Search Results (938)

Machine learning and artificial intelligence have transformed fault detection and maintenance strategies for industrial machinery. This study applies well-established data-driven techniques to a rarely explored industrial application—the condition monitoring of high-precision paper cutting machines—enhancing condition-based maintenance to improve operational efficiency, safety, and cost-effectiveness. A key element of the proposed approach is the integration of an infrared pyrometer into vibration monitoring, utilizing accelerometer data to evaluate the state of health of machinery. Unlike traditional fault detection studies that focus on extreme degradation states, this work successfully identifies subtle deviations from optimal, which even expert technicians struggle to detect. Building on a feasibility study conducted with Tecnau SRL, a comprehensive diagnostic system suitable for industrial deployment is developed. Endurance tests pave the way for continuous monitoring under various operating conditions, enabling real-time industrial diagnostic applications. Multi-scale signal analysis highlights the significance of transient and steady-state phase detection, improving the effectiveness of real-time monitoring strategies. Despite the physical similarity of the classified states, simple time-series statistics combined with machine learning algorithms demonstrate high sensitivity to early-stage deviations, confirming the reliability of the approach. Additionally, a systematic analysis to downgrade acquisition system specifications identifies cost-effective sensor configurations, ensuring the feasibility of industrial implementation. Full article

Fault diagnosis is of great significance for the maintenance of rotating machinery. Deep learning is an intelligent diagnostic technique that is receiving increasing attention. To address the issues of industrial data with small samples and varying working conditions, a residual convolutional neural network based on the attention mechanism is put forward for the fault diagnosis of rotating machinery. The method incorporates channel attention and spatial attention simultaneously, implementing channel-wise recalibration for frequency-dependent feature adjustment and performing spatial context aggregation across receptive fields. Subsequently, a residual module is introduced to address the vanishing gradient problem of the model in deep network structures. In addition, LSTM is used to realize spatiotemporal feature fusion. Finally, label smoothing regularization (LSR) is proposed to balance the distributional disparities among labeled samples. The effectiveness of the method is evaluated by its application to the vibration signal data from the safe injection pump and the Case Western Reserve University (CWRU). The results show that the method has superb diagnostic accuracy and strong robustness. Full article

Prognostic and Health Management (PHM) strategies are gaining increasingly more traction in almost every field of engineering, offering stakeholders advanced capabilities in system monitoring, anomaly detection, and predictive maintenance. Primary flight control actuators are safety-critical elements within aircraft flight control systems (FCSs), and currently, they are mainly based on Electro-Hydraulic Actuators (EHAs) or Electro-Hydrostatic Actuators (EHSAs). Despite the widespread diffusion of PHM methodologies, the application of these technologies for EHAs is still somewhat limited, and the available information is often restricted to the industrial sector. To fill this gap, this paper provides an in-depth analysis of state-of-the-art EHA PHM strategies for aerospace applications, as well as their limitations and further developments through a Systematic Literature Review (SLR). An objective and clear methodology, combined with the use of attractive and informative graphics, guides the reader towards a thorough investigation of the state of the art, as well as the challenges in the field that limit a wider implementation. It is deemed that the information presented in this review will be useful for new researchers and industry engineers as it provides indications for conducting research in this specific and still not very investigated sector. Full article

Accurate prediction of the coal abrasive index (AI) is critical for optimizing coal processing efficiency and minimizing equipment wear in industrial applications. This study explores tree-based machine learning models; Random Forest (RF), Gradient Boosting Trees (GBT), and Extreme Gradient Boosting (XGBoost) to predict AI using selected coal properties. A database of 112 coal samples from the KwaZulu-Natal Coalfield in South Africa was used. Initial predictions using all eight input properties revealed suboptimal testing performance (R²: 0.63–0.72), attributed to outliers and noisy data. Feature importance analysis identified calorific value, quartz, ash, and Pyrite as dominant predictors, aligning with their physicochemical roles in abrasiveness. After data cleaning and feature selection, XGBoost achieved superior accuracy (R² = 0.92), outperforming RF (R² = 0.85) and GBT (R² = 0.81). The results highlight XGBoost’s robustness in modeling non-linear relationships between coal properties and AI. This approach offers a cost-effective alternative to traditional laboratory methods, enabling industries to optimize coal selection, reduce maintenance costs, and enhance operational sustainability through data-driven decision-making. Additionally, quartz and Ash content were identified as the most influential parameters on AI using the Cosine Amplitude technique, while calorific value had the least impact among the selected features. Full article

Prescriptive Maintenance (PsM) transforms industrial asset management by enabling autonomous decisions through simultaneous failure anticipation and optimal maintenance recommendations. Yet, despite increasing research interest, the conceptual clarity, technological maturity, and practical deployment of PsM remains fragmented. Here, we conduct a comprehensive and application-oriented Systematic Literature Review of studies published between 2013–2024. We identify key enablers—artificial intelligence and machine learning, horizontal and vertical integration, and deep reinforcement learning—that map the functional space of PsM across industrial sectors. The results from our multivariate meta-synthesis uncover three main thematic research clusters, ranging from decision-automation of technical (multi)component-level systems to strategic and organisational-support strategies. Notably, while predictive models are widely adopted, the translation of these capabilities to PsM remains limited. Primary reasons include semantic interoperability, real-time optimisation, and deployment scalability. As a response, a structured research agenda is proposed to emphasise hybrid architectures, context-aware prescription mechanisms, and alignment with Industry 5.0 principles of human-centricity, resilience, and sustainability. The review establishes a critical foundation for future advances in intelligent, explainable, and action-oriented maintenance systems. Full article

This study introduces a time-efficient rotating bending fatigue testing system featuring 11 dual-end spindles, enabling simultaneous testing of 22 specimens. Designed for high-throughput fatigue life (S–N curve) assessment, the system theoretically allows over 93% reduction in total test duration, with 87.5% savings demonstrated in validation experiments using AISI 304 stainless steel. The PLC-based architecture provides autonomous operation, real-time failure detection, and automatic cycle logging. ER16 collet holders are easily replaceable within one minute, and all the components are selected from widely available industrial-grade parts to ensure ease of maintenance. The modular design facilitates straightforward adaptation to different station counts. The validation results confirmed an endurance limit of 421 MPa, which is consistent with the established literature and within ±5% deviation. Fractographic analysis revealed distinct crack initiation and propagation zones, supporting the observed fatigue behavior. This high-throughput methodology significantly improves testing efficiency and statistical reliability, offering a practical solution for accelerated fatigue life evaluation in structural, automotive, and aerospace applications. Full article

This study develops a domain-adaptive multimodal RAG (Retrieval-Augmented Generation) system to improve the accuracy and efficiency of technical question answering based on large-scale structured manuals. Using Hyundai Staria maintenance documents as a case study, we extracted text and images from PDF manuals and constructed QA, RAG, and Multi-Turn datasets to reflect realistic troubleshooting scenarios. To overcome limitations of baseline RAG models, we proposed an enhanced architecture that incorporates sentence-level similarity annotations and parameter-efficient fine-tuning via LoRA (Low-Rank Adaptation) using the bLLossom-8B language model and BAAI-bge-m3 embedding model. Experimental results show that the proposed system achieved improvements of 3.0%p in BERTScore, 3.0%p in cosine similarity, and 18.0%p in ROUGE-L compared to existing RAG systems, with notable gains in image-guided response accuracy. A qualitative evaluation by 20 domain experts yielded an average satisfaction score of 4.4 out of 5. This study presents a practical and extensible AI framework for multimodal document understanding, with broad applicability across automotive, industrial, and defense-related technical documentation. Full article

Wax deposition, driven by the crystallization of long-chain n-alkanes, poses severe challenges across industries such as petroleum, oil and natural gas, food processing, and chemical manufacturing. This phenomenon compromises flow efficiency, increases energy demands, and necessitates costly maintenance interventions. Wax inhibitors, designed to mitigate these issues, operate by altering wax crystallization, aggregation, and adhesion over the pipelines. Classic wax inhibitors, comprising synthetic polymers and natural compounds, have been widely utilized due to their established efficiency and scalability. However, synthetic inhibitors face environmental concerns, while natural inhibitors exhibit reduced performance under extreme conditions. The advent of nano-based wax inhibitors has revolutionized wax management strategies. These advanced materials, including nanoparticles, nanoemulsions, and nanocomposites, leverage their high surface area and tunable interfacial properties to enhance efficiency, particularly in harsh environments. While offering superior performance, nano-based inhibitors are constrained by high production costs, scalability challenges, and potential environmental risks. In parallel, the development of “green” wax inhibitors derived from renewable resources such as vegetable oils addresses sustainability demands. These eco-friendly formulations introduce functionalities that reinforce inhibitory interactions with wax crystals, enabling effective deposition control while reducing reliance on synthetic components. This review provides a comprehensive analysis of the mechanisms, applications, and comparative performance of classic and nano-based wax inhibitors. It highlights the growing integration of sustainable and hybrid approaches that combine the reliability of classic inhibitors with the advanced capabilities of nano-based systems. Future directions emphasize the need for cost-effective, eco-friendly solutions through innovations in material science, computational modeling, and biotechnology. Full article

The construction of concrete pavements has increased due to their better durability, lifespan, and lower maintenance costs. However, this has resulted in the increased consumption of Portland cement, which is one of the major contributors to carbon emissions. Consequently, the research on alternative binders such as geopolymer concrete has increased in recent times. There are several research studies that investigate the feasibility of geopolymer concrete as a construction material, with limited studies exploring its application in concrete pavements. Therefore, this review study explores the material properties of geopolymer concrete pertinent to the performance of concrete pavements. It also discusses the potential of various industrial and agricultural waste as precursor material in geopolymer concrete. The findings of this paper show that most of the studies used fly ash and ground granulated blast furnace slag (GGBFS) as precursor material in geopolymer pavement-quality concrete, and there is a vast scope in the exploration of other industrial and agricultural waste as precursor material. The mechanical and durability properties of geopolymer pavement-quality concrete are superior to conventional pavement concrete. It is also observed that the drying shrinkage and coefficient of thermal expansion of geopolymer pavement-quality concrete are lower than those of conventional pavement concrete, and this will positively benefit the long-term performance of concrete pavements. The results of fatigue analysis and mechanical load test on the geopolymer pavement-quality concrete indicate its improved performance when compared to the conventional pavement concrete. Full article

The growing adoption of IoT applications has led to increased use of low-power microcontroller units (MCUs) for energy-efficient, local data processing. However, deploying deep neural networks (DNNs) on these constrained devices is challenging due to limitations in memory, computational power, and energy. Traditional methods like cloud-based inference and model compression often incur bandwidth, privacy, and accuracy trade-offs. This paper introduces a novel Decentralized Distributed Sequential Neural Network (DDSNN) designed for low-power MCUs in Tiny Machine Learning (TinyML) applications. Unlike the existing methods that rely on centralized cluster-based approaches, DDSNN partitions a pre-trained LeNet across multiple MCUs, enabling fully decentralized inference in wireless sensor networks (WSNs). We validate DDSNN in a real-world predictive maintenance scenario, where vibration data from an industrial pump is analyzed in real-time. The experimental results demonstrate that DDSNN achieves 99.01% accuracy, explicitly maintaining the accuracy of the non-distributed baseline model and reducing inference latency by approximately 50%, highlighting its significant enhancement over traditional, non-distributed approaches, demonstrating its practical feasibility under realistic operating conditions. Full article

Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results. Full article

This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and long-term responses of condition indicators to shocks in oil temperature, offering a robust framework for a counterfactual analysis. To complement the time-domain perspective, we applied a wavelet coherence analysis (WCA) to explore time–frequency co-movements and phase relationships between the condition indicators under varying operational regimes. The DARDL results revealed that the ball energy, cage energy, and inner and outer race indicators significantly increased in response to the oil temperature in the long run. The WCA results further confirmed the positive association between oil temperature and the condition indicators under examination, aligning with the DARDL estimations. The DARDL model revealed that the ball energy and the inner race energy have statistically significant long-term effects on the oil temperature, with p-values < 0.01. The adjusted R² of 0.785 and the root mean square error (MSE) of 0.008 confirm the model’s robustness. The wavelet coherence analysis showed strong time–frequency correlations, especially in the 8–16 scale range, while the frequency-domain causality (FDC) tests confirmed a bidirectional influence between the oil temperature and several condition indicators. The FDC analysis showed that the oil temperature significantly affected the BGCIs, with evidence of feedback effects, suggesting a mutual dependency. These findings contribute to the advancement of predictive maintenance frameworks in HUMSs by providing practical insights for enhancing system reliability and optimizing maintenance schedules. The integration of dynamic econometric approaches demonstrates a robust methodology for monitoring critical mechanical components and encourages further research in broader aerospace and industrial contexts. Full article

This study presents the design and implementation of a novel, sensor-based falling-sphere viscometer specifically tailored for measuring the viscosity of engine oil. The equipment utilizes a metallic sphere and two strategically placed sensors to determine the travel time over a predetermined distance within an oil-filled tube. By applying fundamental principles of fluid dynamics, including Stokes’ law, the system accurately calculates the dynamic viscosity based on the sphere’s velocity and the oil’s density. Experimental validation at particular temperature demonstrates the device’s sensitivity and reliability, which are critical for assessing oil degradation and engine performance. The simplicity and low cost of the design make it an attractive alternative to conventional, more complex viscometers. Furthermore, the automated data acquisition system reduces human error and enhances reproducibility of results. Overall, the developed instrument shows great promise for both laboratory research and practical maintenance applications in the automotive industry. Full article

In the context of Industry 4.0, Total Productive Maintenance (TPM) is undergoing a major shift driven by digital technologies such as the IoT, AI, cloud computing, and Cyber–Physical systems. This study explores how these technologies reshape traditional TPM pillars and practices through a two-phase methodology: bibliometric analysis, which reveals global research trends, key contributors, and emerging themes, and a systematic review, which discusses how core TPM practices are being transformed by advanced technologies. It also identifies key challenges of this transition, including data aggregation, a lack of skills, and resistance. However, despite the growing body of research on digital TPM, a major gap persists: the lack of a standardized model applicable across industries. Existing approaches are often fragmented or too context-specific, limiting scalability. Addressing this gap requires a structured approach that aligns technological advancements with TPM’s foundational principles. Taking a cue from these findings, this article formulates a systematic and scalable framework for TPM 4.0 deployment. The framework is based on four pillars: modular technological architecture, phased deployment, workforce integration, and standardized performance indicators. The ultimate goal is to provide a basis for a universal digital TPM standard that enhances the efficiency, resilience, and efficacy of smart maintenance systems. Full article

With advancements in computational resources, artificial intelligence has gained significant attention in motor health monitoring. These sophisticated deep learning algorithms have been widely used for induction motor health monitoring due to their autonomous feature extraction abilities and end-to-end learning capabilities. However, in real-world scenarios, challenges such as limited labeled data and diverse operating conditions have led to the application of transfer learning for motor health monitoring. Transfer learning utilizes pretrained models to address new tasks with limited labeled data. Recent advancements in this domain have significantly improved fault diagnosis, condition monitoring, and the predictive maintenance of induction motors. This study reviews state-of-the-art transfer learning techniques, including domain adaptation, fine-tuning, and feature-based transfer for induction motor health monitoring. The key methodologies are analyzed, highlighting their contributions to improving fault detection, diagnosis, and prognosis in industrial applications. Additionally, emerging trends and future research directions are discussed to guide further advancements in this rapidly evolving field. Full article