☆ 4.6 Article

Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios

APPLIED SCIENCES-BASEL (2022)

相关参考文献

注意：仅列出部分参考文献，下载原文获取全部文献信息。

Article Engineering, Mechanical

Early wheel flat detection: an automatic data-driven wavelet-based approach for railways

Araliya Mosleh et al.

Summary: This paper aims to develop an unsupervised early damage detection methodology that can automatically distinguish a defective wheel from a healthy one by evaluating the acceleration and shear forces on railway tracks. The proposed methodology involves data acquisition, feature extraction, feature normalization, data fusion, and feature classification, and has been shown to effectively detect wheel defects.

VEHICLE SYSTEM DYNAMICS (2023)

添加到收藏夹

Article Chemistry, Multidisciplinary

Genetic Programming-Based Feature Construction for System Setting Recognition and Component-Level Prognostics

Francesca Calabrese et al.

Summary: Extracting representative feature sets from raw signals is crucial in understanding components' behavior in Prognostics and Health Management (PHM). This paper adopts Genetic Programming (GP) to extract system-level and component-level features, and evaluates their performance through classification accuracy and Remaining Useful Life (RUL) prediction error.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Chemistry, Multidisciplinary

Novelty Detection with Autoencoders for System Health Monitoring in Industrial Environments

Francesco Del Buono et al.

Summary: Predictive Maintenance (PdM) is a new strategy that utilizes Machine Learning (ML) algorithms, particularly autoencoders, to predict failure and maximize component lifespan in industrial maintenance management. This paper presents a systematic study of autoencoder-based novelty detection methods and their effectiveness in industrial plant environments.

APPLIED SCIENCES-BASEL (2022)

添加到收藏夹

Article Automation & Control Systems

Incremental Novelty Identification From Initially One-Class Learning to Unknown Abnormality Classification

Zhe Yang et al.

Summary: This study proposes an incremental novelty identification (INI) method based on deep learning to classify unknown abnormal patterns in industrial machinery. By utilizing a convolutional neural network autoencoder for feature extraction and an online sequential-extreme learning machine for model updating, the proposed method can effectively identify both known and unknown abnormal patterns.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2022)

添加到收藏夹

Article Transportation Science & Technology

Automatic clustering-based approach for train wheels condition monitoring

Araliya Mosleh et al.

Summary: This paper presents an unsupervised methodology for identifying railway wheel flats, which is based on the evaluation of acceleration on the rails for the passage of traffic loads. The proposed methodology involves a two-step procedure for automatic damage identification and utilizes machine learning techniques such as data acquisition, feature extraction, feature normalization, data fusion, and unsupervised feature classification.

INTERNATIONAL JOURNAL OF RAIL TRANSPORTATION (2022)

添加到收藏夹

Article Engineering, Industrial

Exploring predictive maintenance applications in industry

Wieger Tiddens et al.

Summary: This study conducted a multiple-case study in The Netherlands to identify the reasons why only few companies were able to successfully implement Predictive Maintenance (PdM). The research found that successful implementation of PdM involves a combination of different techniques, although the most commonly used techniques are still based on previous experiences. The study also calls for better methods to guide the selection and use of suitable types of PdM based on the firm's ambitions and available data.

JOURNAL OF QUALITY IN MAINTENANCE ENGINEERING (2022)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time

Serkan Ayvaz et al.

Summary: The study developed a data-driven predictive maintenance system for manufacturing production lines, utilizing real-time data from IoT sensors to detect potential failures before they occur. The system was successful in identifying indicators of potential failures and the best performing machine learning models were integrated into the production system in the factory.

EXPERT SYSTEMS WITH APPLICATIONS (2021)

添加到收藏夹

Article Chemistry, Multidisciplinary

Feature-Based Multi-Class Classification and Novelty Detection for Fault Diagnosis of Industrial Machinery

Francesca Calabrese et al.

Summary: The paper discusses how to use machine learning techniques for fault diagnosis and novelty detection to improve maintenance approaches in industrial machinery. Results show that the method is simple, fast, accurate, and can enhance the accuracy and generalization ability of classification models.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Article Chemistry, Multidisciplinary

Predictive Maintenance: A Novel Framework for a Data-Driven, Semi-Supervised, and Partially Online Prognostic Health Management Application in Industries

Francesca Calabrese et al.

Summary: Prognostic Health Management (PHM) is a maintenance strategy based on Condition Monitoring (CM) data for predicting machinery states. Literature divides PHM into methodological and applicative levels, with the latter often relying on historical data for training models, which may be challenging for industries to obtain. A novel framework is proposed in this paper, utilizing edge-cloud infrastructure for real-time streaming analysis and training of more accurate diagnostic and prognostic models from multiple machines, ultimately benefiting both producers and users in the industrial field.

APPLIED SCIENCES-BASEL (2021)

添加到收藏夹

Article Engineering, Mechanical

Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

Zhe Yang et al.

Summary: A novel fault diagnostic method is proposed in this research, utilizing a sparse autoencoder-based multi-head Deep Neural Network for joint supervised and unsupervised learning to effectively detect novelties. The method demonstrates excellent performance in experimental data.

CHINESE JOURNAL OF MECHANICAL ENGINEERING (2021)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Novelty Detection and Online Learning for Chunk Data Streams

Yi Wang et al.

Summary: The paper introduces an efficient framework for novelty detection and incremental learning for unlabeled chunk data streams by solving a linear system in the kernel space to generate Reproducing Kernel Hilbert Space (RKHS). Different approaches proposed based on this framework show good performance in novelty detection and online learning.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)

添加到收藏夹

Article Engineering, Multidisciplinary

Maintenance Costs and Advanced Maintenance Techniques in Manufacturing Machinery: Survey and Analysis

Douglas Thomas et al.

Summary: The study estimated national losses due to inadequate maintenance and compared the costs and benefits of different maintenance techniques using manufacturer data. The results show substantial benefits of transitioning from reactive maintenance towards preventive and/or predictive maintenance, leading to reductions in unplanned downtime and defects.

INTERNATIONAL JOURNAL OF PROGNOSTICS AND HEALTH MANAGEMENT (2021)

添加到收藏夹

Proceedings Paper Materials Science, Multidisciplinary

Data science applications for predictive maintenance and materials science in context to Industry 4.0

Sufiyan Sajid et al.

Summary: With the evolution of industries towards Industry 4.0, machines have become more complex and require data science techniques and experts for predictive maintenance to reduce failures and quality deviations, thus improving production efficiency and reliability.

MATERIALS TODAY-PROCEEDINGS (2021)

添加到收藏夹

Article Automation & Control Systems

Incremental novelty detection and fault identification scheme applied to a kinematic chain under non-stationary operation

J. A. Carino et al.

ISA TRANSACTIONS (2020)

添加到收藏夹

Article Computer Science, Information Systems

Challenges to IoT-Enabled Predictive Maintenance for Industry 4.0

Michele Compare et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Ensemble-learning based neural networks for novelty detection in multi-class systems

Felix T. S. Chan et al.

APPLIED SOFT COMPUTING (2020)

添加到收藏夹

Article Automation & Control Systems

Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

Juan Jose Saucedo-Dorantes et al.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2020)

添加到收藏夹

Article Engineering, Industrial

Exploring the impacts and contributions of maintenance function for sustainable manufacturing

Maria Holgado et al.

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)

添加到收藏夹

Article Engineering, Mechanical

Deep representation clustering-based fault diagnosis method with unsupervised data applied to rotating machinery

Xiang Li et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2020)

添加到收藏夹

Article Chemistry, Multidisciplinary

Real-Time and Robust Hydraulic System Fault Detection via Edge Computing

Dzaky Zakiyal Fawwaz et al.

APPLIED SCIENCES-BASEL (2020)

添加到收藏夹

Review Computer Science, Interdisciplinary Applications

Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges

Jovani Dalzochio et al.

COMPUTERS IN INDUSTRY (2020)

添加到收藏夹

Article Computer Science, Information Systems

SOPHIA: An Event-Based IoT and Machine Learning Architecture for Predictive Maintenance in Industry 4.0

Matteo Calabrese et al.

INFORMATION (2020)

添加到收藏夹

Article Chemistry, Analytical

Fault Detection and Diagnosis Using Combined Autoencoder and Long Short-Term Memory Network

Pangun Park et al.

SENSORS (2019)

添加到收藏夹

Proceedings Paper Automation & Control Systems

A Step-wise Feature Selection Scheme for a Prognostics and Health Management System in Autonomous Ferry Crossing Operation

Xu Cheng et al.

2019 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATION (ICMA) (2019)

添加到收藏夹

Article Computer Science, Information Systems

Self-representation and PCA embedding for unsupervised feature selection

Yonghua Zhu et al.

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2018)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Multi-Label Learning with Emerging New Labels

Yue Zhu et al.

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2018)

添加到收藏夹

Article Computer Science, Information Systems

A method for autonomous data partitioning

Xiaowei Gu et al.

INFORMATION SCIENCES (2018)

添加到收藏夹

Review Engineering, Mechanical

Machine health management in smart factory: A review

Gil-Yong Lee et al.

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY (2018)

添加到收藏夹

Review Engineering, Mechanical

Artificial intelligence for fault diagnosis of rotating machinery: A review

Ruonan Liu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

添加到收藏夹

Review Engineering, Mechanical

A review on the application of deep learning in system health management

Samir Khan et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

添加到收藏夹

Review Engineering, Mechanical

Machinery health prognostics: A systematic review from data acquisition to RUL prediction

Yaguo Lei et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2018)

添加到收藏夹

Article Computer Science, Information Systems

Fault Detection and Identification Methodology Under an Incremental Learning Framework Applied to Industrial Machinery

Jesus A. Carino et al.

IEEE ACCESS (2018)

添加到收藏夹

Article Engineering, Mechanical

A Systematic Semi-Supervised Self-adaptable Fault Diagnostics approach in an evolving environment

Yang Hu et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)

添加到收藏夹

Review Engineering, Mechanical

State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels

Kamran Javed et al.

MECHANICAL SYSTEMS AND SIGNAL PROCESSING (2017)

添加到收藏夹

Review Computer Science, Information Systems

Industrial Big Data for Fault Diagnosis: Taxonomy, Review, and Applications

Yan Xu et al.

IEEE ACCESS (2017)

添加到收藏夹

Article Computer Science, Information Systems

Industrial Big Data in an Industry 4.0 Environment: Challenges, Schemes, and Applications for Predictive Maintenance

Jihong Yan et al.

IEEE ACCESS (2017)

添加到收藏夹

Article Computer Science, Artificial Intelligence

MINAS: multiclass learning algorithm for novelty detection in data streams

Elaine Ribeiro de Faria et al.

DATA MINING AND KNOWLEDGE DISCOVERY (2016)

添加到收藏夹

Article Engineering, Multidisciplinary

Fault Diagnosis with Evolving Fuzzy Classifier Based on Clustering Algorithm and Drift Detection

Maurilio Inacio et al.

MATHEMATICAL PROBLEMS IN ENGINEERING (2015)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Fully unsupervised fault detection and identification based on recursive density estimation and self-evolving cloud-based classifier

Bruno Sielly Jales Costa et al.

NEUROCOMPUTING (2015)

添加到收藏夹

Proceedings Paper Engineering, Mechanical

A Feature Extraction Method for Fault Classification of Rolling Bearing based on PCA

Fengtao Wang et al.

11TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES (DAMAS 2015) (2015)

添加到收藏夹

Review Engineering, Electrical & Electronic

A review of novelty detection

Marco A. F. Pimentel et al.

SIGNAL PROCESSING (2014)

添加到收藏夹

Article Computer Science, Artificial Intelligence

COMPOSE: A Semisupervised Learning Framework for Initially Labeled Nonstationary Streaming Data

Karl B. Dyer et al.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2014)

添加到收藏夹

Article Engineering, Industrial

The role of maintenance in improving company's competitiveness and profitability A case study in a textile company

Damjan Maletic et al.

JOURNAL OF MANUFACTURING TECHNOLOGY MANAGEMENT (2014)

添加到收藏夹

Review Environmental Studies

A review of the three most popular maintenance systems: how well is the energy sector represented?

Kym Fraser et al.

INTERNATIONAL JOURNAL OF GLOBAL ENERGY ISSUES (2011)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Novelty detection with application to data streams

Eduardo J. Spinosa et al.

INTELLIGENT DATA ANALYSIS (2009)

添加到收藏夹

Article Engineering, Industrial

The role of maintenance in improving companies' productivity and profitability

Imad Alsyouf

INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS (2007)

添加到收藏夹

Article Computer Science, Artificial Intelligence

Improving the performance of radial basis function classifiers in condition monitoring and fault diagnosis applications where 'unknown' faults may occur

YH Li et al.

PATTERN RECOGNITION LETTERS (2002)

添加到收藏夹

© Peeref 2019-2024. All rights reserved.