthermore, the proposed detection process is training-free and fully automated. From the experiments, we demonstrate that our EWD can achieve better detection performance in low-entropy scenarios, and our method is also general and can be applied to texts with different entropy distributions. Our code and data is available\footnote{\url{https://github.com/luyijian3/EWD}}. Additionally, our algorithm could be accessed through MarkLLM \cite{pan2024markllm}\footnote{\url{https://github.com/THU-BPM/MarkLLM}}.