Search Results (227)

Transitioning predictive maintenance from expensive, high-frequency piezoelectric sensors to affordable, edge-deployed MEMS sensors poses a significant challenge in industrial tool condition monitoring (TCM). Both technologies differ in signal quality, frequency capability, robustness, and reliability, which would affect how accurately machine faults can be detected. This work presents a systematic framework to bridge this gap, enabling real-time tool wear prediction and cross-sensor transferability. The methodology employs unsupervised Wavelet Packet Decomposition (WPD) and dynamic programming on high-resolution vibration signals to establish ground-truth wear phases: initial, steady-state, and accelerated. Multi-resolution time-frequency features are extracted and globally ranked using a multi-metric scoring system. A multi-task Bidirectional Long Short-Term Memory (Bi-LSTM) network is then trained to simultaneously predict a continuous wear index and classify discrete wear zones. To ensure model portability, Canonical Correlation Analysis (CCA) is utilised to align the high-fidelity piezoelectric feature space with the lower-frequency MEMS domain. The optimised multi-task Bi-LSTM architecture achieved up to 97.9% zone classification accuracy and a mean absolute error of 0.042 for wear index regression. Furthermore, CCA-based domain adaptation successfully transferred a model trained on piezoelectric data to classify unseen low-cost MEMS sensor data, maintaining a robust 87 % accuracy. Combining optimised WPD features with CCA effectively overcomes hardware and sampling rate discrepancies, proving the viability of using low-cost sensors for reliable industrial retrofitting and real-time degradation tracking. Full article

Series arc faults on the DC side of photovoltaic (PV) systems are a critical hazard that can trigger system fires. Conventional contact-based detection methods suffer from cumbersome installation and high retrofit cost, whereas existing non-contact approaches mostly rely on megahertz-level high-frequency sampling and therefore require expensive radio-frequency instrumentation or high-performance computing platforms. As a result, it remains difficult to simultaneously achieve strong interference immunity and real-time performance on low-cost embedded devices with limited resources. To address this engineering paradox between high-frequency sampling and constrained computational capability, this paper proposes a fully embedded, non-contact arc fault detection system based on a 12–80 kHz low-frequency sub-band selection strategy. By exploiting the physical characteristic of broadband energy elevation induced by arc faults, the proposed strategy avoids dependence on high-bandwidth hardware. Guided by this strategy, a Moebius-topology coaxial shielded loop antenna is employed as the near-field sensor, while an ultra-simplified passive analog front end is constructed directly by using the on-chip programmable gain amplifier and analog-to-digital converter of the microcontroller unit, enabling efficient signal acquisition and fast Fourier transform processing within the target sub-band. To cope with complex background noise in the low-frequency range, an environment-adaptive baseline mechanism based on exponential moving average and exponential absolute deviation is developed for dynamic decoupling. In addition, a lightweight INT8-quantized multilayer perceptron is introduced as a nonlinear auxiliary module, thereby forming a robust hybrid decision architecture with complementary rule-based and artificial intelligence components. Experimental results show that, under the tested household, laboratory, and PV-site conditions, the proposed system achieved an overall detection rate of 97%, while the remaining 3% mainly corresponded to failed ignition or non-sustained arc attempts rather than persistent false triggering during normal monitoring. Full article

Energy efficiency retrofits are widely promoted for public buildings, yet evidence from large-scale real-world projects remains limited compared with simulation-based assessments. This study leverages measured pre- and post-retrofit operational data from 530 public building retrofit projects across 11 provinces/municipalities in China to quantify realized energy-saving performance and screening-level cost-effectiveness across building types and climate zones. Wilcoxon and Kruskal–Wallis tests were employed to ensure statistical rigor. Retrofit measures were grouped into seven categories (e.g., HVAC, lighting, envelope, monitoring/management), and a median-based four-quadrant framework was employed to characterize investment–savings profiles by climate zone and building function. Across the full sample, mean energy use intensity decreased by 19.1%, with 99.2% of projects achieving positive savings. Savings varied markedly by building type: commercial and hotels achieved the highest savings intensities (26.5–28.0 kWh/(m²·a)), while education and cultural buildings generally showed lower gains, with some projects having < 10 kWh/(m²·a). Technology performance exhibited distinct climate and building suitability. Envelope retrofits were most effective in the Cold and Hot Summer–Cold Winter zones (13.30–22.06 kWh/(m²·a)) but yielded limited benefits in the Hot Summer–Warm Winter zone (~1.73 kWh/(m²·a)). HVAC and lighting upgrades delivered comparatively stable savings across climates and building types and dominated retrofit portfolios. Based on these findings, we propose a tiered strategy: prioritizing HVAC and envelope upgrades for high-load sectors while focusing on low-cost optimizations for educational facilities to mitigate investment risks. The findings provide large-scale empirical evidence to support climate- and building-specific retrofit prioritization and investment decision-making under real-world operating conditions. Full article

The escalating financial burden of deteriorating civil infrastructure worldwide necessitates a shift from conventional repair methods towards more durable and economically efficient long-term solutions. This paper presents a comprehensive techno-economic review of using carbon fibre-reinforced polymer (CFRP) fabrics for structural strengthening. Moving beyond a simple first-cost comparison, this review utilizes a life-cycle cost analysis (LCCA) framework to evaluate the total cost of ownership. The analysis deconstructs the complete cost profile, demonstrating that while CFRP systems have a high initial material cost, this is frequently offset by substantial savings in labour, equipment, and, critically, the indirect costs associated with reduced construction time and operational disruption. Furthermore, the inherent corrosion immunity of CFRP virtually eliminates future maintenance and repair expenditures, leading to a lower total life-cycle cost compared to traditional steel or concrete-based methods in a wide range of applications. Specifically, the conducted LCCA case study demonstrates that the CFRP alternative can reduce total life-cycle costs by nearly 25% relative to conventional steel sheet bonding, overwhelmingly driven by minimized operational downtime and related indirect costs. The value proposition is shown to be context-dependent, driven by minimizing user delay costs in bridges, mitigating catastrophic risk in seismic retrofitting, preserving cultural value in heritage structures, and maximizing revenue uptime in industrial facilities. The review also examines market dynamics, including the roles of standardization and government policy in driving adoption, and explores future trends such as inorganic matrix composites (TRM/FRCM), integrated structural health monitoring (SHM), and the push towards a circular economy. The findings conclude that a holistic, life-cycle-based economic assessment establishes CFRP strengthening as a cornerstone technology for the sustainable and resilient management of modern civil infrastructure. Full article

An automated air-conditioner cleaning system was developed as a retrofit solution for conventional split-type units to reduce residual moisture in the evaporator section and suppress post-shutdown microbial accumulation. The system was integrated with an 18,000 BTU h⁻¹ air-conditioner and implemented using an Arduino-based closed-loop control platform with temperature and relative humidity monitoring. After shutdown, the indoor fan was operated under low-, medium-, or high-speed conditions to remove retained moisture from the cooling coil. System performance was evaluated in an 18 m³ test room through measurements of electrical consumption, operating cost, relative humidity, and microbial contamination in room air and on the evaporator coil before and after system installation. Low-speed operation showed the lowest current demand, power consumption, and electricity cost, with corresponding values of 0.36 ± 0.01 A, 79.2 ± 0.8 W, and 0.47 THB per 150 min. Post-shutdown humidity reduction was achieved under all tested conditions, while the high-speed mode provided the fastest drying response, reducing relative humidity to approximately 60% within 120 min. In the room air, the greatest reduction in airborne fungi after shutdown was observed at low speed, whereas the greatest reduction in airborne bacteria was observed at medium speed. On the evaporator coil, the strongest bacterial suppression was obtained at low speed, where the bacterial count after 24 h decreased from 633.33 ± 34.27 CFUs before installation to below the detection limit after installation. These results indicate that the proposed system reduced moisture retention and microbial contamination with minimal energy consumption. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

Artificial Intelligence of Things (AIoT) technologies shifted the structure of production systems, enabling the development of more intelligent, connected and sustainable manufacturing environments. However, some industrial sectors, such as aerospace manufacturing industry, fell behind in the adoption of these new technologies, mainly because of the high safety standards, strict reliability requirements and long lifespan of aircraft components. Due to low production volumes and complex manufacturing processes, this sector relies heavily on weakly automated legacy machines and production systems. This article proposes a methodology to ease the integration of AIoT technologies for retrofitting legacy industrial equipment in the aeronautical domain in order to achieve the requirements of modern industrial production systems, enabling the development of more flexible, efficient and interconnected manufacturing environments. The proposed methodology is validated through a case study where the Smart Retrofitting of a legacy aeronautical industrial machine is carried out. The case study focuses on the development of an AIoT-based architecture to implement a predictive maintenance system through vibration and infrared thermography monitoring. A three layer architecture is proposed based on Edge/Fog/Cloud Computing paradigms. A hybrid communication architecture is used, combining wired technologies for critical real-time control tasks and wireless technologies for enhanced flexibility and scalability. The results demonstrate the viability of the proposed methodology for retrofitting legacy aircraft manufacturing systems. Full article

Classifying which operation a Computer Numerical Control (CNC) machine is executing, not just detecting whether it is functioning correctly, is a monitoring challenge that existing sensor-based studies rarely address. Unlike tool wear estimation, operation-type classification must resolve toolpath strategies and cutting conditions within heterogeneous, noisy sensor streams in which modalities differ widely in their discriminative value. Which sensors are genuinely necessary, and how many can be removed before performance degrades, directly informs retrofit cost and monitoring system design. We present a systematic cross-validated ablation study for a nine-class CNC toolpath and condition classification task, using 120 operation files collected from a desktop CNC mill instrumented with six distributed sensor units spanning inertial, acoustic, environmental, and electrical modalities. To handle multimodal fusion under sensor noise, we introduce the Multimodal Denoising Temporal Attention Encoder with Long Short-Term Memory (MM-DTAE-LSTM), which combines learned modality weighting, cross-modal attention, and a self-supervised denoising objective, followed by recurrent temporal modeling for classification. We evaluate MM-DTAE-LSTM against five baseline model families across five cumulative sensor-ablation levels and ten temporal resolutions, using file-level cross-validation to prevent data leakage from overlapping windows. MM-DTAE-LSTM maintains 92.5% classification accuracy when nearly half the sensor channels are removed (56 of 110 features), whereas simpler baselines degrade by up to 10.7 percentage points under the same reduction. Analysis of variance reveals that pressure channels encode session-level atmospheric variation rather than machining dynamics, exposing how models that cannot suppress uninformative modalities rely on environmental confounds rather than machining physics. Together, these findings translate into concrete sensor-selection and deployment recommendations for cost-effective CNC process monitoring at under USD 500 in hardware, though generalization to industrial machines, diverse materials, and production environments requires further validation. Full article

The integration of vertical greenery systems (VGSs) into existing reinforced concrete (RC) buildings raises questions regarding interface kinematics and the permanent displacement of soil-retaining elements under seismic excitation. This study experimentally investigates the residual displacement of façade-mounted living walls and rooftop planter pods anchored to a deficient RC frame under shake table excitation. A 1:3 scale reinforced concrete frame was tested in two distinct phases: initially as a deficient, unretrofitted structure (Phase A), and subsequently as a retrofitted system integrated with vertical greenery elements (Phase B). High-precision terrestrial laser scanning (TLS) was employed before and after successive seismic excitation stages to generate dense three-dimensional point clouds. Cloud-to-cloud comparison techniques were used to quantify global structural displacement and local kinematic behavior of greenery components, while results were validated against conventional displacement sensors. The RC frame exhibited millimeter-scale permanent displacements consistent with draw-wire measurements. In contrast, planter pods demonstrated configuration-dependent behavior, including up to 8 cm translational sliding and rotational responses reaching 13° under repeated excitation, whereas living wall panels remained stable. Notably, a 95% reduction in point cloud density reproduced global deformation patterns with an RMSE of 3.03 mm and quantified peak displacements with only ~2% deviation from full-resolution results. The findings demonstrate the capability of TLS-based monitoring to detect differential kinematic behavior of integrated VGSs, while highlighting the variability in performance of friction-based rooftop anchorage utilizing different robust planter pod fixing systems. Full article

Seismic–volcanic monitoring networks often operate in remote areas over heterogeneous links (e.g., microwave radio and cellular). During event-driven seismic episodes, sustained multi-station waveform streams can stress both last-mile connectivity and data acquisition systems, yielding discontinuities in center-side archives even when stations keep recording locally. This paper presents the Python Recovery Tool (PRT), a lightweight command-line artifact that retrieves buffered waveform files after reconnection and rebuilds daily archives that can be ingested by the monitoring center without hardware upgrades. PRT detects archive gaps from daily (Julian day) file partitions and embedded timestamps, and reduces recovery traffic by selectively fetching only the files needed to backfill missing intervals. We evaluated PRT on five event-driven recovery cases using operational file-based evidence from station and center listings complemented with a simple bandwidth-based recovery-time model. Across the cases, PRT restored archive continuity while reducing download volume by 4.43–93.75% relative to naive bulk retrieval, with modeled catch-up times ranging from 0.79 to 207.59 min, depending on station-side packaging granularity and bottleneck link capacity. These results support a practical retrofit path to improve archive completeness under constrained links and heterogeneous deployments. Full article

Soft-ground-story configurations in high-rise buildings present a critical vulnerability during seismic events, often leading to disproportionate structural damage and collapse. This study focuses on the systematic identification of soft-ground-story high-rise structures in Bucharest, a city located in a high seismic hazard zone influenced by Vrancea intermediate-depth earthquakes. The research employs a multi-step methodology combining field surveys, structural documentation, and analysis of architectural layouts from various sources to detect soft-ground-story irregularities across the urban building stock in Bucharest. The findings reveal that such configurations remain prevalent in mixed-use structures along major boulevards, where open ground floors were historically favoured for commercial purposes. The results provide a database of soft-ground-story high-rise buildings in Bucharest, highlighting their prevalence in distinct urban districts and their potential impact on seismic risk. Quantitative screening indicators, vertical element area ratio and mean axial stress in ground-story columns, are proposed for rapid vulnerability assessment. Dynamic measurements confirm a 33–38% increase in fundamental eigenperiods after the 1977 earthquake, indicating moderate-to-extensive damage states. These findings underscore the urgent need for targeted retrofitting strategies and inform seismic risk mitigation policies. The study provides a foundation for future integration of advanced diagnostic tools, such as image-based deep learning and vibration monitoring, into citywide seismic resilience planning. Full article

This paper presents a design research study that develops a comprehensive conceptual framework for an integrated digital twin system for architectural heritage. The framework aims to explore mechanisms for real-time monitoring and the coupled regulation of structural health, microclimatic conditions, and energy performance. In the context of the ongoing global warming emergency, this framework supports climate adaptation strategies for heritage sites. It enables a fully coordinated operational process encompassing real-time sensing, predictive analysis, coupled control, and decision support. In the structural dimension, the framework is designed to utilise sensors to monitor and warn against cracks, settlement, and deformation, whilst integrating models to analyse stress conditions. In the microclimate dimension, the study envisages predicting and adjusting HVAC and lighting systems based on environmental parameters and footfall monitoring data via algorithms, with the aim of balancing occupant comfort with humidity control and mould prevention. Regarding energy, the framework optimises equipment operation through smart metering and algorithms and we propose a modelling tool for the quantitative assessment of energy-saving retrofit effects. Furthermore, the framework incorporates the establishment of an open-access dataset covering structural, microclimate, and energy use data, providing data standards and a foundation for subsequent empirical research. Full article

Phosphorus is a ubiquitous contaminant in urban and agricultural landscapes. A retention basin located in the southern part of Barnegat Bay, New Jersey, was identified as receiving stormwater runoff with elevated phosphorus concentrations. The basin is surrounded by expanding urban development, contributing to the progressive degradation of water quality in the bay, which is already highly eutrophic. This study evaluated the effectiveness of a culvert retrofit with a green filter media composed of granulated-aluminum-based drinking water-treatment residuals (Al-WTR) and granular carbon (5:1 ratio, w/w) for the removal of phosphorus and suspended sediments from stormwater runoff. The performance of the filter media was assessed through water quality monitoring following runoff events over a 12-month period. The results indicated that the green filter media achieved up to 52% removal of total phosphorus from stormwater influent. However, treatment efficiency declined after approximately five months due to clogging of the geotextile bag housing the media. The replacement of the geotextile bag restored phosphorus removal performance (59%), highlighting the importance of routine maintenance. The findings demonstrate a cost-effective, environmentally sustainable, and innovative green engineering approach for mitigating phosphorus contamination in urban stormwater. Full article

Open-air asphalt milling generates hazardous respirable crystalline silica (RCS), posing severe risks to operators of legacy machines lacking enclosed cabs. This study evaluates a novel, standalone retrofit water spray system designed to intercept fugitive dust. Field validation across 11 road maintenance sites involved particle characterization and paired system-off/on exposure monitoring. Results indicated a Mass Median Aerodynamic Diameter (MMAD) of 6.12 µm, confirming the efficacy of fine-atomizing nozzles (0.3 mm) for capturing respirable fractions. The system achieved RCS suppression efficiencies ranging from 60% to over 85% under low-to-moderate wind conditions (<2.5 m/s). A comparative analysis revealed no significant performance gain from larger 0.5 mm nozzles, supporting the use of smaller orifices for optimal water conservation. However, suppression efficacy degraded significantly when crosswinds exceeded 2.5 m/s, indicating a potential operational boundary. This retrofit solution provides a scientifically validated, cost-effective engineering control for reducing occupational silica exposure in aging road maintenance fleets. Full article