Deep learning methods have shown promising performance in fault diagnosis for multimode process. Most existing studies assume that the collected health state categories from different operating modes are identical. However, in real industrial scenarios, these categories typically exhibit only partial overlap. The incompleteness of the available data and the large distributional differences between the operating modes pose a significant challenge to existing fault diagnosis methods. To address this problem, a novel fault diagnosis model named self-adaptive temporal-spatial attention network (TSA-SAN) is proposed. First, inter-mode mappings are constructed using healthy category data to generate multimode samples. To enrich the diversity of the fault data, interpolation is performed between healthy and fault samples. Subsequently, the fault diagnosis model is trained using real and generated data. The self-adaptive instance normalization is established to suppress irrelevant information while retaining essential statistical features for diagnosis. In addition, a temporal-spatial attention mechanism is constructed to focus on the key features, thus enhancing the generalization ability of the model. The extensive experiments demonstrate that the proposed model significantly outperforms the state-of-the-art methods. The code will be available on Github at https://github.com/GuangqiangLi/TSA-SAN.

Abstract--Fault diagnosis prevents train disruptions by ensuring the stability and reliability of their transmission systems. Data-driven fault diagnosis models have several advantages over traditional methods in terms of dealing with non-linearity, adaptability, scalability, and automation. However, existing data-driven models are trained on separate transmission components and only consider single faults due to the limitations of existing datasets. These models will perform worse in scenarios where components operate with each other at the same time, affecting each component's vibration signals. T o address some of these challenges, we propose a frequency domain representation and a 1-dimensional convolutional neural network for compound fault diagnosis and applied it on the PHM Beijing 2024 dataset, which includes 21 sensor channels, 17 single faults, and 42 compound faults from 4 interacting components, that is, motor, gearbox, left axle box, and right axle box. Our proposed model achieved 97.67% and 93.93% accuracies on the test set with 17 single faults and on the test set with 42 compound faults, respectively. Fault diagnosis plays a crucial role in maintaining the stability and reliability of transmission components, helping to prevent disruptions in train operations.

The safe and reliable operation of complex electromechanical systems in nuclear power plants is crucial for the safe production of nuclear power plants and their nuclear power unit. Therefore, accurate and timely fault diagnosis of nuclear power systems is of great significance for ensuring the safe and reliable operation of nuclear power plants. The existing fault diagnosis methods mainly target a single device or subsystem, making it difficult to analyze the inherent connections and mutual effects between different types of faults at the entire unit level. This article uses the AP1000 full-scale simulator to simulate the important mechanical component failures of some key systems in the primary and secondary circuits of nuclear power units, and constructs a fault dataset. Meanwhile, a hierarchical multi granularity classification fault diagnosis model based on the EfficientNet large model is proposed, aiming to achieve hierarchical classification of nuclear power faults. The results indicate that the proposed fault diagnosis model can effectively classify faults in different circuits and system components of nuclear power units into hierarchical categories. However, the fault dataset in this study was obtained from a simulator, which may introduce additional information due to parameter redundancy, thereby affecting the diagnostic performance of the model.

Railway Turnout Machines (RTMs) are mission-critical components of the railway transportation infrastructure, responsible for directing trains onto desired tracks. For safety assurance applications, especially in early-warning scenarios, RTM faults are expected to be detected as early as possible on a continuous 7x24 basis. However, limited emphasis has been placed on distributed model inference frameworks that can meet the inference latency and reliability requirements of such mission critical fault diagnosis systems. In this paper, an edge-cloud collaborative early-warning system is proposed to enable real-time and downtime-tolerant fault diagnosis of RTMs, providing a new paradigm for the deployment of models in safety-critical scenarios. Firstly, a modular fault diagnosis model is designed specifically for distributed deployment, which utilizes a hierarchical architecture consisting of the prior knowledge module, subordinate classifiers, and a fusion layer for enhanced accuracy and parallelism. Then, a cloud-edge collaborative framework leveraging pipeline parallelism, namely CEC-PA, is developed to minimize the overhead resulting from distributed task execution and context exchange by strategically partitioning and offloading model components across cloud and edge. Additionally, an election consensus mechanism is implemented within CEC-PA to ensure system robustness during coordinator node downtime. Comparative experiments and ablation studies are conducted to validate the effectiveness of the proposed distributed fault diagnosis approach. Our ensemble-based fault diagnosis model achieves a remarkable 97.4% accuracy on a real-world dataset collected by Nanjing Metro in Jiangsu Province, China. Meanwhile, CEC-PA demonstrates superior recovery proficiency during node disruptions and speed-up ranging from 1.98x to 7.93x in total inference time compared to its counterparts.

Deep learning models have created great opportunities for data-driven fault diagnosis but they require large amount of labeled failure data for training. In this paper, we propose to use a digital twin to support developing data-driven fault diagnosis model to reduce the amount of failure data used in the training process. The developed fault diagnosis models are also able to diagnose component-level failures based on system-level condition-monitoring data. The proposed framework is evaluated on a real-world robot system. The results showed that the deep learning model trained by digital twins is able to diagnose the locations and modes of 9 faults/failure from $4$ different motors. However, the performance of the model trained by a digital twin can still be improved, especially when the digital twin model has some discrepancy with the real system.

Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. Therefore, double gradient reversal network (DGRN) is proposed. First, the model is pre-trained to acquire fault knowledge from the single seen mode. Then, pseudo-fault feature generation strategy is designed by Adaptive instance normalization, to simulate fault features of unseen mode. The dual adversarial training strategy is created to enhance the diversity of pseudo-fault features, which models unseen modes with significant distribution differences. Subsequently, domain-invariant feature extraction strategy is constructed by contrastive learning and adversarial learning. This strategy extracts common features of faults and helps multi-mode fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman process and continuous stirred-tank reactor. The experiments demonstrate that DGRN achieves high classification accuracy on unseen modes while maintaining a small model size.

Intelligent fault diagnosis is essential to safe operation of machinery. However, due to scarce fault samples and data heterogeneity in field machinery, deep learning based diagnosis methods are prone to over-fitting with poor generalization ability. To solve the problem, this paper proposes a personalized federated learning framework, enabling multi-task fault diagnosis method across multiple factories in a privacypreserving manner. Firstly, rotating machines from different factories with similar vibration feature data are categorized into machine groups using a federated clustering method. Then, a multi-task deep learning model based on convolutional neural network is constructed to diagnose the multiple faults of machinery with heterogeneous information fusion. Finally, a personalized federated learning framework is proposed to solve data heterogeneity across different machines using adaptive hierarchical aggregation strategy. The case study on collected data from real machines verifies the effectiveness of the proposed framework. The result shows that the diagnosis accuracy could be improved significantly using the proposed personalized federated learning, especially for those machines with scarce fault samples.

Quantum computing (QC) and deep learning techniques have attracted widespread attention in the recent years. This paper proposes QC-based deep learning methods for fault diagnosis that exploit their unique capabilities to overcome the computational challenges faced by conventional data-driven approaches performed on classical computers. Deep belief networks are integrated into the proposed fault diagnosis model and are used to extract features at different levels for normal and faulty process operations. The QC-based fault diagnosis model uses a quantum computing assisted generative training process followed by discriminative training to address the shortcomings of classical algorithms. To demonstrate its applicability and efficiency, the proposed fault diagnosis method is applied to process monitoring of continuous stirred tank reactor (CSTR) and Tennessee Eastman (TE) process. The proposed QC-based deep learning approach enjoys superior fault detection and diagnosis performance with obtained average fault detection rates of 79.2% and 99.39% for CSTR and TE process, respectively.