Ji Xing

The advancement of large models has initiated a transformation in the field of time-series forecasting. Both the repurposing of existing large models and the development of large models tailored for time-series analysis have exhibited impressive performance. In industrial applications, challenges, such as limited data availability and constrained computational resources, render the first approach viable. However, it is important to note that this approach is still in its infancy and lacks both a thorough technical analysis and a unified effective framework. Meanwhile, as large models become a mainstream artificial intelligence paradigm, it is urgent to discuss typical industrial scenarios, such as how automated systems can transition from intelligent to collaborative operation and maintenance. In light of this premise, this article endeavors to advance a generalized technical framework for large model-driven time-series forecasting, under which existing methods can be subsumed. Then, within this overarching technical paradigm, the technical advancements facilitated by diverse methods will be systematically elucidated and analyzed, along with a comparative evaluation conducted across seven benchmark datasets. Concluding this analysis, the implementation pathway for the industrial automation system is delineated that integrates operator action commands to forecast post-action trends to assess action correctness in advance. Finally, the challenges and future directions of large model-based time-series forecasting are outlined.

With the transformation of the world’s energy structure and the promotion of clean energy, nuclear power has come to the fore as an efficient and environmentally friendly form of energy. As critical components in the operation of nuclear power plants (NPPs), suction and transportation devices for thermal traps—designed to prevent and control floating bodies—are directly linked to the safety of power generation, the stability of operations, and the reliability of the power grid in NPPs. Technical research and advancements in these devices play a crucial role in enhancing their effectiveness. In view of the intrusion and blockage of floating bodies in the sewage network pocket of the water intake with the cold-source system for coastal NPPs, the key technologies to prevent and control floating bodies that may impair cooling resources in NPPs’ thermal traps is discussed. This paper introduces the safety risks and social impacts associated with water intake, highlights the importance of the water-intake system, and discusses the interception and transportation mechanisms in NPPs. The progress of explosive floating bodies’ interception, cleaning, suction, transportation, and intelligent monitoring technology is introduced through the technical progress in the types of floating bodies, as well as cutting, crushing, suction, and transportation devices. The technical progress of efficient treatment is realized, which provides reference for the research and practice of the key technologies for suction and transportation devices to prevent and control floating bodies’ suction and transportation devices in NPPs’ thermal traps.

Due to coupled nonlinearities and complex measurement noise, assess the condition of the rotor system remains a challenge, particularly in cases where historical run-to-failure data is lacking. To this end, we proposed a hierarchical physics-informed neural network (HPINN) to identify/discover the ordinary differential equations (ODEs) of a healthy/faulty rotor system from noise measurements and then assess the rotor condition based on the discovered ODEs. Specifically, the ODEs of a healthy rotor system are first stably identified from noisy measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODEs, the extra fault terms in the ODEs of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN, in which the phase compensation and alternating training strategy are developed to guarantee training convergence. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified with simulation and test bench datasets, showing the potential for practical industrial applications. Note to Practitioners —This paper investigates the health assessment problem (condition monitoring and fault diagnosis) of the rotor system, a critical component in large rotating machinery. The proposed HPINN provides a hierarchical framework to firstly identify the ODEs of healthy rotor system and then discover the ODEs of faulty rotor system with limited monitoring data (3-5 seconds data collected from sensor commonly, depending on the rotating speeds). With the mathematical terms of discovered fault, the fault can be diagnosed and a health indicator (HI) can be constructed to assess the condition of rotor system in a fully interpretative way. This approach is applicable to large rotating machinery in safety-critical industries, such as circulating water pumps.

As the cornerstone of severe accident management strategy, either in-vessel or ex-vessel retention of core melt (IVR or EVR) plays a pivotal role in the stabilization and termination of a severe accident, and ultimately secures the safety goal of “Practical elimination of large radioactive release” for light water reactors. In contrast to the IVR measures that are more or less identical in reactor designs, the EVR measures are quite different from design to design. This paper was intended to give a critical review on the EVR measures adopted in the reactor designs of VVER-1000, EPR, ESBWR, EU-APR1400 and APWR. The review study began with a general description of the existing EVR measures, including their principles, operational procedures and research efforts. We then focused our discussions on the pros and cons of each EVR measure, through the comparisons with the IVR and with the others in terms of simplicity, reliability and economy. We finally tried to identify the remaining issues and uncertainties in the qualification of the EVR measures, based on which potential design improvements and future research needs were recommended.

Ensuring reliable machine operation is dependent on effective fault detection. Feature indicators construction is considered as the foundation for operational condition assessment among various detection methods. However, the nuclear circulating water pump (NCWP) experiences severe disturbances due to noise and changes in operating conditions. Additionally, the complex movement of the gears causes signal interference, which may result in the feature indicators failing. To address the above issues, this paper proposes a generalized features construction method (GFC) to realize unsupervised planetary gearbox fault detection. First, a feature relevance evaluation method is presented to filter intrinsic mode functions (IMFs) obtained through ensemble empirical mode decomposition (EEMD) for noise reduction. Second, feature indicators are constructed based on interpretable frequency information. Then, global and local features are evaluated respectively to realize fault detection and further detection of faults from the sun or planetary gears. Finally, the effectiveness of the proposed method is demonstrated through data obtained from the NCWP test bench.

Real-time and accurate prediction of remaining useful life (RUL) is important to safe operation and maintenance (O&M) planning of mechanical equipment. However, the uncertainty of online RUL prediction is difficult to predict with most current deep learning (DL)-based methods, making the prediction results difficult to convince. Furthermore, the offline-trained DL model is unable to adaptively update the network parameters online when acquiring new data, leading to a decrease in RUL prediction accuracy. To overcome these problems, an innovative approach based on the incremental contrast hybrid model is proposed for online RUL prediction with uncertainty quantification, which combines the contrastive learning transformer (CLformer) with the enhanced generalized Wiener process (EGWP) to describe trends in mechanical degradation. First, a CLformer is developed for online trend prediction, and an incremental contrastive learning strategy is designed for online adaptive updating of CLformer parameters to reduce prediction offset errors. Then, the degradation increments within the EGWP state-space equations are predicted online by the proposed CLformer network for online updating of EGWP parameters. Finally, online prediction of the machine RUL is provided by the CLformer, whereas the hybrid model provides the probability density function of RUL. The effectiveness of the proposed method is verified using two publicly available datasets and the journal-bearing dataset of the nuclear-circulating water pump. The results demonstrate the ability of the proposed method to dynamically update model parameters when new data are acquired online while giving the RUL prediction values and uncertainties.

Insufficient spatio-temporal feature extraction in normalizing flows (NFs) based anomaly detection (AD) method impedes their performance improvement. Moreover, it is worth noting that the multioperational nature of the process poses a challenge for most AD methods, including those based on NF, rendering them largely ineffective. Hence, this article introduces a new method called spatial-temporal graph conditionalized normalizing flows (STGNFs). First, multiscale dilation convolutional layers and mix-hopping graph convolutional layers are interleaved to form a spatio-temporal feature extractor. Second, spatio-temporal features are employed as conditional information for NF, while scheduling variables are factored in to adapt to operating conditions. Then, tracing the anomaly variables through the conditional density magnitude allows for interpretable AD results. Finally, experimental results on four datasets, including high-fidelity experimental bench data and real nuclear power plant data, demonstrate the performance of STGNF. STGNF enables the detection and precise localization of anomalies in various power modes, including nuclear plant shutdown and peaking, transcending the limitations of existing methods.

Remaining useful life (RUL) prediction is crucial for ensuring machine operating safety and reducing maintenance costs in nuclear power plants. Existing RUL prediction methods generally use run-to-failure data or known degradation mechanisms to establish a static model for degradation process characterization. However, the inherent degradation mechanisms of machines are time-varying, and a static model may only cover part of the degradation, resulting in an inaccurate RUL result. Hence, we propose an optimized online RUL prediction considering time-varying degradation mechanisms. The degradation model type (discrete variables) and boundary/initial conditions (continuous variables) are first set as the main variables affecting the approximation of the time-varying degradation mechanism. The RUL prediction is then formulated as a feedback-decision process through interacting with the anomaly data stream, in which variables are jointly optimized with reinforcement learning by minimizing the approximation error. Based on the degradation model established with optimized variables, the RUL can finally be deduced. The proposed method is validated by a run-to-failure dataset collected in a nuclear circulating water pump test bench.

Nuclear safety technology arouses the concern of nuclear power works when historical nuclear accidents have proven irreversible environmental damage and bodily injury. Significantly, nuclear evacuation technology is one of the critical parts to guarantee public lives, exacerbated by the spatial and temporal uncertainties associated with unfixed routes, radiation distribution, and crowdedness in traffic. At present, large-scale nuclear evacuation falls into three challenges: 1) round-trip, 2) unfixed routes, and 3) dynamic radiation field. This article proposes a multistage vehicle planning model for evacuation time and individual dose optimizations under real-world constraints such as vehicle limits, evacuee demand, and number of evacuation times. The critical contributions include three stages: 1) optimizing the number of vehicles and vehicle schemes for round-trip, guaranteeing the evacuation of all personnel efficiently; 2) the next pick-up point is selected to minimum distance and dose for unfixed routes; and 3) vehicle routing is conducted of emergency response, featuring a dynamic programming by nondominant sorting genetic algorithm II with minimum time and individual dose. Therefore, the proposed model can provide recommended schedules, consisting of the number of vehicles, associated arrival, departure trips, and the exposure dose. With the actual data of the nuclear power plant in China, the case demonstrates the model's efficiency by comparing it with conventional solutions.

Machinery remaining useful life (RUL) prediction has important guiding significance for prognostics and health management. In order to improve the prediction accuracy of the RUL prediction model under different working conditions, the transfer method on domain adaptation (DA) has achieved preliminary results. However, on the one hand, the existing DA methods mostly use a single vibration signal to predict RUL, resulting in low model robustness. On the other hand, DA methods force transfer without considering the degradation information specific to the target domain, resulting in negative transfer. To solve the above problems, a degradation prior assisted multisource information fusion domain adaptive method is proposed for cross-domain RUL prediction. In this method, the multisource information fusion is realized by introducing the convolution neural network with a multidimensional attention mechanism, and comprehensive degradation features are obtained. Then, the degradation prior information of the target domain is fused with the multisource degradation characteristics in a weak supervision way, so as to retain the unique degradation features of the target domain. Finally, the cross-domain RUL prediction is realized by improved long short-term memory neural network. The performance of the proposed method is verified by the commercial modular aero-propulsion system simulation dataset and nuclear circulating water pump bearing dataset. The results show that the proposed method has better accuracy and generalization ability than the existing methods.