Search Results (4,083)

In the current scenario of universal accessibility of data, organisations face highly complex challenges related to integrating and processing diverse sets of data in order to meet their analytical needs. This review paper analyses traditional and innovative methods used for data storage and integration, with particular focus on their implications for scalability, consistency, and interoperability within an analytical ecosystem. In particular, it contributes a cross-layer taxonomy linking integration mechanisms (schema matching, entity resolution, and semantic enrichment) to storage/query substrates (row/column stores, NoSQL, lakehouse, and federation), together with comparative tables and figures that synthesise trade-offs and performance/governance levers. Through schema mapping solutions addressing the challenges brought about by structural heterogeneity, storage architectures varying from traditional storage solutions all the way to cloud storage solutions, and ETL pipeline integration using federated query processors, the research provides specific attention for the application of metadata management, with a focus on semantic enrichment using ontologies and lineage management to enable end-to-end traceability and governance. It also covers performance hotspots and caching techniques, along with consistency trade-offs arising out of distributed systems. Empirical case studies from real applications in enterprise lakehouses, scientific exploration activities, and public governance applications serve to invoke this review. Following this work is the possibility of future directions in convergent analytical platforms with support for multiple workloads, along with metadata-centric orchestration with provisions for AI-based integration. Combining technological advancement with practical considerations results in an enabling resource for researchers and practitioners seeking the creation of fault-tolerant, reliable, and future-ready data infrastructure. This review is primarily aimed at researchers, system architects, and advanced practitioners who design and evaluate heterogeneous analytical platforms. It also offers value to graduate students by serving as a structured overview of contemporary methods, thereby bridging academic knowledge with industrial practice. Full article

This paper presents a radiation-hardened 4-bit flash analog-to-digital converter (ADC) implemented in a 22 nm fully depleted silicon-on-insulator (FD-SOI) process for high-reliability applications in radiation environments. To improve single-event upsets (SEU) tolerance, the design introduces a compact fault-tolerant logic scheme based on Dual Modular Redundancy (DMR), offering reliability comparable to Triple Modular Redundancy (TMR) while using two storage nodes instead of three, and a simple XOR-based check in place of a majority voter. A distributed sampling architecture mitigates SEU vulnerabilities in the input path, while thin-oxide devices are used in analog-critical circuits to enhance total ionizing dose (TID) resilience. Post-layout simulations demonstrate SEU detection within 200 ps and correction within ~600 ps. The ADC achieves an active area of 0.089 mm², power consumption below 30 µW, and provides a scalable solution for radiation-tolerant data acquisition in aerospace and other high-reliability systems. Full article

The global transition toward sustainable energy has significantly accelerated the deployment of solar power systems. Yet, the inherent variability of solar energy continues to present considerable challenges in ensuring its stable and efficient integration into modern power grids. As the demand for clean and dependable energy sources intensifies, the integration of artificial intelligence (AI) with solar systems, particularly those coupled with energy storage, has emerged as a promising and increasingly vital solution. It explores the practical applications of machine learning (ML), deep learning (DL), fuzzy logic, and emerging generative AI models, focusing on their roles in areas such as solar irradiance forecasting, energy management, fault detection, and overall operational optimisation. Alongside these advancements, the review also addresses persistent challenges, including data limitations, difficulties in model generalization, and the integration of AI in real-time control scenarios. We included peer-reviewed journal articles published between 2015 and 2025 that apply AI methods to PV + ESS, with empirical evaluation. We excluded studies lacking evaluation against baselines or those focusing solely on PV or ESS in isolation. We searched IEEE Xplore, Scopus, Web of Science, and Google Scholar up to 1 July 2025. Two reviewers independently screened titles/abstracts and full texts; disagreements were resolved via discussion. Risk of bias was assessed with a custom tool evaluating validation method, dataset partitioning, baseline comparison, overfitting risk, and reporting clarity. Results were synthesized narratively by grouping AI techniques (forecasting, MPPT/control, dispatch, data augmentation). We screened 412 records and included 67 studies published between 2018 and 2025, following a documented PRISMA process. The review revealed that AI-driven techniques significantly enhance performance in solar + battery energy storage system (BESS) applications. In solar irradiance and PV output forecasting, deep learning models in particular, long short-term memory (LSTM) and hybrid convolutional neural network–LSTM (CNN–LSTM) architectures repeatedly outperform conventional statistical methods, obtaining significantly lower Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and higher R-squared. Smarter energy dispatch and market-based storage decisions are made possible by reinforcement learning and deep reinforcement learning frameworks, which increase economic returns and lower curtailment risks. Furthermore, hybrid metaheuristic–AI optimisation improves control tuning and system sizing with increased efficiency and convergence. In conclusion, AI enables transformative gains in forecasting, dispatch, and optimisation for solar-BESSs. Future efforts should focus on explainable, robust AI models, standardized benchmark datasets, and real-world pilot deployments to ensure scalability, reliability, and stakeholder trust. Full article

Rolling element bearings are commonly used in rotating machines. Bearing fault detection and diagnosis play a critical role in machine operations to recognize bearing faults at their early stage and prevent machine performance degradation, improve operation quality, and reduce maintenance costs. Although many fault detection techniques are proposed in the literature for bearing condition monitoring, reliable bearing fault detection remains a challenging task in this research and development field. This study proposes an enhanced Teager–Kaiser (eTK) technique for bearing fault detection and diagnosis. Vibration signals are used for analysis. The eTK technique is novel in two aspects: Firstly, an empirical mode decomposition analysis is suggested to recognize representative intrinsic mode functions (IMFs) with different frequency components. Secondly, an eTK denoising filter is proposed to improve the signal-to-noise ratio of the selected IMF features. The analytical signal spectrum analysis is conducted to identify representative features for bearing fault detection. The effectiveness of the proposed eTK technique is verified by experimental tests corresponding to different bearing conditions. Full article

Judging from the current global exploration trend, ultra-deep layers have become the main battlefield for energy exploration. China has made great progress in the ultra-deep field in recent decades, with the Tarim Basin and Sichuan Basin as the focus of exploration. The Sichuan Basin is a large superimposed gas-bearing basin that has experienced multiple tectonic movements and has developed multiple sets of reservoir–caprock combinations vertically. Notably, the multi-stage platform margin belt-type reservoirs of the Sinian–Lower Paleozoic exhibit inherited and superimposed development. Source rocks from the Qiongzhusi, Doushantuo, and Maidiping formations are located in close proximity to reservoirs, creating a complex hydrocarbon supply system, resulting in vertical and lateral migration paths. The structural faults connect the source and reservoir, and the source–reservoir–caprock combination is complete, with huge exploration potential. At the same time, the ultra-deep carbonate rock structure in the basin is weakly deformed, the ancient closures are well preserved, and the ancient oil reservoirs are cracked into gas reservoirs in situ, with little loss, which is conducive to the large-scale accumulation of natural gas. Since the Nvji well produced 18,500 cubic meters of gas per day in 1979, the study of ultra-deep layers in the Sichuan Basin has begun. Subsequently, further achievements have been made in the Guanji, Jiulongshan, Longgang, Shuangyushi, Wutan and Penglai gas fields. Since 2000, two trillion cubic meters of exploration areas have been discovered, with huge exploration potential, which is an important area for increasing production by trillion cubic meters in the future. Faced with the ultra-deep high-temperature and high-pressure geological environment and the complex geological conditions formed by multi-stage superimposed tectonic movements, how do we understand the special geological environment of ultra-deep layers? What geological processes have the generation, migration and enrichment of ultra-deep hydrocarbons experienced? What are the laws of distribution of ultra-deep oil and gas reservoirs? Based on the major achievements and important discoveries made in ultra-deep oil and gas exploration in recent years, this paper discusses the formation and enrichment status of ultra-deep oil and gas reservoirs in the Sichuan Basin from the perspective of basin structure, source rocks, reservoirs, caprocks, closures and preservation conditions, and provides support for the optimization of favorable exploration areas in the future. Full article

In many industrial facilities, online monitoring systems have improved the reliability of key equipment, reducing the cost of operation and maintenance over recent decades. However, it often requires additional on-site inspection of target facilities due to limited information from installed sensors. To systematically automate such processes, an adaptive online fault diagnosis framework is required, which consecutively selects variables to measure and updates its inference with additional information at each measurement step. In this paper, adaptive online fault detection models—grafted composite decision trees—are proposed for such a framework. While conventional decision trees themselves can serve two required objectives of the framework, information from monitored variables can be less utilized because decision trees do not consider if required input variables are always monitored when the models are trained. On the other hand, the proposed grafted composite decision tree models are designed to fully utilize both monitored and robot-measured variables at any stage in a given measurement sequence by grafting two types of trees together: a prior-tree trained only with observed variables and sub-trees trained with robot-measurable variables. The proposed method was validated on a cooling water system in a nuclear power plant with multiple leak scenarios, in which improved measurement selection and increase in inference confidence in each measurement step are demonstrated. The performance comparison between the proposed models and the conventional decision tree model clearly illustrates how the acquired information is fully utilized for the best inference while providing the best choice of the next variable to measure, maximizing information gain at the same time. Full article

This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the rolling bearing are used as dual-channel data sources to extract multi-dimensional features from time and frequency domains. Then, cloud models are employed to build models for each feature under different conditions, utilizing three digital characteristic parameters to characterize the distribution and uncertainty of features under different operating conditions. Thus, the membership degree vectors of test samples from two channels can be calculated using reference models. Subsequently, D-S evidence theory is applied to fuse membership degree vectors of the two channels, effectively enhancing the robustness and accuracy of the diagnosis. Experiments are conducted on the rolling bearing fault dataset from Case Western Reserve University. Results demonstrate that the proposed method achieves an accuracy of 96.32% using evidence fusion of the drive-end and fan-end data, which is obviously higher than that seen in preliminary single-channel diagnosis. Meanwhile, the final results can give suggestions of the possibilities of anther, which is benefit for technicists seeking to investigate the actual situation. Full article

The difficulty in precisely extracting single-fault signatures from hydraulic pump composite faults, which stems from structural complexity and coupled multi-source vibrations, is tackled herein via a new diagnostic technique based on underdetermined blind source separation (UBSS). Utilizing sparse component analysis (SCA), the proposed method achieves blind source separation without relying on prior knowledge or multiple sensors. However, conventional SCA-based approaches are limited by their reliance on a predefined number of sources and their high sensitivity to noise. To overcome these limitations, an adaptive source number estimation strategy is proposed by integrating information–theoretic criteria into density peak clustering (DPC), enabling automatic source number determination with negligible additional computation. To facilitate this process, the short-time Fourier transform (STFT) is first employed to convert the vibration signals into the frequency domain. The resulting time–frequency points are then clustered using the integrated DPC–Bayesian Information Criterion (BIC) scheme, which jointly estimates both the number of sources and the mixing matrix. Finally, the original source signals are reconstructed through the minimum L1-norm optimization method. Simulation and experimental studies, including hydraulic pump composite fault experiments, verify that the proposed method can accurately separate mixed vibration signals and identify distinct fault components even under low signal-to-noise ratio (SNR) conditions. The results demonstrate the method’s superior separation accuracy, noise robustness, and adaptability compared with existing algorithms. Full article

This paper presents the application of the 8D method to a case study in the automotive industry, combined with the development of a pilot framework that integrates Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), and advanced IT tools. Unlike traditional 8D implementations, the proposed solution introduces several innovations: (i) AI-based complaint analysis with automated keyword detection and classification and localization of batches in the warehouses achieving a 100% accuracy rate in categorizing root causes; (ii) IoT-enabled non-invasive monitoring of production parameters for tests and logs, reducing fault rate to 0 for current case study; (iii) digital cataloging and storage of corrective and preventive actions, improving retrieval speed of past solutions by 300 times (from 300 min to less than 1 min); and (iv) automated reporting tools, cutting the report preparation time from hours to minutes. Through these contributions, the system enhances both responsiveness and prevention, leading to an observed 100% reduction in fault recurrence associated with current case study and an estimated 75% decrease in customer complaints regarding defects occurring throughout the production sector where the pilot was implemented. In addition to the practical improvements, this work extends the literature on digitalized quality management by demonstrating how the 8D methodology can be effectively re-engineered with Industry 4.0 components. Comparative analysis against the enterprise’s conventional 8D system and against recent alternative approaches confirms that our framework provides measurable benefits in the speed, accuracy, and sustainability of problem-solving processes. Full article

Numerous studies have utilized Petri nets for modeling, fault diagnosis, and deadlock control in manufacturing processes and systems. However, most are confined to specific subclasses of Petri nets, thus limiting their applicability. This paper extends the research scope by focusing on generalized Petri nets, analyzing system models from a novel perspective, and demonstrating the complete workflow from modeling to constraint establishment, controller integration, and deadlock avoidance. The methodology begins with an introduction to thread analysis, which involves identifying critical transitions within individual threads to locate resource cycles and minimal siphons corresponding to dead transitions, along with the presentation of a dedicated algorithm for this purpose. Next, optimal constraints are derived based on the thread analysis approach, with generalized patterns summarized for partially parameterized scenarios. Controllers are then incorporated into the system model according to the aforementioned constraints. Finally, derivative issues arising from controller integration are discussed, and pathways based on thread–circuit analysis are synthesized. The entire process described above is substantiated through illustrative case studies. Full article

In the early months of 2025, a significant seismic activity was recorded in the area between Santorini and Amorgos, raising concerns about the potential occurrence of a major earthquake and a possible tsunami. The objective of this study is to assess the earthquake-triggered tsunami hazard in the Santorini-Amorgos Tectonic Zone (SATZ) by simulating tsunami processes using the MOST (Method of Splitting Tsunami) numerical model, implemented through the ComMIT (Community Model Interface for Tsunamis). High-resolution bathymetry and topography were employed to model tsunami generation, propagation, and onshore inundation. A total of 60 simulations were conducted using a deterministic approach based on worst-case scenarios. The analysis considered six major active faults with two kinematic types, pure normal and oblique-slip, and assessed tsunami impact on five selected coastal study areas. The simulations results showed potential maximum run-up values of 4.1 m in Gialos (Ios), 2.7 m in Kamari (Santorini), 2.4 m in Perissa (Santorini), 1.5 m in Katapola (Amorgos), and 2.3 m in Chora (Astypalaea), in some cases affecting residential zones. Inundation flows also impacted the main ports of Gialos, Katapola, and Chora, highlighting the exposure of critical infrastructure. Although earthquake-triggered tsunamis represent a potential hazard in the SATZ, the results indicated that it is unlikely to cause a widespread disaster in the study areas. Full article

This study focuses on the middle Eocene lacustrine shales of the Lower Member 2 of the Liushagang Formation (L–LS2) in the Weixi’nan Depression of the Beibu Gulf Basin. Employing an integrated approach that combines core observation, thin-section analysis, Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and geochemical proxies, we systematically characterize the lithofacies, sedimentary processes, and paleoenvironmental evolution. Six distinct lithofacies were identified: clay-rich mudstone, calcium-bearing mudstone, clay-rich siltstone, siliceous siltstone, ankerite-bearing sandstone, and siliceous sandstone. Based on depositional processes and structural features, these are grouped into three lithofacies assemblages: interbedded lithofacies assemblage, laminated lithofacies assemblage, and matrix lithofacies assemblage. Vertical facies distribution shows that the interbedded lithofacies assemblage dominates the lower L–LS2, reflecting active faulting, volcanism, a low lake level, prevalent gravity flows, and episodic oxidative conditions. The laminated lithofacies assemblage dominates the middle section and results from the combined influence of chemical and mechanical deposition, indicating fluctuating climate conditions that affected water depth, salinity, and redox dynamics. The upper section is characterized by matrix lithofacies assemblage, representing a stable, deep water, anoxic environment with low energy suspension settling. We propose a depositional model in which tectonics and climate jointly control lacustrine shale deposition. During the middle Eocene, intensified tectonic activity expanded accommodation space and increased clastic input, while climate fluctuations influenced chemical weathering, nutrient supply, and salinity. Together, these factors drove lake deepening and variability, affecting sedimentary energy and redox conditions. This study not only clarifies the sedimentary evolution of L–LS2 but also provides a critical geological framework for lacustrine shale oil exploration. Full article

To address the problems of noise sensitivity, insufficient modeling of long-term time-series dependence, and high cost of labeled data in the fault diagnosis of pumping station units, an intelligent diagnosis method integrating the improved Sparrow Search Algorithm (tSSA) and Informer model is proposed in this study. Firstly, an adaptive t-distribution strategy is introduced into the Sparrow Search Algorithm to dynamically adjust the degree of freedom parameters of the mutation operator, balance global search and local development capabilities, avoid the algorithm converging to the origin, and enhance optimization accuracy, with time complexity consistent with the original SSA. Secondly, by combining the sparse self-attention and self-attention distillation mechanisms of Informer, the model’s ability to extract key features of long sequences is optimized, and its hyperparameters are adaptively optimized via tSSA. Experiments were conducted based on 12 types of fault vibration data acquired from pumping station units. Outliers were removed using the interquartile range (IQR) method, and dimensionality reduction was achieved through kernel principal component analysis (KPCA). The results indicate that the average diagnostic accuracy of tSSA-Informer under noise-free conditions reaches 98.73%, which is significantly higher than that of models such as SSA-Informer and GA-Informer; under noise interference of SNR = −1 dB, it still maintains an accuracy of 87.47%, outperforming comparative methods like 1D-DCTN; when the labeled sample size is reduced to 10%, its accuracy is 61.32%, which is more than 40% higher than that of traditional models. These results verify the robustness and practicality of the proposed method in strong-noise and small-sample scenarios. This study provides an efficient solution for the intelligent fault diagnosis of complex industrial equipment. Full article

To address the limitations of one-dimensional vibration signals in convolutional neural networks and the insufficient feature extraction capability of traditional single data processing methods under complex operating conditions, this paper proposes a novel fault diagnosis method that integrates dual-graph transformation and an improved residual network. Firstly, the one-dimensional vibration signals are converted into time–frequency representations using the short-time Fourier transform (STFT) and the synchrosqueezed wavelet transform (SWT). Subsequently, these dual-domain representations are fed in parallel into a customized parallel two-dimensional residual network (P2D-Sk-ResNet), which incorporates the selective kernel network (SKNet) mechanism into a ResNet architecture. This design enables adaptive multi-scale feature extraction. Finally, the features from the fully connected layer are classified using the extreme gradient boosting (XGBoost) algorithm to complete the fault diagnosis task. Comparative experiments demonstrate that the proposed STFT-SWT-P2D-Sk-ResNet-XGBoost achieves a diagnostic accuracy of 98.51% under constant load conditions, significantly outperforming several baseline models. Furthermore, the model exhibits superior generalization capability under varying load conditions and strong robustness in noisy environments. The proposed method provides a valuable and practical reference for intelligent fault diagnosis in industrial applications. Full article