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Anomaly Detection Methods for Industrial Applications: A Comparative Study
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9781119371052openalex-snapshot
From Prognostics and Health Systems Management to Predictive Maintenance 1
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Automatic bearing fault diagnosis based on one-class ν-SVM
inferredcites →https://doi.org/10.1016/j.engappai.2023.106597openalex-snapshot
Real-time anomaly detection on time series of industrial furnaces: A comparison of autoencoder architectures
inferredcites →https://doi.org/10.1016/j.knosys.2014.01.020openalex-snapshot
Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection
inferredcites →https://doi.org/10.1016/j.neucom.2018.03.067openalex-snapshot
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
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A review of novelty detection
inferredcites →https://doi.org/10.1016/j.ymssp.2005.09.012openalex-snapshot
A review on machinery diagnostics and prognostics implementing condition-based maintenance
inferredcites →https://doi.org/10.1016/j.ymssp.2006.12.007openalex-snapshot
Support vector machine in machine condition monitoring and fault diagnosis
inferredcites →https://doi.org/10.1016/j.ymssp.2015.04.021openalex-snapshot
Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study
inferredcites →https://doi.org/10.1016/j.ymssp.2021.108752openalex-snapshot
Vibration-based anomaly detection using LSTM/SVM approaches
inferredcites →https://doi.org/10.1016/s0925-2312(97)90018-7openalex-snapshot
Advances in neural information processing systems 7
inferredcites →https://doi.org/10.1016/s1570-7946(06)80046-5openalex-snapshot
Network of three catalytic reactors with periodical feed switching for methanol synthesis: bifurcation analysis
inferredcites →https://doi.org/10.1049/iet-smt.2016.0423openalex-snapshot
Intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder
inferredcites →https://doi.org/10.1051/e3sconf/202017002007openalex-snapshot
Anomaly Detection for Predictive Maintenance in Industry 4.0- A survey
inferredcites →https://doi.org/10.1109/access.2021.3128669openalex-snapshot
Machine Learning Based Bearing Fault Diagnosis Using the Case Western Reserve University Data: A Review
inferredcites →https://doi.org/10.1109/access.2023.3283466openalex-snapshot
Bearing Fault Diagnosis With Envelope Analysis and Machine Learning Approaches Using CWRU Dataset
inferredcites →https://doi.org/10.1109/iccis.2006.252287openalex-snapshot
A Comparative Study for Outlier Detection Techniques in Data Mining
inferredcites →https://doi.org/10.1109/jas.2019.1911393openalex-snapshot
Unsupervised electric motor fault detection by using deep autoencoders
inferredcites →https://doi.org/10.1109/radarconf2043947.2020.9266497openalex-snapshot
From unsupervised to semi-supervised anomaly detection methods for HRRP targets
inferredcites →https://doi.org/10.1109/smc.2019.8913901openalex-snapshot
Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis
inferredcites →https://doi.org/10.1109/tim.2021.3072131openalex-snapshot
Memory Residual Regression Autoencoder for Bearing Fault Detection
inferredcites →https://doi.org/10.1162/089976601750264965openalex-snapshot
Estimating the Support of a High-Dimensional Distribution
inferredcites →https://doi.org/10.14778/3538598.3538602openalex-snapshot
Anomaly detection in time series
inferredcites →https://doi.org/10.3390/s17020425openalex-snapshot
A New Deep Learning Model for Fault Diagnosis with Good Anti-Noise and Domain Adaptation Ability on Raw Vibration Signals
inferredcites →https://doi.org/10.3390/s23021009openalex-snapshot
LSTM-Autoencoder for Vibration Anomaly Detection in Vertical Carousel Storage and Retrieval System (VCSRS)
inferredcites →https://doi.org/10.3390/s23063157openalex-snapshot
Lite and Efficient Deep Learning Model for Bearing Fault Diagnosis Using the CWRU Dataset
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Review of Trends in Machine Learning Applications for Quality Failure Detection in the Industry Using Natural Language Processing
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Deployment-Oriented Memory-Based Visual Inspection of Injection-Molded Parts
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Anomaly Detection in Bearing Temperature Data of Industrial Centrifuge Device Using One-Class SVM for Predictive Maintenance in the Mining Sector
inferred← citeshttps://doi.org/10.1007/s10845-026-02932-1openalex-snapshot
AG-DPF: adaptive gating-based dual-prototype fusion framework for metal anomaly detection
inferred← citeshttps://doi.org/10.1007/s10847-025-01277-1openalex-snapshot
MEMS vibration sensor-based edge AI for machinery fault prediction: feasibility study using a petrochemical plant process simulation facility
inferred← citeshttps://doi.org/10.1007/s10847-026-01332-5openalex-snapshot
Comparative study of the 1DCNN, WT–2DCNN, LSTM, and 1DCNN-LSTM models to predict machinery faults based on vibrations measured from a simulation facility for petrochemical processes
inferred← citeshttps://doi.org/10.1007/s44443-025-00021-6openalex-snapshot
Generative adversarial message passing-based anomaly detection
inferred← citeshttps://doi.org/10.1016/j.aei.2025.103859openalex-snapshot
A dual-decoupling network for anomaly detection in automotive transmissions under time-varying operating conditions
inferred← citeshttps://doi.org/10.1016/j.procir.2025.02.121openalex-snapshot
Unsupervised Anomaly Detection using Vibration Signals for Milling Processes
inferred← citeshttps://doi.org/10.1016/j.sna.2026.117794openalex-snapshot
Critical application technologies and challenges in the prognostics and health management (PHM) technology system: A review
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Diesel engine lubricating oil fault prognosis: A hybrid Bayesian LSTM and deep generative model architecture for multilayer anomaly detection
inferred← citeshttps://doi.org/10.1109/etfa61755.2024.10710779openalex-snapshot
Efficient Domain Adaptation Using Individual Standardization in Bearing Fault Detection
inferred← citeshttps://doi.org/10.1109/icassp49660.2025.10889385openalex-snapshot
Padnet: a Patch-Based Anomaly Detection Framework for Industrial Pipeline Damage Detection
inferred← citeshttps://doi.org/10.1109/icmla66185.2025.00205openalex-snapshot
Prompt Learner for Industrial Anomaly Detection: Towards Generalizable Multimodal Defect
inferred← citeshttps://doi.org/10.1109/icmlt65785.2025.11193259openalex-snapshot
Outlier detection and classification in inhomogeneous voltage curves using functional depth
inferred← citeshttps://doi.org/10.1109/induscon66435.2025.11241739openalex-snapshot
Unsupervised Anomaly Detection of Energy Consumption Patterns in Electric Buses
inferred← citeshttps://doi.org/10.1109/issrew67781.2025.00047openalex-snapshot
AI-Driven Reliability Through Anomaly Detection System in Industry 4.0
inferred← citeshttps://doi.org/10.1109/mlnlp63328.2024.10800079openalex-snapshot
Fault Detection Method for Dust Removal Equipment Based on Digital Twin and OCSVM Optimized by Clonal Selection
inferred← citeshttps://doi.org/10.1109/noms59830.2024.10575103openalex-snapshot
A Machine Learning Operations Platform for Streamlined Model Serving in Industry 5.0
inferred← citeshttps://doi.org/10.1109/tim.2025.3573336openalex-snapshot
Hybrid Approach for Machine Anomaly Detection Across Diverse Operating Conditions: Combining Self-Supervised Learning With Vibration Statistics
inferred← citeshttps://doi.org/10.1145/3705391.3705407openalex-snapshot
Design of Embedded Motor Diagnostic System Based on Vibration Signal Analysis and Machine Learning
inferred← citeshttps://doi.org/10.15587/1729-4061.2024.317000openalex-snapshot
Design of an intelligent module for detecting signs of information security threats and the emergence of unreliable data
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The 3W Project and Its Approach to Unlock Data-Driven Solutions for Oil Wells Monitoring
inferred← citeshttps://doi.org/10.2139/ssrn.6479488openalex-snapshot
Anomaly Detection in Industrial Robots Using a Variational Autoencoder with Dual-Domain Feature Extraction and Correlation-Based Feature Selection
inferred← citeshttps://doi.org/10.21595/jme.2024.24423openalex-snapshot
A review of artificial intelligence techniques in blast field shockwave pressure testing
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ML-Driven Lightweight Botnet Detection System for IoT-Networks
inferred← citeshttps://doi.org/10.3390/en17030653openalex-snapshot
Anomaly Detection in Gas Turbines Using Outlet Energy Analysis with Cluster-Based Matrix Profile
inferred← citeshttps://doi.org/10.3390/machines12100743openalex-snapshot
An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge
inferred← citeshttps://doi.org/10.3390/pr14071107openalex-snapshot
Imbalance-Aware APS Failure Classification Using Feature-Wise Attention Graph Convolutional Network
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ALSTMResNet: Active-Learning-Enhanced LSTMResNet as an Efficiency-Oriented Training Framework for Well Anomaly Monitoring Under Partial Labels
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GCN-Based LSTM Autoencoder with Self-Attention for Bearing Fault Diagnosis
inferred← citeshttps://doi.org/10.3390/s25113311openalex-snapshot
Time-Shifted Maps for Industrial Data Analysis: Monitoring Production Processes and Predicting Undesirable Situations
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A Deep Learning-Based Method for Mechanical Equipment Unknown Fault Detection in the Industrial Internet of Things
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A Comparative Study of RQA-Guided Attention Mechanisms with LSTM Autoencoder for Bearing Anomaly Detection
inferred← citeshttps://doi.org/10.36001/phme.2024.v8i1.4070openalex-snapshot
An Experiment on Anomaly Detection for Fault Vibration Signals Using Autoencoder-Based N-Segmentation Algorithm
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Using Anomaly Detection to Search for Technosignatures in Breakthrough Listen Observations
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AI-based decision-making in a mass-customization industry 4.0 environment : Application to Total Manufacturing Quality 4.0
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