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Purpose: Predictive maintenance (PdM) aims to optimize maintenance operations by detecting operational anomalies and potential equipment failures before they lead to costly unplanned downtime. The goal is to minimize reactive maintenance and reduce the frequency of preventive maintenance interventions. This paper evaluates PdM strategies using knowledge-based, physics-based, and data-driven models to propose an integrated approach that enhances prediction accuracy, aligning with Industry 4.0 goals.Design/methodology/approach: A Systematic Literature Review (SLR) is conducted to examine the strengths and weaknesses of knowledge-based, physics-based, and data-driven models in predictive maintenance. The study assesses existing research, compares methodologies, and identifies opportunities for integrating these models to improve PdM outcomes.Findings: The review indicates that no single approach—whether knowledge-based, physics-based, or data-driven—is sufficient to meet the comprehensive demands of predictive maintenance. Instead, an integrated approach that combines these three models provides more accurate and cost-effective maintenance solutions, supporting the automation and efficiency goals of Industry 4.0.Research limitations/implications: The study's findings are limited by the availability of real-world data and case studies. Future research should focus on testing the proposed integrated model in diverse industrial contexts to validate its effectiveness across different sectors.Practical implications: The proposed approach offers industries a more reliable framework for optimizing maintenance strategies, improving operational efficiency, and reducing costs associated with equipment failures and excessive preventive measures.Social implications: By enhancing predictive maintenance, the integrated model supports sustainability efforts by reducing waste, improving resource utilization, and contributing to the longevity of machinery and equipment.Originality/value: This research offers a novel contribution by integrating knowledge-based, physics-based, and data-driven models into a unified PdM approach. It provides valuable insights for both academia and industry, especially in the context of Industry 4.0.

This study describes an application of hybrid modelling for an atmospheric variable in the Krško basin. The hybrid model is a combination of a physics-based and data-driven model and has some properties of both modelling approaches. In the authors’ case, it is used for the modelling of an atmospheric variable, namely relative humidity in a particular location for the purpose of using the predictions of the model as an input to the air-pollution-dispersion model for radiation exposure. The presented hybrid model is a combination of a physics-based atmospherical model and a Gaussian-process (GP) regression model. The GP model is a probabilistic kernel method that also enables evaluation of prediction confidence. The problem of poor scalability of GP modelling was solved using sparse GP modelling; in particular, the fully independent training conditional method was used. Two different approaches to dataset selection for empirical model training were used and multiple-step-ahead predictions for different horizons were assessed. It is shown in this study that the accuracy of the predicted relative humidity in the Krško basin improved when using hybrid models over using the physics-based model alone and that predictions for a considerable length of horizon can be used.

Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection–diffusion partial differential equation provides a flexible model class for spatiotemporal processes which is computationally feasible also for large data sets. The Gaussian process defined through the stochastic partial differential equation has, in general, a non‐separable covariance structure. Its parameters can be physically interpreted as explicitly modelling phenomena such as transport and diffusion that occur in many natural processes in diverse fields ranging from environmental sciences to ecology. To obtain computationally efficient statistical algorithms, we use spectral methods to solve the stochastic partial differential equation. This has the advantage that approximation errors do not accumulate over time, and that in the spectral space the computational cost grows linearly with the dimension, the total computational cost of Bayesian or frequentist inference being dominated by the fast Fourier transform. The model proposed is applied to post‐processing of precipitation forecasts from a numerical weather prediction model for northern Switzerland. In contrast with the raw forecasts from the numerical model, the post‐processed forecasts are calibrated and quantify prediction uncertainty. Moreover, they outperform the raw forecasts, in the sense that they have a lower mean absolute error.

Accurately estimating the mass density of the thermosphere can assist satellite operators in planning missions and optimizing satellite orbits, reducing the risk of collisions. The newly developed GOFT (Global Observation‐based Forecast for the Thermosphere) model specifies thermospheric mass density by optimizing uncertainty parameters in a physics‐based model based on model errors. This study extends the model to specify thermospheric mass density based on Two‐Line Elements (TLEs) from numerous space objects. The resulting mass density is validated against accelerometer‐derived mass density from different satellites. The comparison results revealed that assimilating TLEs into the GOFT model significantly improved the accuracy of the thermospheric mass density. In addition, the GOFT model also improved the accuracy of the electron density in the ionosphere, indicating that the assimilation capability of GOFT allows simultaneous specification of coupled ionosphere and thermosphere in future applications.

Soil moisture information is critical for applications like landslide susceptibility analysis and military trafficability assessment. Existing technologies cannot observe soil moisture at spatial scales of hillslopes (e.g., 100 to 102 m) and over large areas (e.g., 102 to 105 km2) with sufficiently high temporal coverage (e.g., days). Physics‐based hydrologic models can simulate soil moisture at the necessary spatial and temporal scales, albeit with error. We develop and test a data assimilation framework based on the ensemble Kalman filter for constraining uncertain simulated high‐resolution soil moisture fields to anticipated remote sensing products, specifically NASA's Soil Moisture Active‐Passive (SMAP) mission, which will provide global L band microwave observation approximately every 2–3 days. The framework directly assimilates SMAP synthetic 3 km radar backscatter observations to update hillslope‐scale bare soil moisture estimates from a physics‐based model. Downscaling from 3 km observations to hillslope scales is achieved through the data assimilation algorithm. Assimilation reduces bias in near‐surface soil moisture (e.g., top 10 cm) by approximately 0.05 m3/m3and expected root‐mean‐square errors by at least 60% in much of the watershed, relative to an open loop simulation. However, near‐surface moisture estimates in channel and valley bottoms do not improve, and estimates of profile‐integrated moisture throughout the watershed do not substantially improve. We discuss the implications of this work, focusing on ongoing efforts to improve soil moisture estimation in the entire soil profile through joint assimilation of other satellite (e.g., vegetation) and in situ soil moisture measurements. Key Points A hillslope‐scale soil moisture data assimilation framework is developed Simulated 3 km L band radar backscatter observations are assimilated directly Downscaling observations to hillslope scales is achieved via data assimilation

Prognostic and health management (PHM) plays a vital role in ensuring the safety and reliability of aircraft systems. The process entails the proactive surveillance and evaluation of the state and functional effectiveness of crucial subsystems. The principal aim of PHM is to predict the remaining useful life (RUL) of subsystems and proactively mitigate future breakdowns in order to minimize consequences. The achievement of this objective is helped by employing predictive modeling techniques and doing real-time data analysis. The incorporation of prognostic methodologies is of utmost importance in the execution of condition-based maintenance (CBM), a strategic approach that emphasizes the prioritization of repairing components that have experienced quantifiable damage. Multiple methodologies are employed to support the advancement of prognostics for aviation systems, encompassing physics-based modeling, data-driven techniques, and hybrid prognosis. These methodologies enable the prediction and mitigation of failures by identifying relevant health indicators. Despite the promising outcomes in the aviation sector pertaining to the implementation of PHM, there exists a deficiency in the research concerning the efficient integration of hybrid PHM applications. The primary aim of this paper is to provide a thorough analysis of the current state of research advancements in prognostics for aircraft systems, with a specific focus on prominent algorithms and their practical applications and challenges. The paper concludes by providing a detailed analysis of prospective directions for future research within the field.

This review identifies current machine-learning algorithms implemented in building structural health monitoring systems and their success in determining the level of damage in a hierarchical classification. The integration of physical models, feature extraction techniques, uncertainty management, parameter estimation, and finite element model analysis are used to implement data-driven model detection systems for SHM system design. A total of 68 articles using ANN, CNN and SVM, in combination with preprocessing techniques, were analyzed corresponding to the period 2011–2022. The application of these techniques in structural condition monitoring improves the reliability and performance of these systems.

Demands for more accurate machine learning models have given rise to rethinking current modeling approaches that were deemed unsuitable, primarily due to their computational complexity and the lack of availability and accessibility to representative data. In Industry 4.0, rapid advancements in Digital Twin (DT) technologies and the pervasiveness of cost-effective sensor technologies have pushed the incorporation of artificial intelligence, particularly data-driven machine learning models, for use in smart manufacturing. However, the persistent issue with such models is their high sensitivity to the training data and the lack of interpretability in the outcomes, at times generating unrealistic results. The incorporation of knowledge into the machine learning pipeline has been earmarked as the most promising approach to address such issues. This paper aims to answer this call through a Knowledge-embedded Machine Learning (KML) framework for smart manufacturing, which embeds knowledge from experience and, or physics information into the machine learning pipeline, thus making the outcomes from these models more representative of real applications. The merits of KML were then presented through comparative studies showing its capability to outperform knowledge-based and data-driven models. This promising outcome led to the development of frameworks that can potentially incorporate KML for smart manufacturing applications such as Prognostics and Health Management (PHM) and DT, further supporting the usefulness of the proposed KML framework.

Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple-phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

As the global population is expected to reach 10.3 billion by the mid-2080s, optimizing agricultural production and resource management is crucial. Climate change and environmental degradation further complicate these challenges, impacting crop productivity and food security. Traditional farming methods struggle with efficiently managing nutrients and water while ensuring high-quality products, leading to resource wastage and food safety concerns. This study aims to develop a hybrid model combining machine learning and physics-based techniques to predict fresh weight, leaf area, nitrate levels, and water consumption in lettuce grown in aeroponic systems, thereby enhancing resource management and product quality. We integrated a physics-based model with machine learning algorithms to create a dynamic hybrid framework. The model was validated with real-time data from aeroponic systems, showing good predictive performance, particularly for fresh weight and total leaf area. In contrast, predictions of nitrate content and water consumption were less accurate, due in part to smaller training datasets and limitations of the physics-based component under soilless conditions. Despite these challenges, the hybrid model offers a promising solution for optimizing controlled environment agriculture, addressing critical challenges in modern agriculture by improving efficiency and sustainability.