bluuebunny/crossref_metadata_2025 huggingface dataset:
Data-Driven Prognostics with Multi-Layer Perceptron Particle Filter: a Cross-Industry Exploration
Authors: Canceliere Francesco , Girard Sylvain , Bourinet Jean-Marc
Published: June 2024

The integration of particle or Kalman filters with machine learning tools like support vector machines, Gaussian processes, or neural networks has seen extensive exploration in the context of prognostic and health management, particularly in model-based applications. This paper focuses on the Multi-Layer Perceptron Particle Filter (MLP-PF), a data-driven approach that harnesses the non-linearity of MLP to describe degradation trajectories without relying on a physical model. The Bayesian nature of the particle filter is utilized to update MLP parameters, providing flexibility to the method and accommodating unexpected changes in the degradation behavior. To showcase the versatility of MLP-PF, this work demonstrates its seamless integration into diverse use cases, such as lithium-ion battery analysis, virtual health monitoring for turbofans, and the assessment of fatigue crack growth. We illustrate how it effortlessly accommodates various contexts through slight parameter modifications. Adjustment includes variation in the number of neurons or layers in the MLP, threshold adjustments, initial training refinements and the adaptation of the process noise. Addressing different degradation processes across these applications, MLP-PF proves its adaptability and utility in various contexts. These findings highlight the method’s versatility in adapting to diverse use cases and its potential as a robust prognostic tool across various industries. MLP-PF offers a practical and efficient means of estimating remaining useful life and predicting degradation in complex systems, with implications for advancing prognostic tools in diverse applications.
Development of Anomaly Detection Technology Applicable to Various Equipment Groups in Smart Factory
Authors: PARK KIWON , Lee Myoung Gyo , Cho Sung Yong , Jang Yoon , Choi Young Tae
Published: June 2024

This study delves into the creation of anomaly detection technology applicable to a range of equipment groups within smart factories. This advanced technology uses high-performance MEMS vibration sensors, edge CMS devices, and PHM platforms to tackle issues such as data imbalance, learning model limitations, complex equipment operating patterns, and real-time processing. It also addresses central server concentration, data cycling problem, various equipment classification, and algorithm operation problems that can arise when implementing systems in the field. Using AI-based vibration detection algorithms, data can be collected at high sampling rates and analyzed in real-time through edge computing, minimizing latency and mitigating server capacity issues compared to cloud-based analytics. The system continually monitors and learns standard performance data from equipment to provide practical solutions that minimize equipment failures and downtimes. The results of this study are impressive, as it has successfully developed anomaly detection framework and PHM systems that are expected to enhance the efficiency and sustainability of smart factories. Furthermore, the study aims to showcase and improve the effectiveness of predictive maintenance in both domestic and international automotive factory production lines. This revolutionary technology will be a key component in smart and software-defined factories and help companies achieve intelligent automation.
Maintenance decision-making model for gas turbine engine components
Authors: Kim Hongseok , Kim Do-Nyun
Published: June 2024

When designing gas turbine engine components, the inspection and maintenance (I&M) plan is prepared using the safe life. However, the I&M plan determined using safe life may be costly since all components are replaced at designated life. Therefore, it is important to make maintenance decisions considering the time-dependent deterioration process of gas turbine engine components for a cost-saving I&M plan. In this study, we proposed a maintenance decision-making model for gas turbine engine components based on a partially observed Markov decision process (POMDP). Using dynamic Bayesian networks, a decision-making model integrating a reliability analysis model, and a decision model for I&M planning was constructed. The signal amplitude data resulting from non-destructive inspection according to operation hour was used as partially observed data. The total cost obtained from the proposed model were compared with the results using a fixed I&M plan. The proposed model resulted in more cost-effectiveness I&M planning within affordable risk levels by considering the interaction between risk cost and I&M cost.
Characterizing Damage in Wind Turbine Mooring Using a Data-Driven Predictor Model within a Particle Filtering Estimation Framework
Authors: Kumar Rohit , Thakur Ananay , O A Shereena , Keprate Arvind , Sen Subhamoy
Published: June 2024

Floating Offshore Wind Turbines (FOWT) represent a promising solution to renewable energy challenges, yet effective maintenance remains critical for cost management. Traditional machine learning (ML) approaches for detecting FOWT damage often rely on extensive real-world data, which can be impractical and economically unfeasible. Alternatively, stochastic filtering-based time-domain approaches leverage physical understanding through dynamic models, typically finite element models. However, these methods are hindered by excessive simulation calls within the recursive filtering frameworks. This study proposes a novel filtering-based approach that replaces the computationally intensive process model with a Deep Neural Network (DNN) surrogate, addressing the aforementioned limitations. The proposed approach utilizes synthetic data generated from the high-fidelity calibrated OpenFAST model of FOWT dynamics to train a DNN toward learning the dynamic evolution of the FOWT conditioned on the current health state. By offering a computationally efficient representation of system dynamics conditioned on health state, this approach allows for real-time damage detection and interpretable information on damage severity within a stochastic inverse estimation framework, specifically employing Particle Filtering in this study. This approach contrasts with traditional black-box ML-based methods, which typically struggle to provide interpretable information on damage characteristics. Extensive numerical investigations on damaged FOWT mooring lines demonstrate this approach's practical applicability and superiority over traditional ML-based methods. Eventually, integrating explainable ML models within the filtering framework induces promptness in detection without sacrificing transparency.
A Comparative Study of Semi-Supervised Anomaly Detection Methods for Machine Fault Detection
Authors: Neupane Dhiraj , Bouadjenek Mohamed Reda , Dazeley Richard , Aryal Sunil
Published: June 2024

Industrial automation has extended machines’ runtime, thereby raising breakdown risks. Machine breakdowns not only have economic and productivity consequences, but they can also be fatal. Thus, the early detection of fault signs is essential for the safe and uninterrupted operation of machinery and its maintenance. In the last few years, machine learning has been widely used in machine condition monitoring. Most existing approaches rely on supervised learning techniques, which face challenges in real-world scenarios due to the lack of enough labelled fault data. Additionally, models trained on historical fault data might struggle to detect new and unseen faults accurately in the future. Therefore, this research uses semi-supervised Anomaly Detection (AD) techniques to detect abnormal patterns in machines’ vibration signals. As semi-supervised techniques are trained on normal data only, they do not require faulty samples and abnormal patterns are detected based on their deviations from the learned normal pattern. We compared the effectiveness of seven state-of-the-art AD methods, ranging from traditional approaches such as isolation forest and local outlier factor to more recent Deep Learning (DL) approaches based on autoencoders. We evaluated the effectiveness of different feature types extracted from the raw vibration signals, including simple statistical features like kurtosis, mean, peak-to-peak, and more complex representations like the scalogram images. Our study on three public datasets, with unique challenges, shows that the traditional methods based on simple statistical analysis have shown comparable and sometimes superior performance to more complex DL approaches. The use of traditional approaches offers simplicity and lower computational needs. Thus, our study recommends that future researchers start with the traditional approaches first and then jump to DL methods if necessary.
Leveraging Generative and Probabilistic Models for Diagnostics of Cyber-Physical Systems
Authors: Alvaropiedrafita  , Barbini Leonardo
Published: June 2024

A critical task for system operators is the precise identification of the root causes underlying an error situation. This identification is fundamental in deciding optimal maintenance actions, such as replacing a component versus calibrating it. However, the actual causes of an error are often neither measured nor unique. The measured quantities are the result of complex interactions between different error causes and system variables. Root cause identification in this context becomes a matter of inferring hidden causes from their measurable effects. This challenge is notably pronounced in cyber-physical systems comprising control loops. Control mechanisms, integral to maintaining system performance, introduce a layer of complexity in diagnostics and ultimately  complicate the isolation of the underlying causes of errors. To address this challenge, we introduce a two-step approach to derive the hidden causes as a statistical inference task. First, we develop a generative model leveraging existing control software and expert-based insights into the mechanisms of errors, i.e., a simulator of synthetic data given some hidden error causes. Then, we transform the generative model into a probabilistic program on which statistical inference can be executed within a probabilistic programming language framework. This inference effectively estimates the hidden causes given some measured data from the system. Being intrinsically a statistical approach, these inferences come with a confidence interval. We applied this methodology to an industrial printer’s sheet transport belt, operating in a closed-loop configuration. Our approach successfully discerned the contributions of three distinct hidden causes to the belt’s deviation from its intended position. This paper highlights the efficacy of generative modeling followed by a probabilistic programming approach in unraveling complex interactions within cyber-physical systems for optimal maintenance.
Integrating Network Theory and SHAP Analysis for Enhanced RUL Prediction in Aeronautics
Authors: Alomari Yazan , Baptista Marcia , Andó Mátyás
Published: June 2024

The prediction of Remaining Useful Life (RUL) in aerospace engines is a challenge due to the complexity of these systems and the often-opaque nature of machine learning models. This opaqueness complicates the usability of predictions in scenarios where transparency is crucial for safety and operational decision-making. Our research introduces the machine learning framework that significantly improves both the interpretability and accuracy of RUL predictions. This framework incorporates SHapley Additive exPlanations (SHAP) with a surrogate model and Network Theory to clarify the decision-making processes in complex predictive models and enhance the understanding of the hidden pattern of features interaction. We developed a Feature Interaction Network (FIN) that uses SHAP values for node sizing and SHAP interaction values for edge weighting, offering detailed insights into the interdependencies among features that affect RUL predictions. Our approach was tested across 44 engines, showing RMSE values between 2 and 17 and NASA Scores from 0.2 to 1.5, indicating an increase in prediction accuracy. Furthermore, regarding interpretability the application of our FIN, revealed significant interactions among corrective speed and critical temperature points key factors in engine efficiency and performance.
Health-aware Control for Health Management of Lithium-ion Battery in a V2G Scenario
Authors: Spinola Felix Monica , Martinez-Molina John J. , Bérenguer Christophe , Kulkarni Chetan S. , Orchard Marcos E.
Published: June 2024

In response to the urgent need to combat climate change and reduce greenhouse gas emissions, the transition towards renewable energy sources such as solar and wind power is indispensable. However, the intermittent nature of these sources poses significant challenges to the stability of power grids. Battery Energy Storage Systems (BESS) offer a viable solution, and there is potential for Electric Vehicles (EVs) to serve as energy reservoirs, thereby bolstering grid stability through Vehicle-to-Grid (V2G) technology. While V2G holds promise, concerns persist regarding the longevity of batteries, particularly with the additional demand from charging and discharging cycles. To address these concerns, this study introduces a health-aware control strategy for V2G service scenarios. By employing feedback control mechanisms to adjust degradation rates, the strategy aims to effectively manage battery aging. Simulation outcomes of a V2G scenario with random input sources illustrate the efficacy of this proposed approach, demonstrating its potential applicability in practical settings where battery health needs to be managed. In summary, this research contributes to the advancement of health-aware strategies for an interconnected grid where electric vehicles participate as energy sources, with a primary focus on optimizing battery health management while fulfilling grid demands. Future efforts will concentrate on refining optimization strategies and integrating control methodologies with state estimators to ensure the performance of the approach on embedded battery health management systems.
Advancing Durability Testing in Automotive Component through Prognostics and Health Management (PHM) Integration
Authors: Song Jinwoo , Choi Junggyu , Shin Jeongmin , Oh Seungyoon , Hong Seok Hyun , Lee Yun Jong , Yoon Hae-Sung , Choi Joo-Ho
Published: June 2024

In automotive Powered Door Systems (PDS), the emergence of grinding and clicking noise over time is a common failure mode. This issue typically arises from design or assembly inconsistencies and intensifies due to wear or increased clearance at its component, becoming noticeable to passengers, and causing discomfort. Numerous automotive manufacturers conduct comprehensive durability tests to tackle such issues during the development. Conventional durability tests, however, rely on the manual effort such as visual and auditory inspection at regular intervals, hence, is subjective and inefficient. This study introduces a novel method by the prognostics and health management (PHM) approach to detect anomaly and assess its severity of the noise during the durability test of the PDS, which may improve the reliability of noise detection and reduces the test time by early termination using prognosis capability. The results demonstrate the potential, paving the way for its broader application across various domains to advance testing processes and reliability.
Maintenance Strategies for Sewer Pipes with Multi-State Degradation and Deep Reinforcement Learning
Authors: Jimenez-Roa Lisandro Arturo , Simão Thiago D. , Bukhsh Zaharah , Tinga Tiedo , Molegraaf Hajo , Jansen Nils , Stoelinga Marielle
Published: June 2024

Large-scale infrastructure systems are crucial for societal welfare, and their effective management requires strategic forecasting and intervention methods that account for various complexities. Our study addresses two challenges within the Prognostics and Health Management (PHM) framework applied to sewer assets: modeling pipe degradation across severity levels and developing effective maintenance policies. We employ Multi-State Degradation Models (MSDM) to represent the stochastic degradation process in sewer pipes and use Deep Reinforcement Learning (DRL) to devise maintenance strategies. A case study of a Dutch sewer network exemplifies our methodology. Our findings demonstrate the model's effectiveness in generating intelligent, cost-saving maintenance strategies that surpass heuristics. It adapts its management strategy based on the pipe's age, opting for a passive approach for newer pipes and transitioning to active strategies for older ones to prevent failures and reduce costs. This research highlights DRL's potential in optimizing maintenance policies. Future research will aim improve the model by incorporating partial observability, exploring various reinforcement learning algorithms, and extending this methodology to comprehensive infrastructure management.
Labeling Algorithm for Outer-Race Faults in Bearings Based on Load Signal
Authors: Bublil Tal , Taal Cees , Maljaars Bert , Klein Renata , Bortman Jacob
Published: June 2024

Rolling element bearings are essential components for the proper functioning of many types of rotating equipment. Diagnosing faults in bearings has traditionally been done using signal processing techniques inspired by physics, wherein acceleration signals are analyzed using time-frequency analysis methods. To study the effect of bearing damage on acceleration signals, experiments are typically performed aiming for a natural propagation of a spall. However, the extent of spall severity during the test remains uncertain. It is possible to disassemble and reassemble the bearing for visual inspection. Nevertheless, previous studies observed that the vibration signal would drastically change if this operation was conducted repeatedly, impacting the identification of trends in the acceleration signal. The objective of this study is to provide a method which can assist with labeling the spall size in endurance tests without the necessity of disassembling and reassembling the test rig. To address this issue, a new algorithm, based on the load cell signal was developed to assess the spall size using low-speed measurements. This algorithm enables the identification of the circumferential angle at which the rolling element interacts with the spall and is only carrying a partial load. The algorithm has been validated through visual inspections conducted during the experiment. This algorithm makes it possible to estimate the spall size without the need for visual inspection in subsequent experiments. A labeled endurance test contributes to a better understanding of spall propagation, such as the effect of speed, load, and material properties on the propagation speed. This study demonstrates how the load signal can be used for fault labeling with relatively simple and common techniques. This approach will enable the tackling of advanced and more complex problems in future endeavors, such as fault severity estimation and even prognosis.
A Review of Prognostics and Health Management in Wind Turbine Components
Authors: Cuesta Jokin , Leturiondo Urko , Vidal Yolanda , Pozo Francesc
Published: June 2024

Wind turbines (WTs) play an essential role in renewable energy generation, and ensuring their reliable operation is essential for sustainable energy production and reduction of levelized cost of energy. In this context, the field of prognostics and health management (PHM) is a powerful tool to predict and assess the health status of WT components, thereby enabling timely maintenance and reducing downtime. The study begins with an overview of WT components studied, including the blades, gearbox, generator, and bearings, and their common failure modes. For each component, various remaining useful life (RUL) estimation methods are explored, categorizing them into physics-based, data-driven, and hybrid methods. Despite the potential benefits, the application of PHM strategies in WTs is currently limited. Although PHM strategies have been present for years, their development in WTs remains a challenge. These key challenges are presented, including uncertainty management, integrating physical knowledge into models, variable operational conditions, data issues and system complexity.
A rolling bearing state evaluation method based on deep learning combined with Wiener process
Authors: Ta Yuntian , Wang Tiantian
Published: June 2024

As a key component of rotating parts, rolling bearings largely determine the operation safety of equipment. However, in practical applications, because the degradation trajectory of rolling bearings cannot be truly characterized, the existing model cannot accurately describe the degradation trajectory of rolling bearings, resulting in the running state of rolling bearings cannot be directly evaluated. Therefore, a method of rolling bearing state assessment based on deep learning combined with Wiener process is proposed in this paper. Firstly, a deep network model is constructed by deep learning to mine the degradation information of rolling bearings. Secondly, the mined degradation information is fused, and then the degradation indicator used to characterize the degraded trajectory of the rolling bearing is constructed. Then, based on Wiener process, the degradation model of rolling bearing is established to describe the degradation mode of rolling bearing. Finally, the constructed degradation indicator is input into the established degradation model to predict its RUL, and then the running state of the rolling bearing is evaluated.
Hybrid AI-Subject Matter Expert Solution for Evaluating the Health Index of Oil Distribution Transformers
Authors: Cathignol Augustin , Thuillie-Demont Victor , Baldi Ludovica , Micheau Laurent , Petitpretre Jean-Pierre , Ouberehil Amelle
Published: June 2024

Reliability of oil distribution transformers is paramount, ensuring a stable flow of electricity and shielding from potential fire hazards. The internal insulation system of these transformers utilizes a combination of oil and paper. As the oil circulates through the active part of the system, it collects gaseous and physical traces of existing or past defects or degradations, providing a holistic view of the transformer's health, and allowing for early detection of problems and predictive maintenance. While various and mainly data-driven methods have been developed to calculate a transformer health index from oil samples, they lack accuracy due to limited data. This paper proposes a novel hybrid approach that leverages both Artificial Intelligence and Subject Matter Expertise to enhance the health estimation of oil distribution transformers. Our methodology utilizes a substantial dataset exceeding 65,600 analyzed oil samples, coupled with the valuable knowledge of domain experts. This combined approach achieves an accuracy exceeding 95%, suitable for real-world industrial applications. Furthermore, we introduce a risk management feature that strengthens the ability to identify transformers at high risk of failure. Notably, the health index estimation is implemented as a semi-automatic process, retaining the "expert in the loop" principle for managing critical and ambiguous cases.
A Practical Example of Applying Machine Learning to a Real Turbofan Engine Issue: NEOP
Authors: Hrncirzdenek  , Hickenbottom Chris
Published: June 2024

There are high expectations for the use of Machine Learning algorithms in Engine Health Management, but the practical application for use with turbofan engines is often hindered by small sample sizes and noisy data.  This paper discusses a case in which Machine Learning techniques were combined with domain expertise to develop a classifier called Non-seal Erratic Oil Pressure (NEOP).  This classifier is used as an engineering tool to support manual review of engines flagged with Honeywell’s OPX (Oil Pressure Transducer) algorithm.  The purpose of the classifier is to assist a human in analyzing engine trend data from the HTF7000 turbofan engine, when the OPX algorithm identifies an engine with erratic oil pressure.  The NEOP history provides an additional data source when deciding if aft sump maintenance is needed to replace a worn carbon seal, or if the erratic signal is associated with some other cause.  The OPX algorithm has enabled the prevention and avoidance of costly unscheduled engine failures resulting in millions of dollars in documented savings, and the NEOP algorithm helps to ensure that the conclusions from the OPX process continue to result in the appropriate engines being identified for maintenance inspection and corrective action.
Enhancing gearbox condition monitoring using randomized singular value decomposition and K-nearest neighbor
Authors: Afia Adel , Soualhi Mocnef , Gougam Fawzi , Touzout Walid , Ait-Chikh Abdassamad , Meloussi Mounir
Published: June 2024

Efficient gear and bearing diagnosis has become a critical requirement across diverse industrial applications precisely due to their complex design and exposure to difficult operating conditions, which predispose them to frequent failure. Early fault identification remains problematic, as defects are commonly obscured by extensive background noise. Moreover, the exponential increases in gearbox data further complicate the defect classification process, confusing even the most sophisticated algorithms and significantly making the procedure time consuming. Singular Value Decomposition (SVD) has proved to be highly efficient in signal denoising, stability preservation, and feature extraction reliably under varying conditions, filtering out non-linear signals to reconstruct relevant features only. However, its considerable computation time necessitates exploring alternatives like Randomized SVD (RSVD) to mitigate processing time while maintaining classification accuracy. In this work, an intelligent algorithm for gear and bearing fault diagnosis is developed, incorporating Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) and Time-Domain Features for feature extraction. RSVD is employed for signal denoising and feature reconstruction, while K-Nearest Neighbor (KNN) for feature classification. The combined techniques ensure enhanced diagnostic accuracy, addressing critical challenges in industrial maintenance and performance optimization.
PHM for Spacecraft Propulsion Systems: Developing Resilient Models for Real-World Challenges
Authors: Minamitu  , Ji Dai-Yan , Lee Jay
Published: June 2024

This paper extends the research presented at the Prognostics and Health Management (PHM) Asia-Pacific 2023 Conference Data Challenge, focusing on a more pragmatic approach to spacecraft propulsion system health assessment. While the previous competition saw a variety of solutions, they predominantly relied on the assumption of highly stable initial hydraulic conditions – an idealization seldom met in real-world scenarios. In practical settings, factors such as operational noise, recent operational states, and ambient environmental conditions significantly disrupt this stability, rendering such solutions less feasible. Addressing this gap, our current study introduces a novel diagnostic model capable of valve faults without depending on the initial stable state of hydraulics. This approach marks a significant shift from our previous methodology, which primarily utilized similarity measures and physics-inspired features to classify health states and identify solenoid valve faults in spacecraft propulsion systems. The proposed model in this paper is validated against a diverse set of conditions, emphasizing its robustness and applicability in fluctuating real-world scenarios. Our findings demonstrate that the new model not only effectively diagnoses system health under varied and less controlled conditions but also enhances the practicality of spacecraft health management, offering a more adaptable solution in the face of operational uncertainties.
MOXAI – Manufacturing Optimization through Model-Agnostic Explainable AI and Data-Driven Process Tuning
Authors: Heistracher Clemens , Wachsenegger Anahid , Weißenfeld Axel , Casas Pedro
Published: June 2024

Modern manufacturing equipment offers numerous configurable parameters for optimization, yet operators often underutilize them. Recent advancements in machine learning (ML) have introduced data-driven models in industrial settings, integrating key equipment characteristics. This paper evaluates the performance of ML models in classification tasks, revealing nuanced observations. Understanding model decision-making processes in failure detection is crucial, and a guided approach aids in comprehending model failures, although human verification is essential. We introduce MOXAI, a data-driven approach leveraging existing pre-trained ML models to optimize manufacturing machine parameters. MOXAI underscores the significance of explainable artificial intelligence (XAI) in enhancing data-driven process tuning for production optimization and predictive maintenance. MOXAI assists operators in adjusting process settings to mitigate machine failures and production quality degradation, relying on techniques like DiCE for automatic counterfactual generation and LIME to enhance the interpretability of the ML model's decision-making process. Leveraging these two techniques, our research highlights the significance of explaining the model and proposing the recommended parameter setting for improving the process.
From Prediction to Prescription: Large Language Model Agent for Context-Aware Maintenance Decision Support
Authors: DENG HAOXUAN , Namoano Bernadin , ZHENG BOHAO , Khan Samir , Ahmet Erkoyuncu John
Published: June 2024

Predictive analytics with machine learning approaches has widely penetrated and shown great success in system health management over the decade. However, how to convert the prediction to an actionable plan for maintenance is still far from mature. This study investigates how to narrow the gapbetween predictive outcomes and prescriptive descriptions for system maintenance using an agentic approach based on the large language model (LLM). Additionally, with the retrieval-augmented generation (RAG) technique and tool usage capability, the LLM can be context-aware when making decisions in maintenance strategy proposals considering predictions from machine learning. In this way, the proposed method can push forward the boundary of current machine-learning methods from a predictor to an advisor for decision-making workload offload. For verification, a case study on linear actuator fault diagnosis is conducted with the GPT-4 model. The result demonstrates that the proposed method can perform fault detection without extra training or fine-tuning with comparable performance to baseline methods and deliver more informatic diagnosis analysis and suggestions. This research can shed light on the application of large language models in the construction of versatile and flexible artificial intelligence agents for maintenance tasks.
Robust Remaining Useful Life Prediction Using Jacobian Feature Regression-Based Model Adaptation
Authors: Sheth Prasham , Roychoudhury Indranil
Published: June 2024

The accurate and robust prediction of remaining useful life (RUL) is critical for enabling the proactive mitigation of fault effects rather than reacting to them. For RUL prediction, one must model nominal and faulty system behaviors and how different faults progress over time. Complex data-driven machine learning (ML) models may capture both nominal and fault progression by updating the model parameters at different stages. As new data are observed, these model parameters can be updated to keep the system model always accurate. However, complete retraining of these models is both data- and computation-intensive and unsuitable for dynamic, fast-changing environments requiring quick recalibration. This calls for efficiently adapting the model to new operating conditions or the system’s current state. One such efficient way to recalibrate model parameters to newly observed data using Jacobian feature regression (JFR) is presented in Forgione, Muni, Piga, and Gallieri (2023), where a recurrent neural network (RNN) models the current behavior of the dynamic system. Then, any subsequent deviation of observed measurements and the RNN model is attributed to an “unacceptable degradation of the nominal model performance.” To update the RNN model, Forgione et al. (2023) propose augmenting the current model with additive correction terms learned by implementing JFR on observed “perturbed system” data. In this paper, we propose an automated online framework to adapt the model efficiently to always reflect the system’s current state and use it for accurate RUL prediction and select JFR as one such adaptation technique. We extend the implementation of JFR-based model adaptation to hybrid models and demonstrate JFR to be more sustainable than the other retraining methods. Finally, we showcase the application of this approach to the oil and gas industry. A testbed that simulates a digital synthetic oilfield is used to show the effectiveness of this adaptation-based RUL prediction technique.
Human-Centric PHM in the Era of Industry 5.0
Authors: Khanna Parul , Kumari Jaya , Karim Ramin
Published: June 2024

The maintenance industry is undergoing a major transformation as it embraces the shift towards Industry 5.0. The focus of Industry 5.0 is on the integration of human intelligence with advanced technologies. It emphasizes interaction and collaboration between humans and machines and aims to combine the strengths of both. The efficiency of prognostics and health management (PHM) for maintenance in industrial contexts can be enhanced by improving this human-machine interaction and collaboration. This paper investigates the human-centric aspects, with a focus on PHM systems for facilitating the enablement of Industry 5.0 in maintenance. Acknowledging human as an active participant, this study explores their integral role in designing and developing PHM systems. The data collection for this study has been based on available literature, active and passive observations, and unstructured interviews and discussions with experienced industry professionals. As a result of the analysis of collected data, this study identifies and highlights potential areas for research and exploration. The research in these areas can advance the understanding and application of human-centric PHM strategies within Industry 5.0 in maintenance contexts. This is expected to improve the resilience and sustainability aspects of the industrial ecosystem and facilitate the shift towards Industry 5.0.
Dynamic Modeling of Distributed Wear-Like Faults in Spur Gears: Simplified Approach with Experimental Validation
Authors: Bachar Lior , Cohen Roee , Matania Omri , Bortman Jacob
Published: June 2024

Dynamic models of gears are recognized for offering a promising platform for gaining a profound understanding of the dynamic response, particularly the vibration signature. Wear is considered among the most common and concerning fault mechanisms in gears, yet its recognition and subsequent diagnosis remain challenging. In this study, we introduce an existing dynamic model of spur gear vibrations and extend its validation for distributed wear-like faults. The novelty of this work lies in addressing the complexities associated with modeling distributed faults using simplified yet sophisticated approaches. These involve variance among defected teeth, calculation of time-variant gear mesh stiffness, and consideration of the forces induced by the fault. The model is validated through pioneering controlled experiments, analyzing dozens of degrading distributed wear-like faults. This comparison verifies our capability to generate realistic simulations of vibration signals from worn gears. To bridge the discrepancy between the induced and simulated faults, the model first constructs the healthy profile of the inspected gear, incorporating manufacturing errors and tooth modifications. Subsequently, meticulous photography enables the replication of faults in the model with a high resemblance to the experiment. The results demonstrate a strong correlation between measured and simulated signals, as verified through trend analysis of features extracted from synchronous average signals in both the cycle and order domains. This study lays the groundwork for in-depth investigation into the physics of gear wear, paving the way for potential applications such as fault severity estimation and intelligent fault diagnosis in future studies.
Graph Neural Networks for Electric and Hydraulic Data Fusion to Enhance Short-term Forecasting of Pumped-storage Hydroelectricity
Authors: Theiler Raffael , Fink Olga
Published: June 2024

Pumped-storage hydropower plants (PSH) actively participate in grid power-frequency control and therefore often operate under dynamic conditions, which results in rapidly varying system states. Predicting these dynamically changing states is essential for comprehending the underlying sensor and machine conditions. This understanding aids in detecting anomalies and faults, ensuring the reliable operation of the connected power grid, and in identifying faulty and miscalibrated sensors. PSH are complex, highly interconnected systems encompassing electrical and hydraulic subsystems, each characterized by their respective underlying networks that can individually be represented as graph. To take advantage of this relational inductive bias, graph neural networks (GNNs) have been separately applied to state forecasting tasks in the individual subsystems, but without considering their interdependencies. In PSH, however, these subsystems depend on the same control input, making their operations highly interdependent and interconnected. Consequently, hydraulic and electrical sensor data should be fused across PSH subsystems to improve state forecasting accuracy. This approach has not been explored in GNN literature yet because many available PSH graphs are limited to their respective subsystem boundaries, which makes the method unsuitable to be applied directly. In this work, we introduce the application of spectral-temporal graph neural networks, which leverage self-attention mechanisms to concurrently capture and learn meaningful subsystem interdependencies and the dynamic patterns observed in electric and hydraulic sensors. Our method effectively fuses data from the PSH’s subsystems by operating on a unified, system-wide graph, learned directly from the data, This approach leads to demonstrably improved state forecasting performance and enhanced generalizability.
Hybrid Prognostics for Aircraft Fuel System: An Approach to Forecasting the Future
Authors: Felixfu2022  , Avdelidis Nicolas P.
Published: June 2024

The copious volumes of data collected incessantly from diverse systems present challenges in interpreting the data to predict system failures. The majority of large organizations employ highly trained experts who specialize in using advanced maintenance equipment and have specific certification in predictive maintenance (PdM). Prognostics and health management (PHM) connects research on deterioration models to strategies for PdM. Prognostics refer to the process of estimating the time until failure and the associated risk for one or more current and potential failure modes. Prognostics aim to provide guidance by alerting to imminent failures and predicting the remaining useful life (RUL). This eventually leads to improved availability, dependability, and safety, while also reducing maintenance costs. This research work diverges from existing established prognostic methodologies by emphasising the use of hybrid prognostics to predict the future performance of an aircraft system, especially the point in which the system will cease to operate as intended, often referred to as its time to failure. We have developed a new method that combines a physics-based model with the physics of failure (PoF) and a multiple-layered hyper-tangent-infused data-driven approach. The results are useful. The authors retrieved datasets for analysis using a laboratory aircraft fuel system and simulation model. Consequently, the comparative results demonstrate that the proposed hybrid prognostic approach properly estimates the RUL and demonstrates strong application, availability, and precision.
Ultrafast laser damaging of ball bearings for the condition monitoring of a fleet of linear motors
Authors: Jabbar Abdul , Mazzonetto Manuel , Orazi Leonardo , Cocconcelli Marco
Published: June 2024

Machine learning-based condition monitoring of mechanical systems, such as bearings, employs two primary approaches: unsupervised and supervised methods. Unsupervised approaches aim to characterize the healthy state of the machine and monitor deviations from this state. The advantage lies in requiring only the health condition of the component without the need for historical data until breakdown. However, the disadvantage is the lack of information regarding the root cause of any potential malfunction. On the other hand, supervised methods consider both healthy and faulty cases, aiming to maximize the difference between them through post-processing, as well as among different fault types. The advantage is the ability to analyze the specific signature of a particular fault type. Nonetheless, the disadvantage is that available data usually do not cover all possible faults that may occur.Typically, obtaining a faulty bearing involves either a time-consuming run-to-failure test or the artificial induction of faults using drills, electro-discharge pens, etc. While artificial faults offer a quicker procedure, they often fail to replicate real faults faithfully. This paper suggests using picosecond laser technology to engrave the surface of the bearing and create artificial faults. Modern laser technology allows for precise control over the dimensions of injected faults, enhancing the understanding of fault progression at various stages in the life of bearings. These measurements are crucial parameters for evaluating the robustness of diagnostic algorithms. This paper focuses on artificially damaging a ball bearing used in an independent cart systems application, which comprises a fleet of linear motors moving on the same rail. These systems have recently been proposed by different manufacturers and adopted in the field of packaging machines for their flexibility. For such systems, no prior instances of faulted bearings are available, and the size of a real fault is also unknown. Hand-made faults with drills did not produce discernible faults appreciable in post-processing of the data. Therefore, a picosecond laser with a pulse duration of 10 ps and a maximum energy per pulse of approximately 100 µJ is utilized to create a set of test bearings with increasing fault sizes on the outer race. Post-processing of the data enables the qualification of the minimum fault severity detectable in this specific application.
A Study on the Equipment Data Collection and Developing Next Generation Integrated PHM System
Authors: KIM DEOGHYEON
Published: June 2024

This research presents an integrated PHM system for 2,000 rotating equipment units across press, car body, paint, and assembly lines in Hyundai/Kia factories. The system addresses limitations of individual monitoring systems by consolidating vibration, current, robot AI diagnostics, PLC backup status, and operational data. Vibration monitoring utilizes wired/wireless sensors, server storage, and automated analysis for trend detection and fault diagnosis. PLC data monitoring retrieves motor drive information (current, temperature, frequency, etc.) to predict equipment anomalies. Robot monitoring integrates with various manufacturers and tracks operational status, motor load, and alarms for maintenance and lifespan management. The PLC backup solution ensures proper backup functionality. The integrated PHM architecture manages data collection, analysis, diagnostics, reporting, and visualization, enabling comprehensive equipment health monitoring and proactive maintenance.
Personality, mood, or emotion? Influence of customer trait and state during the cellar door experience on sales and word-of-mouth intention: A Bayesian approach
Authors: D'Ament Genevieve , Nayeem Tahmid , Saliba Anthony J.
Published: June 2024

Direct-to-customer sales provide higher returns, maximising the profitability of wine businesses. While recent research has expanded the understanding of the sales relationship developed during a cellar door experience, individual influences on this relationship remain under-researched. The current study examines the direct-to-customer sales relationship co-created with staff during a cellar door experience. The influence of a participant’s personality, mood, and emotion on onsite sales and intention to recommend the experience was captured through an online questionnaire completed by 136 customers during their cellar door experience. A Bayesian Network was produced to determine the influence of states and traits on purchase and loyalty behaviours. Results showed all outcome variables were sensitive to wine-evoked emotions, aroused mood, and neuroticism. Additionally, results show that cellar door staff make an invaluable contribution to maximising profitability. The developed framework provides cellar door managers and staff with a valuable guide to create engaging cellar door experiences which are essential to maximising overall winery profitability.
Developing A Prototype of Module Instruction of Writing Short-Stories Integrated with Samawa Local Wisdom
Authors: Ria Saputri  , Yeti Mulyati  , Sumiyadi  , Vismaia S. Damaianti
Published: June 2024

The teaching modules should be carefully designed to not only cover the academic aspects of literature but also to incorporate elements that foster cultural understanding and appreciation. The goal is to create a comprehensive learning framework that allows students to explore literature in a way that is both intellectually stimulating and culturally enlightening. Therefore, this study aims to develop instructional module reflecting the didactic theories and local wisdom principles to facilitate students to practice writing short stories. These theoretical principles are integrated in the module components, namely in the material, training and evaluation sections, student activities in the Module, glossary, and Module illustrations/graphics. The research method uses the R2D2 (Reflective, Recursive, Design and Development) design. Characteristics of the R2D2 model include being recursive, non-linear, sometimes irregular, reflective, and collaborative. However, this model emphasizes learning in a meaningful context. Formative assessment is considered highly important. This research method emphasizes the subjectivity of the model developer. The research commenced with a reflective activity, which is the needs analysis phase, aimed at elucidating the gaps in short story writing instruction. To obtain accurate information regarding the issues at hand, the researcher engaged in a recursive process encompassing all components related to learning and the design of the short story writing module. The results of the research are a prototype of a short story writing module that can be adapted to other text genres in various literary learning materials.