Predictive Maintenance Research Articles

The Fuse Platform: Integrating data from IoT and other Sensors into an Industrial Spatial Digital Twin

Abstract. Digital Twins as virtual representations of industrial assets are being used to assimilate varied sources of data for improved awareness and decision making in operations and process optimisation. This paper explores the integration of IoT sensors into a spatial digital twin called Fuse that Woodside Energy has been building for the assets it operates. We describe the Fuse platform and its knowledge graph data core that is used to organise and inter-relate data for presentation within 3D visualisations, domain-specific contexts and immersive augmented reality presentations. The key contribution here is the use of a knowledge graph to link diverse data sources so as to contextualise sensor data for actionable insights. One area of significant innovation has been the development and use of new Internet of Things (IoT) devices which have been enabled by advances in sensor technology, connectivity, and cloud computing. These new tailored data sources are complimenting existing plant and resource planning data for improved asset monitoring, predictive maintenance and process automation.

Learning to rank anomalies: scalar performance criteria and maximization of rank statistics

AbstractThe ability to collect and store ever more massive data, unlabeled in many cases, has been accompanied by the need to process them efficiently in order to extract relevant information and possibly design solutions based on the latter. In various situations, the vast majority of the observations exhibit the same behavior, while a small proportion deviates from it. Detecting these outlier observations (or equivalently defined as anomalies) is now one of the major challenges for machine learning applications (e.g. fraud detection or predictive maintenance). We propose here a novel methodology for outlier/anomaly detection, by learning a scoring function defined on the feature space allowing for ranking the observations by degree of abnormality. The scoring function is built through maximization of an empirical performance criterion taking the form of a (two-sample) linear rank statistic. We show that bipartite ranking algorithms can thus be used to learn nearly optimal scoring function with provable theoretical guarantees. We illustrate our methodology with numerical experiments based on open access online code.

Smart composite structural insulated panels (CSIPs) with embedded piezoelectric sensors for damage localization

Coastal communities face increasing vulnerability to natural disasters, necessitating resilient construction solutions. This study presents the development of smart composite structural insulated panels (CSIPs) embedded with piezoelectric sensors for damage localization in hurricane and tornado-prone areas. Experimental studies, such as four-point bending and in-panel compression tests, were conducted to analyse the shock response and failure modes of these smart CSIPs. Material characterization informs the development of finite element models for simulation, enhancing structural design. By integrating structural health monitoring capabilities, these smart CSIPs enable continuous tracking of their response to environmental factors, improving longevity and durability. Shock identification and localization in smart CSIPs are successfully demonstrated through numerical simulations and experimental validation, showcasing the potential step towards self-resilient structures in coastal regions. This novel approach provides real-time information on structural integrity, paving the way for improved safety and predictive maintenance of buildings and infrastructure utilizing CSIPs.

Fertigungsprozessanalyse eines mittels Innenhochdruck-Umformung hergestellten Bauteils

Abstract Series manufacturers in the field of hydroforming do not always have the necessary database for predictive maintenance, especially for the production of complex hydroformed components. Small and medium-sized companies in particular often lack the resources to acquire, process and profitably utilize these data sets under production conditions. The IHU processes that occur in practice are usually highly complex, both in terms of geometry and against the background of additional process steps (punching, plunging). This results in large amounts of data and complex data analyses, which are reflected in the data processing costs. A data analysis is to be carried out on the basis of a complex hydroforming process and its additional benefit for quality management and predictive maintenance is to be explained.

Leveraging Big Data Analytics for Enhanced Production Processes and Predictive Maintenance

Leveraging Big Data Analytics for Enhanced Production Processes and Predictive Maintenance

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

Abstract In the field of electric motor maintenance, this study introduces a transformative approach by inte-grating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropy algorithms on standard fault detection measure-ments, the research significantly advances predictive maintenance strategies through a robust, ear-ly-indication, system-agnostic analysis. Detailed examination is conducted, comparing results de-rived from datasets that include statistical features (excluding entropy) against the proposed entro-py-based datasets, when applied to a Multi-Layer Perceptron Classifier. Optimization of the MLPC and all compared algorithms’ hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in clas-sification accuracy of diverse electric motor operational states, facilitating the differentiation be-tween healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby support-ing the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.

Autonomous Vehicle Diagnostics and Support: A Framework for API-Driven Microservices

The rapid evolution of autonomous vehicles (AVs) demands robust and scalable diagnostic and support systems to ensure seamless operations, safety, and performance. This paper presents a framework for API-driven microservices tailored to address the unique diagnostic and support requirements of AVs. Traditional monolithic systems are insufficient for managing the complex, real-time data generated by AVs, necessitating a shift toward microservices architecture. By leveraging API-driven microservices, the proposed framework ensures modularity, scalability, and real-time communication between various vehicle components and external support systems. The framework is designed to support continuous vehicle monitoring, predictive maintenance, fault detection, and real-time decision-making, all while minimizing latency. APIs play a critical role in enabling interaction between different microservices, allowing for seamless data exchange across diverse platforms. This interoperability facilitates integration with third-party services, such as cloud-based diagnostics, real-time traffic information, and software updates. Additionally, the microservices architecture ensures fault isolation, meaning that failures in one service do not compromise the entire system. The framework also emphasizes the importance of cybersecurity and data privacy. Given the sensitive nature of AV data, API security protocols such as OAuth2 and TLS encryption are incorporated to safeguard communication between microservices and external systems. Furthermore, the design is scalable, allowing for the integration of future AV technologies and third-party service enhancements without disrupting existing functionalities. In conclusion, API-driven microservices provide an efficient, flexible, and secure solution for autonomous vehicle diagnostics and support. This framework has the potential to improve AV reliability, reduce downtime, and enhance safety by enabling real-time, data-driven decision-making and proactive maintenance. Future work may explore the integration of machine learning algorithms to further optimize diagnostics and predictive maintenance capabilities. Keywords: Autonomous Vehicles, Microservices, API-Driven Architecture, Diagnostics, Predictive Maintenance, Real-Time Data, Cybersecurity, Vehicle Support Systems, Modularity, Scalability.

AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability

AI-Driven Predictive Maintenance in Modern Maritime Transport—Enhancing Operational Efficiency and Reliability

Predictive Maintenance of Induction Motor in the Cutting Section of Paper Industry

Induction Motors are vital to the paper industry because they power a variety of equipment needed for pulp processing, drying and cutting among other tasks. Ensuring the consistent manufacture of these motors and satisfying market demands depends heavily on their dependability. However, the extreme humidity, dust and fluctuating loads that occur in the paper sector present serious obstacles to the efficiency and durability of induction motors. To maintain continuous production and satisfy consumer demand, the paper industry significantly depends on the effective operation of a variety of machinery including induction motors in the cutting area. Unexpected malfunctions in these vital parts however might result in expensive downtime and lost output. By using data-driven strategies that anticipate and avoid breakdowns before they happen, predictive maintenance techniques provide a proactive way to reduce such risks. This report provides an extensive analysis of the application of predictive maintenance techniques designed especially for induction motors in the paper industry’s cutting division. The predictive maintenance techniques includes Random Forest Tree, Linear Regression and Support Vector Machines algorithm with these algorithm the Induction Motor’s variations in temperature, vibration, sound, and speed parameters were collected and trained for predicting the failure. Among these algorithms Support Vector Machines shows greater advantage in predicting the failure with the accuracy of 84% whereas Random Forest Tree with 75% and Linear regression with 72%. As well as the real time data’s were collected and stored in the database. The performance of the Induction Motor were efficiently improved and monitored under normal and fault condition by Machine Learning techniques.

Artificial Intelligence (AI) Applications in Drug Discovery and Drug Delivery: Revolutionizing Personalized Medicine

Artificial intelligence (AI) encompasses a broad spectrum of techniques that have been utilized by pharmaceutical companies for decades, including machine learning, deep learning, and other advanced computational methods. These innovations have unlocked unprecedented opportunities for the acceleration of drug discovery and delivery, the optimization of treatment regimens, and the improvement of patient outcomes. AI is swiftly transforming the pharmaceutical industry, revolutionizing everything from drug development and discovery to personalized medicine, including target identification and validation, selection of excipients, prediction of the synthetic route, supply chain optimization, monitoring during continuous manufacturing processes, or predictive maintenance, among others. While the integration of AI promises to enhance efficiency, reduce costs, and improve both medicines and patient health, it also raises important questions from a regulatory point of view. In this review article, we will present a comprehensive overview of AI’s applications in the pharmaceutical industry, covering areas such as drug discovery, target optimization, personalized medicine, drug safety, and more. By analyzing current research trends and case studies, we aim to shed light on AI’s transformative impact on the pharmaceutical industry and its broader implications for healthcare.

Anomaly detection and confidence interval‐based replacement in decay state coefficient of ship power system

AbstractThe anomaly detection and predictive replacement of the degradation decay state coefficient (Desc) of ship power system (SPS) are crucial for ensuring their operational safety and maintenance efficiency. This study introduces the YC3Model, a model based on a dynamic triple sliding window mechanism, and Gaussian process regression) to address this challenge. It combines the temporal variation characteristics of the decay state coefficient's original data, first‐order, and second‐order differential data in both normal and abnormal trend intervals. The model calculates three local statistical measures within each sliding window and employs the Z‐score method for anomaly detection. The combination of three sliding windows reduces false positives and negatives, enhancing the precision of anomaly detection. For detected anomalies, Gaussian process regression is used for prediction and replacement, providing confidence intervals to increase the reliability of the predicted values. Experimental results demonstrate that the YC3Model exhibits superior anomaly detection accuracy and adaptability in the degradation process of SPS, surpassing traditional methods across a range of evaluation metrics. This confirms the potential of YC3Model in health monitoring and predictive maintenance of SPS, offering reliable data input for the intelligent operation and maintenance (IO&M) of SPS.

Analysis of signals from air conditioner compressors with ordinal patterns and machine learning

Most machines are equipped with devices that monitor their operation. Air conditioners, in particular, are routinely monitored through various measurements. A desirable outcome of this monitoring is identifying when the device will likely require maintenance. In this study, we present the use of Ordinal Patterns, a symbolic transformation of time series, which allows for the visual assessment of the type of operation. Ordinal Patterns are chosen because they can transform intricate time series into simple and intuitive symbolic representations. The technique is visually appealing, generating points on a plane whose positions reveal hidden dynamics. This approach makes it easier to identify recurring or abnormal patterns in machine operations that may indicate wear or impending failure. Additionally, Ordinal Patterns allow for precise and understandable visualization of operational data, making interpreting results more accessible for professionals who may not be experts in data analysis. We compare two machines under different operational conditions with six measured variables. We analyze the expressiveness of the Ordinal Patterns and identify those variables that best differentiate the two machines. Furthermore, we incorporate machine learning algorithms, such as Artificial Neural Networks, Support Vector Machines, and Decision Trees, to evaluate and validate the effectiveness of Ordinal Patterns as discriminative features. Integrating machine learning methods with a symbolic transformation offers a robust approach for the early and accurate diagnosis of potential failures, enhancing the predictive maintenance of air conditioning equipment.

A hybrid LSTM random forest model with grey wolf optimization for enhanced detection of multiple bearing faults

Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

AI and Blockchain Integration in Business Aviation: Securing Supply Chain and Enhancing Traceability

Purpose: The research looks at how AI converges with the blockchain technology in business aviation to better the domains considered most important: maintenance, operations, flight optimization, and observation of regulatory standards. This is aimed at understanding how these technologies bring solutions that will mitigate the specific challenges to aviation related to the security of supply chains, fraud detection, and customer experience improvement. Methodology: The research adopts a qualitative and quantitative approach, reviewing case studies from leading aviation companies such as Airbus and Boeing, alongside real-world data analysis. The integration of AI for predictive maintenance, flight optimization, and in-flight entertainment (IFE) was examined, while blockchain’s role in securing data management, regulatory compliance, and fraud prevention was assessed through documented applications and industry reports. Findings: The identification of predictive maintenance, as ensured through AI, optimizes aircraft performance, having reduced downtime. Moreover, blockchain increases supply chain transparency and security, reduces fraud, and ensures immutable records. On top of that, AI-powered flight planning and in-flight operations significantly help in operational efficiency and customer satisfaction. Moreover, blockchain helps in compliance with regulations because of its secured and traceable data; hence, it serves as a very viable tool in aviation operations. Unique Contribution to Theory, Policy, and Practice: This research contributes to the theory of AI and blockchain integration by presenting a comprehensive framework for their application in aviation. It provides new insights into how AI and blockchain can not only enhance operational efficiency but also address fraud and compliance issues, which are pivotal for policy development. Practically, it offers aviation companies a roadmap for leveraging these technologies to secure their supply chains, optimize operations, and enhance customer experiences, ultimately advancing the entire industry.

Remaining Useful Life Prediction Method Based on Dual-Path Interaction Network with Multiscale Feature Fusion and Dynamic Weight Adaptation

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction.

AI in Business Aviation Route Optimization: Reducing Fuel Consumption and Environmental Impact

Purpose: This paper reviews ways that artificial intelligence could be used to make business aviation operations most fuel-efficient, cheapest in cost of operation, and smallest in terms of ecological footprint. The research elaborated possible ways of using AI in the spheres of flight planning, predictive maintenance, fuel management, emission tracking, and compliance in business aviation. Methodology: It was also empirical case-study-oriented research that investigated the impacts of AI-driven technologies on business aviation operators, basically NetJets, VistaJet, and Flexjet. The impacts are from route optimization to predictive maintenance, fuel management, and compliance with regulations. Information such as fuel consumption, CO2 emissions, and operational efficiency was obtained before and after the adoption of AI technologies. Findings: The fuel savings from AI-driven systems are reaching a point of salience, at 9 to 14% in the various cases, with associated reductions in CO2 emissions. AI-powered predictive maintenance resulted in a 20% reduction in unscheduled events, thereby bettering the availability of fleets. Artificial intelligence increased overall efficiency and improved decisions for in-flight, real-time operations management while conforming to regulatory requirements in reporting. Additionally, AI is going to bring tremendous values in optimizing SAFs and aircraft energy-efficient technology to make flying sustainable. Unique Contribution to Theory, Policy, and Practice: This work contributes to AI in aviation by demonstrating its practical application in reducing environmental impacts and operational costs in business aviation. It provides a framework for integrating AI into aviation management systems and highlights the importance of public-private cooperation for wider AI adoption. The findings are valuable for policymakers, business aviation operators, and industry leaders aiming to advance sustainability and regulatory compliance

Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity

Predictive maintenance has gained importance due to industrialization. Harnessing advanced technologies like sensors and data analytics enables proactive interventions, preventing unplanned downtime, reducing costs, and enhancing workplace safety. They play a crucial role in optimizing industrial operations, ensuring the efficiency, reliability, and longevity of equipment, which have become increasingly vital in the context of industrialization. The analysis of time series’ stationarity is a powerful and agnostic approach to studying variations and trends that may indicate imminent failures in equipment, thus contributing to the effectiveness of predictive maintenance in industrial environments. The present paper explores the use of the Augmented Dickey–Fuller p-value temporal variation as a possible method for determining trends in sensor time series and thus anticipating possible failures of a wood chip pump in the paper industry.

Machine learning-based outlier detection for pipeline in-line inspection data

Pipeline companies are facing challenges in maintaining the integrity and reliability of their pipelines. They are working towards predictive maintenance using machine learning-based approaches to predicting anomalies. Training machine learning models requires sufficient data. Data quality is therefore becoming important because inaccurate data will lead to an inaccurate or wrong decision on pipeline condition assessment and the following management. This research paper intends to address the data quality issues of pipeline inspection data such as in-line inspection (ILI) data using machine learning models. Different machine learning models developed by random forest regression, linear regression, and nearest neighbors’ methods were tested to detect outliers in the ILI data. In this paper, the ILI data collected from an oil pipeline over a period of 22 years was applied to testing and analysis. To verify the outlier detection results of machine learning models, we used statistical analysis including Z-score method to check and find if there are any gaps in the analysis. It verifies that all these methods show almost the same or very similar results for the detection of the outliers. Hence, this study presents a robust method for the field applications in the pipeline industry.

Artificial Intelligence in Enhancing Operational Efficiency in Logistics and SCM

This study examines the role of Artificial Intelligence (AI) in enhancing operational efficiency in logistics and supply chain management (SCM). It highlights how AI automates processes, improves demand forecasting, optimises inventory management, and enhances route planning. The research also explores AI’s impact on predictive maintenance and customer experience through real-time tracking and chatbots. By leveraging advanced algorithms, organisations can achieve greater accuracy, reduce costs, and increase flexibility, gaining a competitive advantage in the market. The findings provide valuable insights for practitioners and researchers on the strategic opportunities presented by AI in logistics and SCM.

Emerging Trends in Industry 4.0 and Predictive Maintenance

With the advent of Industry 4.0, predictive maintenance (PdM) is revolutionising maintenance procedures across various industries. The integration of advanced technologies, such as artificial intelligence (AI), the internet of things (IoT) and big data, is enhancing the potential of PdM in sectors including non-traditional sectors such as nuclear infrastructure, logistics and healthcare. This study highlights the development of PdM within Industry 4.0 and its impact on the financial and operational facets of numerous industries. It underscores the need for continuous innovation and adaptation in PdM strategies, emphasising the necessity for substantial investments in training, technology and the cultivation of a workforce skilled in digital technologies and data analytics. To fully capitalise on this dynamic field, the study stresses the importance of staying abreast of industry advancements, evaluating new tools and refining maintenance practices.