Search

In recent years, the way that maintenance is carried out has evolved due to the incorporation of digital tools and Industry 4.0 concepts. By connecting to and communicating with their production system, companies can now gather information about the current and future health of the equipment, enabling more efficient control through a process called predictive maintenance (PdM). The goal of PdM is to reduce unplanned downtimes and proactively address maintenance needs before failures occur. However, it can be challenging for industrial practitioners to implement an intelligent maintenance system that effectively manages data. This paper presents a methodology for developing and implementing a PdM system in the automotive industry, using open standards and scalable data management capabilities. The platform is validated through the presentation of two industry use cases. [ABSTRACT FROM AUTHOR]

This paper considers a multi-period multi-product capacitated lot-sizing problem. It develops an integrated predictive maintenance and production planning framework using deep learning and mathematical programming. The objective is to minimise the sum of maintenance, setup, holding, backorder, and production costs, while satisfying the demand for all products over the horizon under consideration. Based on a rolling horizon approach, the model dynamically integrates data-driven predictive maintenance and production planning. The used maintenance policy includes replacements and minimal repairs that are considered as preventive and corrective maintenance, respectively. To select preventive maintenance actions, a long short-term memory model is employed to accurately predict the health condition of the machine. Each rolling horizon consists of ordinary and forecast stages, and by collecting new sensor data, the maintenance and production decisions are simultaneously updated. The resulting integrated framework is validated using a benchmarking data set. The results are compared for different approaches to highlight the advantages of the proposed framework. [ABSTRACT FROM AUTHOR]

Predictive Maintenance (PdM) solutions assist decision-makers by predicting equipment health and scheduling maintenance actions, but their implementation in industry remains problematic. Specifically, prior research repeatedly indicates that decision-makers often refuse to adopt the data-driven, system-generated advice in their working procedures. In this paper, we address these acceptance issues by studying how PdM implementation changes the nature of decision-makers' work and how these changes affect their acceptance of PdM systems. We build on the human-centric Smith-Carayon Work System model to synthesise literature from research areas where system acceptance has been explored in more detail. Consequently, we expand the maintenance literature by investigating the human-, task-, and organisational characteristics of PdM implementation. Following the literature review, we distil ten propositions regarding decision-making behaviour in PdM settings. Next, we verify each proposition's relevance through in-depth interviews with experts from both academia and industry. Based on the propositions and interviews, we identify four factors that facilitate PdM adoption: trust between decision-maker and model (maker), control in the decision-making process, availability of sufficient cognitive resources, and proper organisational allocation of decision-making. Our results contribute to a fundamental understanding of acceptance behaviour in a PdM context and provide recommendations to increase the effectiveness of PdM implementations. [ABSTRACT FROM AUTHOR]

Maintenance is crucial for ensuring equipment reliability and minimizing downtime while managing associated costs. This study investigates a datadriven approach to predicting machine faults using Response Surface Methodology (RSM) and Adaptive Neuro-Fuzzy Inference System (ANFIS). RSM was employed to develop a mathematical model to analyze how operational parameters such as pressure, voltage, current, vibration, and temperature affect fault occurrence. Data were collected at three levels for each parameter using a central composite design. The model identified that faults peaked at a pressure of 28.38 N/m2, an operating voltage of 431.77 V, current consumption of 12.54 A, machine vibration of 47.17 Hz, and temperature of 25°C, with a maximum of 25 faults observed. Conversely, the lowest fault detection occurred at a pressure of 29.42 N/m2, an operating voltage of 441.04 V, current consumption of 12.04 A, machine vibration of 49.46 Hz, and temperature of 46.5°C. A strong correlation was found between these parameters and machine faults, with the model achieving high accuracy (R2 = 98.22%) and statistical significance (p-value <0.05), demonstrating its reliability in predicting faults. The study also compared RSM with ANFIS for fault detection and process optimization in the beverage industry. While RSM effectively optimized parameter relationships, ANFIS, with its adaptive learning capabilities, provided superior fault prediction accuracy. This comparative analysis highlighted the strengths of both methods and suggested that integrating them could enhance predictive maintenance strategies. The findings offer valuable insights for industry practitioners, recommending a combined approach to improve fault detection, optimize production processes, and enhance operational efficiency. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Contact probe is broadly used for the continuous monitoring of microelectronic components in manufacturing industries. False rejection of fine product due to defective contact probe significantly reduces the yield in production. Traditionally, defect detection for contact probes heavily depends on a valid range manually defined by engineers over the measured value of certain parameters. However, the subjective range defined according to engineer experience is prone to trigger a high rate of false alarms due to the inherent noise in the measured parameters. To address this issue, we construct a health index (HI) with the contact resistance‐directly‐related features to help monitor and assess the condition of contact probe. Based on the established HI, we develop Long Short‐Term Memory (LSTM) encoder‐decoder machine learning model to assess the condition of contact probe by forecasting the HI value in the future. Encoders from LSTM and convolutional neural network (CNN) are selected as the encoder‐decoder architecture for the sequence‐to‐sequence prediction due to their advantage in extracting the correlation of features at different scales. An explainable Artificial Intelligence (XAI) technique named Local interpretable model‐agnostic explanations (LIME) is used to quantify the contribution of each feature to the model prediction. The encoder from CNN is found to outperform the LSTM encoder in extracting the inter‐feature correlation. Finally, the predicted HI is used to signal the alarm for the maintenance action of contact probe when its value is below a predefined threshold. Comparison between the action alarm triggered by the developed HI and the actual maintenance records suggests that the proposed approach achieves at least 75% accuracy for the triggered alarm in the next 15 mins. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework for implementing digital twins in aircraft lifecycle management, with a focus on using data-driven models to enhance decision-making and operational efficiency. The proposed framework integrates cutting-edge technologies such as IoT sensors, big data analytics, machine learning, 6G communication, and cloud computing to create a robust digital twin ecosystem. This paper explores the key components of the framework, including lifecycle phases, new technologies, and models for digital twins. It discusses the challenges of creating accurate digital twins during aircraft operation and maintenance and proposes solutions using emerging technologies. The framework incorporates physics-based, data-driven, and hybrid models to simulate and predict aircraft behavior. Supporting components like data management, federated learning, and analytics tools enable seamless integration and operation. This paper also examines decision-making models, a knowledge-driven approach, limitations of current implementations, and future research directions. This holistic framework aims to transform fragmented aircraft data into comprehensive, real-time digital representations that can enhance safety, efficiency, and sustainability throughout the aircraft lifecycle. [ABSTRACT FROM AUTHOR]

The term “digital twin” is becoming increasingly prevalent in both research and politics with regard to infrastructure structures. Industry is making significant advancements in the fields of automation and autonomy. While numerous definitions are in circulation, some of which are identical, many are similar in nature. This work examines the digital twin from its original industry perspective and considers its relevance to the constructional engineering sector. The objective of this study is to provide an overview of the current approaches in the Industry 4.0 and constructional engineering industry, examining the associated technical opportunities and challenges. The study compares the definitions, requirements and projects executed in this field. The technologies under examination are contrasted on the basis of their position within the data model. The preliminary findings suggest that some solutions for Industry 4.0 have already been developed for the construction engineering sector. However, it must be acknowledged that some solutions have yet to be fully validated. [ABSTRACT FROM AUTHOR]

Smart systems such as data-driven machine health monitoring are emerging as powerful technology for advanced manufacturing as a result of the availability of low-cost sensors, wireless communication, and advances in Machine Learning (ML) and Artificial Intelligence (AI). Predictive maintenance (PdM) has become increasingly popular in manufacturing, which can identify approaching failures, determine root causes of operation anomalies, estimate the current health state of a system, and predict the future state and time when a component will fail in the absence of an intervention. One weakness of many past studies is the lack of run-to-failure data from an actual production environment. This paper presents run-to-failure data for the air compressor of an injection molding machine. A Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) is proposed to detect bearing faults in the air compressor, which can capture the long-term dependencies without losing the capability to identify local dependencies. The model achieves a 97.4% of prediction accuracy (95.3% of overall accuracy). Experiments for machine state classification are also conducted, and the classification performance compares favorably with conventional models. [ABSTRACT FROM AUTHOR]

The sustainability of enterprises in an increasingly competitive environment is proportional to their ability to use the resources efficiently. Maintenance departments are critical to ensure that resources are ready and operational. Increasing the efficiency of maintenance departments depends on reducing the number of failures, performing planned maintenance on time and sustaining availability of spare parts needed. Therefore, it is vital that businesses consider predictive maintenance and spare parts jointly. In this study, predictive maintenance and spare parts integration studies are carried out using gearbox failure data and spare parts consumption data of a bus fleet. This study aims to contribute to the reduction of failure costs by finding failure patterns and predicting the subsequent failures and spare parts to be used. The sequential pattern mining approach was used to determine failure patterns and the traditional frequent itemset mining approach was used to predict spare parts. As a result, 45 failure patterns were found. Rules with a reliability of up to 79% were obtained. In addition, spare part clusters with a support value of approximately 40% were created. With this valuable information, businesses are able to investigate root causes, take precautions against future failures, make predictions about the spare parts that will be needed, and develop joint maintenance planning and inventory management policies. [ABSTRACT FROM AUTHOR]

Italy offers a cultural heritage of considerable value that needs to be protected. Indeed, natural deterioration linked to the passage of time affects ancient artifacts and buildings. Sometimes, the deterioration compromises the functionality of cultural assets, pushing them toward decay. In this scenario, effective intervention seems impossible on the various critical points because of the wide variability of factors involved and the wide range of possible treatments. However, the spread of low-cost technologies has led to the possibility of having different devices and sensors able to communicate and interact with each other and humans: the Internet of Things (IoT). In this scenario, the IoT paradigm makes it possible to map reality by defining a coherent virtual representation (Digital Twin), which could help preserve Cultural Heritage. This work introduces an IoT-based system combining monitoring, predictive maintenance, and decision-making regarding the implementable interventions for protecting cultural heritage buildings. For this purpose, deep and machine learning techniques allow for the detection and classification of damages on specific materials. The experimental phase consists of two phases: the first aims to evaluate the accuracy of the proposed architecture, and the second exploits a prototype capable of interacting with expert users. The results of the experimental campaign are promising. [ABSTRACT FROM AUTHOR]

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future. [ABSTRACT FROM AUTHOR]

The advent of 6G technology will transforms aviation, particularly in the realm of aircraft health monitoring systems (AHMSs). This paper explores the transformative potential of 6G in enhancing real-time data exchange, predictive maintenance, and overall communication efficiency within the aviation sector. By using ultra-fast data transmission, low latency, and advanced AI integration, 6G enables the development of a unified AHMS architecture that significantly improves aircraft safety, operational efficiency, and reliability. The proposed eight-layer AHMS model, incorporating digital twins, federated learning, and edge computing, showcases how 6G can revolutionize aircraft maintenance by providing continuous, real-time monitoring and decision-making capabilities. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern aircraft systems necessitates advanced monitoring solutions to ensure operational safety and efficiency. Traditional aircraft health monitoring systems (AHMS) often rely on reactive maintenance strategies, detecting only visible faults while leaving underlying issues unaddressed. This gap can lead to critical failures and unplanned downtime, resulting in significant operational costs. To address this issue, this paper proposes the integration of artificial intelligence (AI) and blockchain technologies within an enhanced AHMS, utilizing the iceberg model as a conceptual framework to illustrate both visible and hidden defects. The model highlights the importance of detecting and addressing issues at the earliest possible stages, ensuring that hidden defects are identified and mitigated before they evolve into significant failures. The rationale behind this approach lies in the need for a predictive maintenance system capable of identifying and mitigating hidden risks before they escalate. Key tasks completed in this study include: a comparative analysis of the proposed system with existing monitoring solutions, the selection of AI algorithms for fault prediction, and the development of a blockchain-based infrastructure for secure, transparent data sharing. The evolution of AHMS is discussed, emphasizing the shift from traditional monitoring to advanced, predictive, and prescriptive maintenance approaches. This integrated approach demonstrates the potential to significantly improve fault detection, optimize maintenance schedules, and enhance data security across the aviation industry. [ABSTRACT FROM AUTHOR]

Optimizing energy consumption is an important aspect of industrial competitiveness, as it directly impacts operational efficiency, cost reduction, and sustainability goals. In this context, anomaly detection (AD) becomes a valuable methodology, as it supports maintenance activities in the manufacturing sector, allowing for early intervention to prevent energy waste and maintain optimal performance. Here, an AD-based method is proposed and studied to support energy-saving predictive maintenance of production lines using time series acquired directly from the field. This paper proposes a deep echo state network (DeepESN)-based method for anomaly detection by analyzing energy consumption data sets from production lines. Compared with traditional prediction methods, such as recurrent neural networks with long short-term memory (LSTM), although both models show similar time series trends, the DeepESN-based method studied here appears to have some advantages, such as timelier error detection and higher prediction accuracy. In addition, the DeepESN-based method has been shown to be more accurate in predicting the occurrence of failure. The proposed solution has been extensively tested in a real-world pilot case consisting of an automated metal filter production line equipped with industrial smart meters to acquire energy data during production phases; the time series, composed of 88 variables associated with energy parameters, was then processed using the techniques introduced earlier. The results show that our method enables earlier error detection and achieves higher prediction accuracy when running on an edge device. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PM) is a data-driven approach to performing proactive maintenance by analyzing the condition of the equipment in any industrial setting. The high-precision sensors are widely adapted to meticulously analyze critical maintenance conditions using such a datadriven approach. In a similar context, a fiber brag grating (FBG) sensor is a passive and high-precision sensor that is widely used in industries where conventional sensors are not preferred. Broadly, this article presents four sub elements of the proposed integrated system such as the design of the sensor element, signal processing scheme (SPS), machine learning (ML) model for predicting anomalies, and decision support system (DSS) to suggest maintenance actions. Also, this article highlights an experimental case study on vibration monitoring and analysis of real-time signals for making proactive maintenance decisions. An FBG vibration sensor of center wavelength 1,550 nm is designed and utilized to acquire real-time vibration signatures of a rotating machine under test. A piezoelectric vibration sensor is used with the FBG sensor to compare the vibration response obtained during the test. Pre-processing of raw signals is performed using a moving average filter (MAV) followed by a low pass filter to nullify the effect of noise. To obtain proactive maintenance decisions, a DSS model is prepared by considering the processed vibration signatures. Various maintenance conditions are tested during the experimental analysis and detailed results analysis are presented. [ABSTRACT FROM AUTHOR]

Maintaining the condition of a vessel and its equipment guarantees the scheduled completion of voyages and the safety of the crew. This paper presents condition monitoring techniques for early detection of faults related to piston rings in remote cylinders of two-stroke marine diesel engines. Operational sensor data from the main engine of a container ship are provided by a shipping company. A graphical approach complimented by correlation heatmaps and feature importance from gradient boosting trees are used for feature selection. Support Vector Machine, Random Forest and Extreme Gradient Boosting Trees are tested for residual generation from the nominal behavior. The residual time series gives a good indication of the degradation of the system and can be used for alarm raising under strict rules. It is proven that the proposed method could alert the engine crew of a change in the condition of the piston rings much earlier than existing methods. [ABSTRACT FROM AUTHOR]

The integration of Artificial Intelligence (AI)-driven predictive maintenance (PM) into the management of medical 3D printing machines promises a revolutionary advancement in operational efficiency and reliability. Traditional maintenance approaches often fail to optimize machine uptime and reduce costs effectively. AI and Machine Learning (ML) techniques, however, enable a proactive maintenance strategy by predicting potential failures and facilitating timely interventions. This ensures continuous high-quality production, which is critical for patient safety and effective medical care. AI-driven PM employs various methods, including supervised and unsupervised learning, neural networks, and natural language processing, to analyze vast datasets and perform intelligent diagnostics. The incorporation of advanced technologies such as digital twins, generative AI, collaborative robots, blockchain, and the Industrial Internet of Things (IIoT) further enhances fault detection, transparency, and efficiency in maintenance processes. The benefits of this approach include minimized downtime, cost savings, improved safety, and increased equipment reliability. As AI technology advances, its role in PM will become more sophisticated, establishing new benchmarks for the operation and management of 3D printing machines. This proactive maintenance strategy not only improves performance but also provides a competitive advantage in the healthcare sector. [ABSTRACT FROM AUTHOR]

Predictive maintenance has gained increasing importance recently due to the potential for significant losses resulting from unexpected machine failures. Additionally, the lengthy process of manufacturing and shipping components for repairs exacerbates the situation. Therefore, this study aims to introduce a method for enhancing time series classification performance by integrating MINImally RandOm Convolutional KErnel Transform (MiniRocket) and a genetic algorithm (GA)-based ensemble method for time series classification in the context of predictive maintenance. This study lies in the utilization of MiniRocket for feature extraction from time series data, which significantly reduces computational time compared to traditional methods. Additionally, the application of GA to ensemble learning of homogenous tree-based models introduces a unique strategy for improving classification accuracy and stability. In addition to using benchmark datasets, this study addresses a real-world problem provided by a prominent wire and cable company in Taiwan. Based on experimental results involving a wire drawing machine, it is evident that employing the GA within the ensemble learning method yields superior performance compared to using individual algorithms. This improvement is consistent across both benchmark and case study datasets, in terms of four performance indicators. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä's Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI's potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations. [ABSTRACT FROM AUTHOR]

This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework that continuously observes the induction motor, providing essential support to maintenance personnel. The motor operates under a range of environmental and operational conditions, including temperature fluctuations, rotational speeds, and mechanical loads. These variations can obscure the current time series data, potentially masking signs of actual damage and hindering effective damage detection. To tackle this issue, the proposed framework utilizes artificial intelligence (AI) technology, specifically the well-established autoencoder, in conjunction with the Mahalanobis statistical distance. This approach accounts for the diverse operating conditions during the training phase, allowing it to model complex, non-linear relationships and effectively differentiate between normal and anomalous states. The framework is integrated into a decision support platform designed for real-time operations in maritime settings, offering a sophisticated system architecture that aims to align advanced damage detection methodologies with the maritime industry's need for real-time, user-friendly solutions. [ABSTRACT FROM AUTHOR]

In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence-based tools is having a big impact on industry. In this context, the maintenance operations of important assets and industrial resources are changing, both from a theoretical and a practical perspective. Namely, conventional maintenance reacts to faults and breakdowns as they occur or schedules the necessary inspections of systems and their parts at fixed times by using statistics on component failures, but this can be improved by a predictive maintenance based on the real component's health status, which is inspected by appropriate sensors. In this way, maintenance time and costs are saved. Improvements can be achieved even in the marine industry, in which complex ship propulsion systems are produced for operation in many different scenarios. In more detail, data-driven models, through machine learning (ML) algorithms, generate the expected values of monitored variables for comparison with real measurements on the asset, for a diagnosis based on the difference between expectations and observations. The first step towards realization of predictive maintenance is choosing the ML algorithm. This selection is often not the consequence of an in-depth analysis of the different algorithms available in the literature. For that reason, here the authors propose a framework to support an initial implementation stage of predictive maintenance based on a benchmarking of the most suitable ML algorithms. The comparison is tested to predict failures of the oil circuit in a diesel marine engine as a case study. The algorithms are compared by considering not only the mean squared error between the algorithm predictions and the data, but also the response time, which is a crucial variable for maintenance. The results clearly indicate the framework well supports predictive maintenance and the prediction error and running time are appropriate variables to choose the most suitable ML algorithm for prediction. Moreover, the proposed framework can be used to test different algorithms, on the basis of more performance indexes, and to apply predictive maintenance to other engine components. [ABSTRACT FROM AUTHOR]

This paper presents a comparative analysis of deep learning techniques for anomaly detection and failure prediction. We explore various deep learning architectures on an IoT dataset, including recurrent neural networks (RNNs, LSTMs and GRUs), convolutional neural networks (CNNs) and transformers, to assess their effectiveness in anomaly detection and failure prediction. It was found that the hybrid transformer-GRU configuration delivers the highest accuracy, albeit at the cost of requiring the longest computational time for training. Furthermore, we employ explainability techniques to elucidate the decision-making processes of these black box models and evaluate their behaviour. By analysing the inner workings of the models, we aim at providing insights into the factors influencing failure predictions. Through comprehensive experimentation and analysis on sensor data collected from a water pump, this study contributes to the understanding of deep learning methodologies for anomaly detection and failure prediction and underscores the importance of model interpretability in critical applications such as prognostics and health management. Additionally, we specify the architecture for deploying these models in a real environment using the RAI4.0 metamodel, meant for designing, configuring and automatically deploying distributed stream-based industrial applications. Our findings will offer valuable guidance for practitioners seeking to deploy deep learning techniques effectively in predictive maintenance systems, facilitating informed decision-making and enhancing reliability and efficiency in industrial operations. [ABSTRACT FROM AUTHOR]

In the chemical industry, stable reactor operation is essential for consistent production. Motor failures can disrupt operations, resulting in economic losses and safety risks. Traditional monitoring methods, based on human experience and simple current monitoring, often need to be faster and more accurate. The rapid development of artificial intelligence provides powerful tools for early fault detection and maintenance. In this study, the Hotelling T2 index is used to calculate the root mean square values of the normal motor's x, y, and z axes. A long short-term memory (LSTM) model creates a trend model for the Hotelling T2 index, determining an early warning threshold. Current anomaly detection follows the ISO 10816-1 standard, while future anomaly prediction uses the T2-LSTM trend model. Validated at a chemical plant in Southern Taiwan, the method shows 98% agreement between the predicted and actual anomalies over three months, demonstrating its effectiveness. The T2-LSTM model significantly improves the accuracy of motor fault detection, potentially reducing economic losses and improving safety in the chemical industry. Future research will focus on reducing false alarms and integrating more sensor data. [ABSTRACT FROM AUTHOR]

The purpose of this research is to develop an innovative software framework with AI capabilities to predict the quality of automobiles at the end of the production line. By utilizing machine learning techniques, this framework aims to prevent defective vehicles from reaching customers, thus enhancing production efficiency, reducing costs, and shortening the manufacturing time of automobiles. The principal results demonstrate that the predictive quality inspection framework significantly improves defect detection and supports personalized road tests. The major conclusions indicate that integrating AI into quality control processes offers a sustainable, long-term solution for continuous improvement in automotive manufacturing, ultimately increasing overall production efficiency. The economic benefit of our solution is significant. Currently, a final test drive takes 10–30 min, depending on the car model. If 200,000–300,000 cars are produced annually and our data prediction of quality saves 10 percent of test drives with test drivers, this represents a minimum annual saving of 200,000 production minutes. [ABSTRACT FROM AUTHOR]

The rapid booming of information and communication technologies (ICT) and artificial intelligence has promoted the development of maintenance practices in the building industry towards a data-driven paradigm, of which the digital twin (DT) serves as the fundamental framework to strengthen data transit ability and interoperability. Among the state-of-the-art approaches in the maintenance industry, predictive maintenance (PdM) is a prominent approach by anticipating unexpected failures and unscheduled maintenance tasks. However, most current PdM frameworks are facility-specific, lacking generality and scalability. Besides, existing solutions mainly concentrate on condition monitoring and fault identification rather than failure prediction. Moreover, good prediction results rely heavily on sufficient labelled data sets, which are costly and labour-intensive to collect. To address these issues, the author developed a unified PdM framework for the building industry from the DT perspective. Next, a novel failure prediction method utilising the Semi-supervised Generative Adversarial Network (GAN) has been proposed in this article, which makes effective utilisation of both labelled and unlabelled data. Finally, an online platform has been developed to present the monitoring and prediction information. Experimental findings show the effectiveness and superiority of the proposed method for failure prediction through public data sets of building facilities. [ABSTRACT FROM AUTHOR]

The concept of digital twins has gained significant attention in recent years due to its potential to transform various industries, including renewable energy. Digital twins involve the creation of virtual models that mirror the behaviour and characteristics of real-world physical systems. In the context of solar plants, digital twins have emerged as a promising tool to enhance performance monitoring, predictive maintenance, and overall operational efficiency. Digital twin engineering, characterized by its dynamic data modelling of industrial assets, offers a disruptive technology capable of adapting to real-time changes in the environment and operations. This living model can predict future infrastructure behaviour and proactively identify potential issues within the physical system. The article highlights the essential components of the digital twin ecosystem, such as sensor technologies, the Industrial Internet of Things, simulation, modelling, and machine learning, underscoring their relevance in predictive maintenance applications. This review provides an in-depth review of the development and application of digital twins for predicting and mitigating faults and defects in solar power plants. It opens with a look at current developments, underlining the rising focus on digital twins for optimizing solar farms. It begins with an overview of existing solutions in the field, highlighting the growing interest in leveraging digital twin technology to enhance solar plant operations. Additionally, the article outlines the implementation stage of a prototype digital twin for a solar power plant. [ABSTRACT FROM AUTHOR]

This study addresses the integration of machine learning (ML) with supervisory control and data acquisition (SCADA) systems to enhance predictive maintenance and operational efficiency in oil well monitoring. We investigated the applicability of advanced ML models, including Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Momentum LSTM (MLSTM), on a dataset of 21,644 operational records. These models were trained to predict a critical operational parameter, FlowRate, which is essential for operational integrity and efficiency. Our results demonstrate substantial improvements in predictive accuracy: the LSTM model achieved an R2 score of 0.9720, the BiLSTM model reached 0.9725, and the MLSTM model topped at 0.9726, all with exceptionally low Mean Absolute Errors (MAEs) around 0.0090 for LSTM and 0.0089 for BiLSTM and MLSTM. These high R2 values indicate that our models can explain over 97% of the variance in the dataset, reflecting significant predictive accuracy. Such performance underscores the potential of integrating ML with SCADA systems for real-time applications in the oil and gas industry. This study quantifies ML's integration benefits and sets the stage for further advancements in autonomous well-monitoring systems. [ABSTRACT FROM AUTHOR]

When a wheel passes over a rail surface irregularity, the resulting vehicle excitations lead to the application of additional system forces to both the track and the vehicle. These forces contribute to an accelerated track geometry deterioration, which in turn results in increased costs. In a recent paper, a clear correlation between the presence of rail irregularities and poor track geometry quality was demonstrated. Rail surface irregularities thereby were quantified by raw data of a chord-based optical measurement system mounted on the regular track recording vehicle in Austria. This paper deals with deconvolution of the recorded data in order to guarantee irregularity quantification without any distortion. Two different deconvolution approaches are developed and validated by additional measurements. Using the deconvoluted data, previously published evaluations were repeated, and the impact of using deconvoluted data instead of chord values was analysed. The correlation between short-wave effects and track geometry quality can not only be confirmed; it is even stronger than predicted by the chord data. The results of the analysis demonstrate that irregularities with amplitudes exceeding 0.08 mm contribute to an accelerated deterioration in track geometry. Amplitudes of a greater severity result in track geometry levels that are up to 120% inferior to the average. [ABSTRACT FROM AUTHOR]

The quality of resistance spot welding (RSW) joints is strongly affected by the condition of the electrodes. This work develops a machine learning-based tool to automatically assess the influence of electrode wear on the quality of RSW welds. Two different experimental campaigns were performed to evaluate the effect of electrode wear on the mechanical strength of spot welds. The resulting failure load of the joints has been used to define the weld quality classes of the machine learning tool, while data from electrode displacement and electrode force sensors, embedded in the welding machine, have been processed to identify the predictors of the tool. Some machine learning algorithms have been tested. The most performing algorithm, i.e., the neural network, achieved an accuracy of 90%. This work provides important theoretical and practical contributions. First, the decreasing thermal expansion of the weld nugget as the electrode degradation advances results in a strong correlation between the difference of the maximum displacement value and the last value recorded during the welding and the relative failure load. Then, this work offers a practical decision support tool for manufacturers. In fact, the automatic detection of low-quality welds allows to reduce or eliminate unnecessary redundant welds, which are performed to compensate for the uncertainty of electrode wear. This leads to savings in time, energy, and resources for manufacturers. Finally, general recommendations for the timing of redressing or replacing the electrode are provided in the manuscript based on the company willingness to accept some non-compliant welds or not. [ABSTRACT FROM AUTHOR]

This study explores the transformative impact of Artificial Intelligence (AI) on sustainable IT asset lifecycle management (ITALM). The research examines AI-driven approaches compared to traditional methods, focusing on key areas such as predictive maintenance, asset utilization optimization, and e-waste reduction. The findings indicate significant improvements in operational efficiency, cost reduction, and sustainability. The study provides valuable insights for organizations aiming to integrate AI into their ITALM strategies to achieve sustainability objectives. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value. [ABSTRACT FROM AUTHOR]

In this paper, the concept of hybrid predictive maintenance for a single industrial machine is presented. A review of the solutions in the area of machine maintenance (especially predictive maintenance) which have been described in the literature is provided. The assumptions of the hybrid predictive maintenance model for modules, machines, or systems are presented. The methods used within the developed methodology are described. This includes the use of diagnostic data, experience, and a mathematical model. A case study of an industrial machine on which a system for collecting diag-nostic data has been pilot-implemented, using, among others, vibration sensors and drive system pa-rameters for damage detection is presented. The registered data can be used to precisely determine the time of upcoming failure after detection of the characteristic symptoms resulting from component wear In addition, an analysis of the durations of correct operation and failure events was performed and indicators describing these values were determined. The values of the aforementioned indicators were determined based on empirical data and described using a gamma distribution. The objective of the research was to prepare, implement and draw conclusions on a hybrid predictive maintenance model. A real industrial machine was used in the research study. The hybrid predictive maintenance model presented in this paper enables the use of data of different types (diagnostic, historical and mathemat-ical model-based) in scheduling machine downtime for maintenance actions. On the basis of the re-search conducted, it was determined which machine operating parameters are characterised by varia-bility that enables the detection of upcoming failure. This allows for precise planning of maintenance activities and minimization of unplanned downtime. [ABSTRACT FROM AUTHOR]

The problem of achieving a good maintenance plan is well-known in the modern industry. One of the most promising approaches is predictive maintenance, which schedules interventions based on predictions made by collecting and analyzing data from the process. However, to the best of the authors' knowledge, this approach is still not widespread and known enough, and particularly, the real-case scenarios of its application appear not exhaustive. To contribute to fill this gap, this work proposes a digital twin (DT), which performs a predictive maintenance approach for a conveyor belt within a real-case scenario with the overall goal of predicting faults during normal belt operations. Specifically, the core of the implemented DT is a model that analyzes the data collected by various sensors distributed along the conveyor belt. In turn, this model exploits a machine learning-based algorithm that predicts the insurgence of faults. The tests of the developed solution, conducted within a real scenario, demonstrated good precision and accuracy in identifying the fault status and also in a time deemed acceptable for the involved stakeholders. [ABSTRACT FROM AUTHOR]

Assessing the vibration signature produced by a rotating component of the wind turbine enables the identification of operational conditions and the detection of potential faults at an early stage. The main purpose is to enhance the sustainability of wind turbines while increasing the lifespan and uptime of their operational systems. This vibration analysis is based on the processing of the signal provided by sensors, which often incorporates piezoelectric transducers. This paper evaluates the consequences of employing piezoelectric sensors used for vibration measurement on electrical machines integrated into wind turbines by conducting a life cycle assessment (LCA). The widespread use of piezoelectric materials is due to their high sensitivity to vibrations, although their selection is also influenced by regulatory restrictions. This research focuses on the environmental impact of piezoelectric accelerometers used commonly in condition monitoring systems. The collected literature data on the manufacturing processes are inputted into the LCA model which is powered by the Ecoinvent 3 database. The impact assessment is carried out using the European ILCD 2011 Midpoint+ method by calculating the unique scores of the selected impact categories. The results are presented and discussed in terms of environmental indicators, as well as ecological recommendations on the design. [ABSTRACT FROM AUTHOR]

This paper explores and discusses the revolutionary applications of digital twin technology in space environments and its profound impact on future space exploration activities. Originating from a proposal by the National Aeronautics and Space Administration (NASA) in 2002, digital twin technology aims to enhance the safety and reliability of space missions by creating precise virtual models. As the technology has evolved, its applications have successfully expanded beyond aerospace to include Industry 4.0, healthcare, and urban management, demonstrating remarkable cross-industry adaptability and broad impact. In space applications, digital twin technology can not only improve spacecraft design and maintenance processes but also enhance the efficiency of mission planning and execution. It plays a crucial role in astronaut training and emergency response as well. Particularly in extreme space conditions, this technology provides real-time monitoring and fault prediction, significantly enhancing mission safety and success rates. However, despite its recognized potential, the implementation of digital twins in space environments faces numerous challenges, including data transmission delays, model accuracy, and the design of user–system interactions. In the future, as artificial intelligence (AI) and machine learning (ML) technologies become mature and integrated, the digital twin will play a more central role in space missions, especially in remote operations, complex system management, and deep space exploration. This article is to overview key technical features, application examples, and challenges of digital twin technology, aiming to provide a comprehensive reference framework for researchers and developers while inspiring further in-depth studies and innovative applications. [ABSTRACT FROM AUTHOR]

The article introduces datasets representing piston pump failures along with the experimental evaluation of various machine learning classification models. It starts with a detailed description of three classification datasets consisting of three different levels of valve plate damages and signals recorded from sensors used in classical hydraulic systems (pressure, temperature, flow). The obtained datasets consist of 100k (Failure 1), 30k (Failure 2) and 30k (Failure 3) samples and eight attributes. Then a broad range of classifiers are evaluated including three ensemble models based on decision trees: Random Forest, Gradient-Boosted Trees, and Rotation Forest, as well as the kNN algorithm and a neural network. The analysis showed that neural networks achieved the highest prediction accuracy, enabling a prediction accuracy level of 89%. The kNN algorithm ranked second, and tree-based algorithms performed 4% worse than the neural network. Next, the attribute importance analysis revealed that leak flow, pressure output, pressure of the leak line, and oil temperature are the most important parameters for accurate predictions. Additionally, the research includes a sensitivity analysis of the best classifier to verify the impact of sensor measurements or other noise indicators on the prediction model performance. The analysis indicates a 5% margin of measurement quality. [ABSTRACT FROM AUTHOR]

AbstractAdvanced reactor designers are looking to maximize the system capacity factor to make advanced reactors more economically competitive and meet the projected energy demand. To achieve this goal, we propose a Dynamic Operation and Maintenance Optimization (DyOMO) framework to perform system-level predictive maintenance (PdM) using a dynamic Bayesian network and component-specific PdM using deep neural networks. At the system level, DyOMO detects the presence of anomalous phenomena, determines the most influential degradation mode, and estimates the remaining useful life (RUL) distribution for the system. At the component level, DyOMO summarizes the health state of key system components, determines the presence of an anomaly using a feedforward neural network, and predicts component RUL using a Bayesian neural network. To evaluate the overall performance of DyOMO, normal operations of a Pebble-Bed High-Temperature Gas-cooled Reactor (PB-HTGR) were simulated with realistic component degradation for the steam turbine and steam generator. Across the 20 independent reactor life simulations, it was found that maintenance was always performed before any safety limits were violated and before a component failed. Specifically, the system-level PdM suggested maintenance on the steam generator once the steam pressure approached its safety limit, and the component-specific PdM suggested maintenance on the steam turbine once the turbine blade hardness degraded. The results indicate that through the continuous monitoring of the system and individual components, the DyOMO framework improves safety and increases the availability of the reactor when compared to traditional maintenance philosophies. [ABSTRACT FROM AUTHOR]

This research paper presents the development and implementation of an integrated condition monitoring and fault detection system for AC induction motors using a combination of sensors, GSM communication, and a cloud-based Internet of Things (IoT) platform. The proposed system aims to enhance industrial motors' operational reliability and efficiency by providing real-time data monitoring and early fault detection. The key components of the system include temperature, vibration, current, voltage, and speed sensors, which are strategically placed to gather critical motor performance data. These sensors feed data to an Arduino-based control unit responsible for sensor data acquisition and processing. To ensure timely response to anomalies, the system is equipped with an alarm system and GSM alerts, which notify designated personnel in case of abnormal motor behavior. Moreover, the paper incorporates remote monitoring capabilities, enabling users to access motor health data and real-time status from a distance. Historical data is also stored for analysis and comparison through the integration of a cloud-based Blynk-IoT platform. Additionally, the system facilitates RPM control and utilizes relay modules for seamless motor control and protection. The proposed system was tested and validated using Proteus for circuit diagram simulation and Arduino for sensor coding. The results demonstrate its effectiveness in detecting abnormal motor behavior and its potential to prevent catastrophic failures by enabling predictive maintenance. The proposed system successfully detects and displays abnormalities in important parameters like vibration, temperature, speed, three-phase currents, and voltages with 99% accuracy. [ABSTRACT FROM AUTHOR]

Data science can create value by extracting structured and unstructured data using an appropriate algorithm. Data science operations have undergone drastic changes because of accelerated deep learning progress. Deep learning is an advanced process of machine learning algorithm. Its simple process of presenting data to the system is sharply different from other machine learning processes. Deep learning uses advanced analytics to solve complex problems for accurate business decisions. Deep leaning is considered a promising area for creating additional value in firms' productivity and sustainability as they develop their smart manufacturing activities. Deep learning capability can help a manufacturing firm's predictive maintenance, quality control, and anomaly detection. The impact of deep learning technology capability on manufacturing firms is an underexplored area in the literature. With this background, the purpose of this study is to examine the impact of deep learning technology capability on manufacturing firms with moderating roles of deep learning related technology turbulence and top management support of the manufacturing firms. With the help of literature review and theories, a conceptual model has been prepared, which is then validated with the PLS-SEM technique analyzing 473 responses from employees of manufacturing firms. The study shows the significance of deep learning technology capability on smart manufacturing systems. Also, the study highlights the moderating impacts of top management team (TMT) support as well as the moderating impacts of deep learning related technology turbulence on smart manufacturing systems. [ABSTRACT FROM AUTHOR]