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Article
Engineering, Industrial
Fariba Azizi et al.
Summary: This paper addresses the condition-based maintenance scheduling for a continuously monitored manufacturing system. It develops a novel framework using a tractable bivariate Birth/Birth-Death process and renewal theory to find the optimal policy. The results show that the optimal maintenance policy can be derived by minimizing the long-run expected average cost per unit time.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Yingsai Cao et al.
Summary: This paper proposes a new condition-based maintenance policy for systems subject to multi-state deterioration and random shock simultaneously. The Semi-Markov process and compound Poisson process are used to model the failure processes and dependent relationships. A condition-based inspection policy is designed to capture the states where maintenance actions are needed. The optimal values of the proposed maintenance policy are obtained by Monte Carlo simulation technique.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Industrial
Nan Zhang et al.
Summary: This paper develops a condition-based maintenance model for a multi-component system operating under a dynamic environment. The model considers the intrinsic characteristics and common operating environment of each component, and models the environment evolution as a continuous-time Markov process. The paper also investigates the structural properties of the optimal maintenance policy and illustrates the applicability of the model through numerical examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Chemistry, Analytical
Cyprien Hoelzl et al.
Summary: Monitoring information is crucial in assessing the condition of railway infrastructure, and Axle Box Accelerations (ABAs) provide valuable data on the dynamic vehicle/track interaction. However, the uncertainties in ABA measurements make it challenging to assess the condition of rail welds accurately. In this study, expert feedback is used to narrow down these uncertainties and improve the assessment of faulty welds.
Article
Computer Science, Interdisciplinary Applications
Adolfo CRESPO Marquez et al.
Summary: Digital Twins (DTs) are being increasingly popular in replicating physical equipment and systems in the digital world in the context of the fourth industrial revolution. They promise improved productivity and sustainability through data integration, models, and decision-support systems. However, challenges such as conceptual basis, functional description, and established requirements need to be addressed before reaping the potential benefits of DTs for maintenance management. This paper presents a practical approach to bridge this gap by designing digital configurations for maintenance management, focused on the development of a condition-based maintenance application (CBM App) using a DT of train axle bearings.
COMPUTERS IN INDUSTRY
(2023)
Article
Chemistry, Analytical
Satish Kumar et al.
Summary: In this study, a low-cost multi-sensor data acquisition system was proposed to detect faults in 3D printed products. Real-time multi-sensor signals were collected and analyzed, feature vectors were created using the chi-square method and trained the CNN model, achieving high accuracy in classification.
Article
Mathematics
Nien-Che Yang et al.
Summary: This study proposes a voting-based ensemble learning algorithm with linear regression, decision tree, and support vector machine (EL-VLR-DT-SVM) for PV fault detection and diagnosis. The algorithm is validated by comparing it with other algorithms in terms of accuracy, precision, recall, and F-1 score. The results show that the proposed EL-VLR-DT-SVM algorithm outperforms the others.
Article
Chemistry, Analytical
Ayman Mohamed et al.
Summary: This article reviews the latest technologies and components of TCM systems, with a focus on analyzing the advantages and limitations of wireless tool-embedded sensor nodes. It also provides a comprehensive review of dimensionality reduction techniques. Finally, it discusses attempts to generalize and enhance TCM systems and offers recommendations for future research directions.
Article
Mathematics
Zhe Tong et al.
Summary: This paper proposes a Packet Loss Influence-inspired Retransmission Mechanism (PLIRM) to improve the efficiency of online fault diagnosis. Experimental results show that the proposed method can accurately and timely identify bearing faults, outperforming competitive approaches.
Article
Mathematics
Zitong Yan et al.
Summary: A new unsupervised representation learning method called Signal Momentum Contrast (SMoCo) is proposed in this study, which extracts fault features using the contrastive learning algorithm - Momentum Contrast (MoCo), and performs feature extraction using the data augmentation method.
Review
Engineering, Chemical
Basheer Wasef Shaheen et al.
Summary: This study conducted a comprehensive literature review on how Industry 4.0 technologies and features affect the various functions of maintenance management systems. The findings show that artificial intelligence-machine learning, CPS, IoT, big data, augmented reality, and cloud computing significantly influence the main functions of maintenance management systems. The integration of Industry 4.0 technologies and features improves real-time condition monitoring, data management, coordination of maintenance tasks, remote maintenance supervision, and overall operations and productivity.
Proceedings Paper
Materials Science, Multidisciplinary
Sucharitha Saha et al.
Summary: Acoustic emission (AE) testing is a promising non-destructive testing method for in-situ detection of damages in structures and materials. This study presents the design and development of a signal conditioning unit (SCU) for processing the AE signals, and the fabrication of a low-cost AE signal data acquisition system. The performance of the developed system is tested and compared with a commercially available one.
MATERIALS TODAY-PROCEEDINGS
(2022)
Article
Engineering, Mechanical
Ali Moshrefzadeh
Summary: Extensive research has been conducted on intelligent fault diagnosis and prognosis of rolling element bearings. A newly proposed approach, spectral amplitude modulation, is employed to differentiate between different health states of machinery regardless of load and speed conditions. This method can be used as a health indicator for online condition monitoring of machines.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2021)
Article
Chemistry, Analytical
Nestor Diego Rivera-Campoverde et al.
Summary: This study proposes a methodology for emissions estimation in real driving conditions using onboard diagnostics data and machine learning. By training artificial neural networks and analyzing driving data, the study achieves accurate emission estimation in different traffic conditions, showing results comparable to international standards in real driving tests.
Article
Acoustics
Nadine Martin et al.
Summary: This paper discusses the importance of machine health condition monitoring, highlighting the challenges and solutions, focusing on the significance of signal processing in condition-based preventive maintenance, emphasizing the advantages and goals of automated signal processing.
ACOUSTICS AUSTRALIA
(2021)
Article
Chemistry, Analytical
Seyedmilad Komarizadehasl et al.
Summary: Engineers currently use accelerometers for structural verification, but the high cost limits their use. A Cost Hyper-Efficient Arduino Product (CHEAP) has been developed, which accurately measures structural accelerations at a significantly lower price and with better accuracy, especially at low frequencies and low acceleration amplitudes. Tests showed that CHEAP outperformed other systems in terms of price and accuracy, with better observable resolution in Fast Fourier Transformation assessments.
Article
Chemistry, Multidisciplinary
Cesar Ricardo Soto-Ocampo et al.
Summary: The increasing industrial competitiveness globally has sparked a growing interest in condition monitoring, particularly in the role of rotating machinery and the critical component of bearings. Vibration-based signal treatments are being used to identify features associated with bearing faults, which are then used to construct health indicators for evaluating current machine operating status. The use of contour maps to represent a bearing's diagnosis map, serving as a health map, shows promise in detecting and evaluating the severity of bearing damage, potentially improving the classification of bearing faults and optimizing maintenance processes.
APPLIED SCIENCES-BASEL
(2021)
Review
Materials Science, Multidisciplinary
T. Mohanraj et al.
JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T
(2020)
Article
Engineering, Electrical & Electronic
Lei Kou et al.
IET POWER ELECTRONICS
(2020)
Article
Chemistry, Analytical
Cesar Ricardo Soto-Ocampo et al.
Article
Computer Science, Information Systems
P. Mcmahon et al.
Article
Engineering, Multidisciplinary
Alejandro Bustos et al.
Article
Engineering, Multidisciplinary
Kun Yu et al.
Article
Automation & Control Systems
Zhiliang Liu et al.
Review
Engineering, Electrical & Electronic
Esteban Bernal et al.
IEEE SENSORS JOURNAL
(2019)
Article
Engineering, Mechanical
Jerome Antoni et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2019)
Article
Engineering, Civil
Nicola Bosso et al.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART F-JOURNAL OF RAIL AND RAPID TRANSIT
(2018)
Article
Chemistry, Analytical
Antonio Vidal-Pardo et al.
Article
Automation & Control Systems
S. Martin-del-Campo et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2017)
Review
Engineering, Mechanical
Wade A. Smith et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2015)
Article
Automation & Control Systems
Guo-Jin Feng et al.
INTERNATIONAL JOURNAL OF AUTOMATION AND COMPUTING
(2015)
Article
Engineering, Mechanical
B Samanta et al.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2003)
