Search Results (1,987)

Various methodologies have been developed over the years to assess model predictive control (MPC) performance. However, few have been applied in industry, and they remain limited in terms of providing a rapid indication of the root causes of deteriorated control performance. This article aims to review the existing methodologies in the literature that address these challenges. Additionally, it implements a structure in which MPC performance assessment is integrated within a fault detection and diagnosis (FDD) framework. The integrated approach employs cascaded modules of machine learning (ML) binary classifiers arranged in a sequence that mimics the decision-making logic of an operator. To illustrate the integrated strategy both conceptually and operationally, a van de Vusse reactor, controlled by a nonlinear model predictive controller (NMPC), is used as a case study. The ML models evaluated include XGBoost, Random Forest, Multilayer Perceptron, Long Short-Term Memory, and Gated Recurrent Unit. The results show that these models can correctly distinguish the cause of abnormalities, even in the presence of measurement noise, with a detection accuracy of 99% and an abnormality classification accuracy above 85% for the best-performing models. Different ML models performed best for distinct diagnostic tasks, highlighting the flexibility of arranging models according to their most suitable application. The investigation indicates that the proposed ML-based FDD framework, which embeds control performance assessment, is competitive for control-aware diagnosis of MPC-controlled processes. Full article

With the emergence of data-intensive computing, which is due to the growth of the data produced and generated each day, it became necessary to store and manage big data. Cloud data storage is actually the best choice for large distributed systems. Successful Cloud Computing cannot be achieved without a reliable data-management system to store and handle the enormous volume of data. Management of the available storage system at large scale becomes progressively more complicated, and we face many challenges, such as scalability, data availability, fault tolerance, etc. Also, data storage is faced with specific access patterns: highly concurrent reads of data from the same file, many overwrites, and very concurrent appends to the same file. Most of the existing storage systems use versioning to bring and enhance data access parallelism and this enables better performance levels under concurrency; but, generally, these systems use one component (version manager), which is responsible for generating new versions of each file stored. When we speak in the context of big data, the requests for read, write and append increase. If these requests are managed by a single component, then we have a performance bottleneck and an overloaded version manager. To avoid this drawback, we proposed and designed a new architecture of storage systems that uses versioning; the new architecture uses multi-version managers to support better the scalability and provide partial fault tolerance. To illustrate the practicability of our approach, we assessed it on the BlobSeer data-management system. The experimental results demonstrate that our architecture achieves near-linear scalability for CREATE operations (495 ops/sec per additional version manager), reduces WRITE execution time by up to 66%, and maintains 67% availability under single-node failures, all while introducing minimal resource overhead (3% aggregate CPU increase). These results confirm that the proposed multi-version manager architecture offers a practical, scalable, and partially fault-tolerant solution for Cloud data-storage systems. Full article

Rolling-element bearings remain a leading cause of unplanned downtime in industrial machinery, while vibration-based condition monitoring has matured, the post-2018 literature has converged on machine-learning classifiers whose interpretability layer remains restricted to expert analysts. This paper presents an operator-centred visualization framework supported by two time-frequency methods: the Zhao–Atlas–Marks Distribution (ZAMD), a Cohen’s-class representation with a cross-term-suppressing cone kernel, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), evaluated through its Hilbert spectral analysis output. Both methods produce two-dimensional time-frequency artefacts with a similar visual structure—impact-related energy bursts that recur at the characteristic fault frequencies—and are presented in side-by-side form for each fault class. A four-stage framework wraps either method with the characteristic fault frequencies (supplied as a comparison reference) and colour-coded, healthy baseline-referenced scaling. The framework is demonstrated on a laboratory bearing rig (KOYO 6302, 600 RPM) across inner-race, outer-race, and ball-spin fault classes. A preliminary readability assessment of annotated ZAMD-generated artefacts, with twelve non-specialist participants from a brewing and packaging industrial context, recorded 89.8% aggregate classification accuracy (194 of 216 trials) at a mean response time of 15.4 s. Because no label-free or alternative-format control conditions were included, this result characterises the annotated artefact as a whole and does not isolate the contribution of the time-frequency representation from that of the annotation layer; it is established for the ZAMD engine only. The two methods are compared as visualization engines—qualitatively, through the structure of their side-by-side time-frequency artefacts, and quantitatively, through computational cost—whereas the non-specialist readability assessment characterises the ZAMD-based framework specifically. CEEMDAN is positioned as a candidate alternative engine whose time-frequency output is shown to be structurally similar but whose operator readability has not been tested with human participants and is identified as future work. Full article

Battery pack fires remain a critical safety concern for lithium-ion battery systems. This study presents a comprehensive application of Fault Tree Analysis (FTA) to identify and structure the sequences of failures that may lead to a battery pack fire. A detailed fault tree is developed for a cell–module–pack architecture equipped with a thermal management system, enabling a clear representation of failure pathways. The analysis highlights four main origins of battery pack fire. Each intermediate scenario is described through dedicated branches of the fault tree to enhance clarity and facilitate its adoption for other battery pack designs and use-cases. As most failure modes involved in battery pack fire do not have reliable probability data available or exhibit strong dependency on usage conditions, a fuzzy logic-based expert approach is employed. Probabilistic data are collected through a questionnaire, allowing the assignment of probabilities to undocumented failure events. A quantified use-case is presented for an electric vehicle, illustrating the practical application of the methodology. The objective of this work is to demonstrate a structured and adaptable methodology for applying FTA to lithium-ion battery pack fire risk analysis. The resulting fault tree, provided as open-access supplementary material, aims to support safety analysis, highlight critical protection failures, and identify current limitations in battery pack safety systems. It can also help identify critical components in order to support the development of rapid and targeted diagnostic strategies for battery packs throughout their lifetime. Full article

The integration of data centers (DCs) into distribution systems introduces new challenges for reliability assessment. It also provides operational flexibility through the spatial migration of computational workloads. However, existing reliability assessment methods have not sufficiently captured the joint effects of DC workload flexibility, post-fault network reconfiguration, and multiple heterogeneous faults. To address this gap, this paper proposes an optimization model-based reliability assessment framework for distribution systems with integrated DCs. A DC workload dispatch model is first developed by considering server operation, cooling demand, backup requirements, and inter-DC workload transfer limits. The post-fault operation process is then formulated as a two-stage optimization problem, including fault isolation and service restoration. The proposed framework co-optimizes network reconfiguration and DC workload migration under line faults, node faults, and remote-controlled switch failures. The resulting problem is reformulated as a mixed-integer linear programming model and solved using commercial solvers. Case studies on a modified IEEE 123-node test feeder show that DC workload migration can significantly reduce expected energy not supplied by exploiting the spatial flexibility of interconnected DCs. The results also demonstrate that neglecting node faults and switch failures may lead to overly optimistic reliability estimates. Additional tests on a modified 356-node regional distribution system further verify the applicability and scalability of the proposed framework. The proposed method provides a physically interpretable reliability assessment tool for active distribution systems with DC integration and heterogeneous fault mechanisms. Full article

Accurate monitoring of internal winding temperature is essential for assessing the thermal state and operational reliability of oil-immersed transformers. However, direct deployment of distributed temperature sensors inside transformer windings is difficult because of insulation constraints, structural complexity, and potential reliability risks. To address this problem, this paper proposes a non-invasive internal winding temperature estimation method based on surface temperature sensing and a hybrid deep learning model. In the proposed framework, external surface temperature measurements are used as sensor inputs to infer the internal transient thermal state of the transformer. First, an extreme gradient boosting (XGBoost) model is employed to evaluate the contribution of different surface temperature measurement points and select the sensing locations that are most strongly correlated with internal winding temperature variations. Then, the selected surface temperature time-series data are used to train a Long Short-Term Memory (LSTM) network, which captures the temporal evolution of the transformer temperature field under different operating conditions. The proposed method is verified through both numerical simulation and experimental testing on a scaled single-phase oil-immersed converter transformer model (D-800/35) developed in this study. The results show that the proposed XGBoost-LSTM model can estimate internal winding temperature with an error of less than 1.5 K. Compared with direct internal sensing, the proposed method provides a non-invasive and sensor-efficient solution for internal temperature monitoring. The results demonstrate its potential for real-time thermal state estimation, condition monitoring, and fault diagnosis of oil-immersed converter transformers. Full article

Existing reliability assessment methods for distribution networks insufficiently characterize the impacts of faults in key components other than branches, and they often suffer from high computational complexity and repeated searches when identifying fault-affected areas. To address these issues, this paper proposes an analytical method for calculating reliability indices in distribution networks based on extended component fault modeling. First, the fault modeling objects in distribution networks are extended to multiple types of key components, including branches, sectionalizing switches, ring main units, and distribution transformers. Second, a spatial correlation matrix is constructed to describe the topology of the distribution system, source-to-load power supply paths, and the positional relationships of switching components. On this basis, an extended component fault impact correlation matrix is derived to characterize the spatial correlations between different types of component faults and the affected load nodes. Finally, through a single algebraic operation between the extended component fault–impact correlation matrix and the reliability parameter vector, explicit analytical expressions of both load-level and system-level reliability indices are obtained, thereby avoiding the repeated iterative searches required in the fault enumeration process of existing reliability assessment methods. Case study results show that the proposed method improves the evaluation efficiency by one order of magnitude while maintaining result accuracy. It can also explicitly quantify the impacts of faults in branches, sectionalizing switches, ring main units, and distribution transformers on system reliability, thereby more accurately reflecting the actual level of secure and reliable operation of the distribution network. This method can provide theoretical support and a methodological basis for reliability assessment and planning of complex distribution systems. Full article

Shapelet-based classifiers offer structural interpretability: discriminative subsequences form an inspectable vibration-pattern vocabulary and a shallow decision-tree ensemble produces traceable fault-type predictions. We impose explicit interpretability constraints on the model and apply Bayesian optimisation within this bounded region; the primary experimental question is whether these constraints carry a performance penalty relative to an unconstrained baseline. Evaluation uses recording-level cross-validation on CWRU (Case Western Reserve University) and MFPT (Machinery Failure Prevention Technology) bearing datasets with Hilbert envelope demodulation. The central finding is that the constraints impose no systematic performance penalty: the shapelet classifier matches ROCKET, a non-interpretable baseline, on both datasets, with cross-validated mean F1 differences smaller than the fold-to-fold standard deviation. To further characterise the selected models under the controlled laboratory conditions studied here, we assess probability calibration and conformal prediction coverage as secondary analyses. Raw probability estimates are well-calibrated, but Platt scaling degrades under cross-severity distribution shift; split conformal prediction yields valid coverage on CWRU but fails on MFPT due to class-proportion mismatch across recording-level splits. Together, these results show that structural constraints supporting interpretability are compatible with competitive performance, and identify the conditions under which reliability tools succeed and fail in this setting. Full article

In response to the challenges encountered in high-throughput resilience assessment of zonal ship central cooling systems, including numerical stiffness in physics-based dynamic models, abnormal solver termination, and insufficient continuity in batch simulation campaigns, a physics-informed co-simulation framework for resilience-oriented assessment is proposed. With control–physics orthogonal decoupling as its core, the framework separates the control-scheduling layer from the thermo-hydraulic solver at the software-execution level, while retaining information exchange through standardized interfaces. In addition, physics constraint-based pre-filtering, process-level fault isolation, and automatic recovery mechanisms are integrated to improve the robustness and continuity of automated batch assessment. A hierarchical reduced-order thermo-hydraulic model of the zonal ship central cooling system is established. Subsequently, the numerical stiffness characteristics of the fluid network and heat-transfer units under valve topology switching conditions are analyzed. A standalone C++ solver kernel is generated from a Simulink prototype model, and a Java/Web-based collaborative scheduling platform is constructed. Cross-environment consistency tests show that the C++ solver reproduces the Simulink prototype results under representative fast hydraulic and slow thermal scenarios, with steady-state and transient discrepancies below 0.05% and 1.08%, respectively. Physics constraint-based pre-filtering intercepted 42.6% of infeasible samples and reduced the total wall-clock runtime of the tested optimization task by approximately 38%. In 1000 fault-injection tests, the process-isolation mechanism isolated 12 abnormal solver terminations, while the main scheduling process remained alive and the remaining batch tasks were completed under the tested conditions. Finally, an abrupt pulse thermal-load increase in the forward zone was used as a representative scenario to demonstrate automatic extraction of temperature trajectories and quantitative evaluation using the cumulative temperature-exceedance severity (CTS) index. The results indicate that the proposed framework can support offline resilience-oriented assessment, reconfiguration-strategy screening, and batch evaluation of shipboard fluid–thermal systems under the tested conditions. Full article

The Upper Yellow River Basin (UYRB) is one of the most tectonically complex and seismically active regions in China, but the detailed crustal deformation and interseismic fault couplings, providing the essential parameters for geodynamics and seismic hazard analysis, are still unclear in this region. We thus adopt Sentinel-1 Synthetic Aperture Radar images to form frame-based line-of-sight velocity maps, and then derive a high-resolution surface deformation map around the UYRB. Slip rates and coupling states are further inverted for some active yet less-investigated faults. For instance, we estimate a right-lateral strike-slip motion of ~2.3–3.5 mm/yr along the Riyueshan Fault, and a thrust rate of ~2.0–3.5 mm/yr across the Lajishan Fault. Finally, the seismic moment budgets and the potential magnitudes are calculated based on the fault slip deficits and historical earthquakes. The accumulated moment deficit could produce earthquakes of M_W ≥ 6.0 in most active faults, and up to M_W ≥ 7.0 along the Dongdatan-Xidatan and Maqin-Maqu segments of the East Kunlun Fault and the Jinqianghe segment of the Haiyuan Fault. Our inverted slip rates, interseismic coupling states, and potential seismic moment on the active faults provide a basis for understanding kinematic processes and assessing seismic hazards within the UYRB. Full article

The fracture–cave carbonate reservoir in Unit S91 of the Tahe Oilfield is jointly controlled by strike-slip fault activity, karstification, and later-stage fracture development, resulting in reservoir spaces characterized by strong heterogeneity, strong discreteness, and multi-scale superimposition. The inter-well connectivity of this type of reservoir is not governed by the size of a single fracture–cave body or local fracture density, but rather by the spatial configuration among the main controlling fault, the associated fracture network, and the fracture–cave reservoir bodies. As the reservoir enters the middle–high-water-cut development stage, the production differential between dominant connecting channels and weakly connected fracture–cave bodies further enlarges, leading to marked heterogeneity in the remaining oil distribution. Integrating post-stack seismic data, fracture prediction, RGB attribute fusion, production performance, and numerical simulation data, this paper constructs a main fault-controlled fracture–vug coupling probability (MFC-FVCP) model under the constraint of the main controlling fault. Unlike conventional multi-attribute fusion methods that mainly enhance seismic anomaly visualization, the MFC-FVCP model transforms the main fault constraint, fracture connectivity, and fracture–cave reservoir-body effectiveness into a unified coupling probability. The model uses three core components—the main fault response field, the fracture attribute response field, and the fracture–cave reservoir body response field—to characterize the fault-control effect, fracture-network continuity, and effective reservoir-body response, respectively. By evaluating the coupling probability, the inter-well connectivity potential is assessed, the dominant connectivity areas where fractures and fracture–cave bodies synergistically develop under the constraint of the main controlling fault are identified, and potential remaining oil targets are clarified. The predicted connectivity pattern was further constrained by production performance, nitrogen injection response, and staged oil saturation simulation, which improves the reliability of remaining oil enrichment prediction. The results show that the T74 layer is the dominant development interval of fracture–cave reservoir bodies in Unit S91. These fracture–cave bodies are mainly distributed along the main controlling fault and associated fracture zones in beaded, chain-like, and banded patterns, exhibiting distinct fault-and-fracture control characteristics. Potential point A near well TK858XCH features both good reservoir physical properties and insufficient sweep efficiency, making it a key target for subsequent injection–production adjustment and remaining oil tapping. The MFC-FVCP model can incorporate static seismic responses, fracture–cave spatial structures, and dynamic development responses into a unified evaluation framework, providing a quantitative basis for characterizing inter-well connectivity and identifying remaining oil enrichment areas in fracture–cave carbonate reservoirs. Full article

Near-fault ground motions influenced by rupture directivity impose seismic demands that differ fundamentally from those associated with conventional far-field earthquakes, particularly in terms of displacement-controlled response. This study presents a performance-based seismic assessment of existing reinforced concrete (RC) buildings subjected to near-fault ground motions in the Sivrice–Pütürge segment of the Malatya–Ovacık Fault Zone, Eastern Anatolia. A probabilistic seismic hazard analysis (PSHA) was performed using NGA-West2 ground-motion prediction equations together with a regionally defined fault model, and the resulting hazard was evaluated within the framework of the Turkish Building Earthquake Code (TBEC-2018). Code-compatible earthquake records were selected and scaled for the DD-2 design earthquake level, while rupture directivity was represented using a literature-based median amplification factor. Nonlinear time-history analyses were subsequently carried out for three existing RC buildings representing low-, mid-, and high-rise structural typologies. Structural performance was evaluated in terms of roof displacement, interstory drift ratio, base shear, and element-level damage states. The maximum roof displacements reached 0.070 m, 0.107 m, and 0.138 m for the low-, mid-, and high-rise buildings, respectively, corresponding to maximum drift ratios of 0.60%, 0.46%, and 0.34%. The results indicate that rupture directivity has only a limited influence on base shear demand but substantially increases displacement-related response quantities and promotes a redistribution of structural damage from predominantly beam-controlled behavior toward increased participation of columns and shear walls, particularly in medium- and high-rise buildings. These findings demonstrate that conventional code-based assessment procedures may underestimate deformation demands in fault-proximal regions and highlight the importance of explicitly considering rupture directivity in the seismic performance assessment of existing reinforced concrete buildings. Full article

Liquefied natural gas (LNG) submersible centrifugal pumps are critical components in marine energy transportation systems, and fault-induced degradation may threaten operational safety, transfer reliability, and maintenance efficiency. However, condition monitoring and fault diagnosis often rely on heterogeneous multi-source data, where redundant information, unequal channel sensitivity, and inter-signal coupling may obscure discriminative fault features. To address this challenge, this paper proposes a hierarchical multi-source condition monitoring and fault diagnosis framework for LNG submersible centrifugal pumps by integrating an Entropy-Weighted Sensor Selection Method (EWSSM) with a hybrid convolutional neural network (CNN)–Transformer model. Functional information is used for front-end abnormality screening, while selected response signals are used for fault category recognition. EWSSM evaluates channel contribution and suppresses redundant inputs to construct a compact fault-sensitive input space. The CNN–Transformer model combines local feature extraction with global dependency modeling to identify complex fault patterns. A laboratory-scale fault simulation platform was established, and vibration, acoustic, internal pressure-pulsation-related response information, and operating parameter data were collected under ten operating states. Experimental results show that the proposed framework achieves effective abnormality screening and accurate fault diagnosis, with an average classification accuracy of 98.73% over repeated experiments. Covariance-difference analysis further provides interpretable evidence for condition assessment by revealing fault-related multi-source response redistribution. The proposed framework provides an effective, intelligent monitoring and diagnosis solution for LNG submersible centrifugal pumps and supports reliability-oriented operation and maintenance of marine energy transportation equipment. Full article

Landslides are among the most destructive geological hazards, posing significant threats to human life, infrastructure, and ecological environments. In this research, to improve the accuracy and reliability of landslide susceptibility assessment, Guangdong Province was selected as the study area, and a multi-source environmental factor dataset incorporating topographic, geological, hydrological, climatic, vegetation, and anthropogenic factors was constructed. Geological factors, including fault distance and seismic point distance, were introduced to characterize the influence of tectonic activities on slope instability. A landslide inventory and a non-landslide sample dataset were established for model training and validation. The Extreme Gradient Boosting (XGBoost) model was employed for landslide susceptibility mapping, and SHapley Additive exPlanations (SHAP) analysis was used to interpret the contribution of different conditioning factors. The results showed that the model achieved an area under the receiver operating characteristic curve (AUC) of 0.8335 on the independent test dataset and a mean AUC of 0.8457 ± 0.0219 for a five-fold stratified cross-validation. The high-susceptibility areas were primarily distributed in the mountainous and hilly regions of northern and eastern Guangdong Province. Vegetation-related variables, road proximity, land-cover type, slope, and distance to coal mines were identified as important contributors to landslide occurrence. This study provides useful references for geological hazard prevention, risk management, and sustainable regional planning. Full article

The Yili River Valley in Xinjiang is characterized by complex geological structures and frequent geological hazards, which seriously threaten local lives, property, and infrastructure. Improving the accuracy and interpretability of geological hazard assessment is therefore of great significance. To address this, nine factors, including elevation, distance from fault, and slope, were selected to construct a WOE-BPNN-SHAP coupled model. The weights of evidence (WOE) method was first used for factor correlation testing and to optimize the input of the BP neural network. The evaluation accuracies of WOE, WOE-DNN, and WOE-BP models were then compared, and the SHAP model was introduced to analyze the coupling relationships among factors. Results show that the WOE-BP model achieves the best predictive performance, with an AUC of 83.65%. Areas of extremely high-risk account for 8.63% of the study area, while higher-risk areas account for 15.39%. Elevation (1688–2847 m), distance from fault (<3000 m), precipitation (192.6–290.8 mm), and slope (>16°) are identified as the main driving factors. This coupled method provides a new technical approach for regional geological hazard assessment and offers a theoretical basis for disaster prevention, mitigation, and resilience building in the Yili River Valley. Full article