Autoencoder and Incremental Clustering-Enabled Anomaly Detection

Autoencoder and Incremental Clustering-Enabled Anomaly Detection

Many machine-learning-enabled approaches towards anomaly detection depend on the availability of vast training data. Our data are formed from power readings of cycles from domestic appliances, such as dishwashers or washing machines, and contain no known examples of anomalous behaviour. Moreover, we...

Full description

Bibliographic Details
Main Authors:	Andrew Charles Connelly, Syed Ali Raza Zaidi, Des McLernon
Format:	Article
Language:	English
Published:	MDPI AG 2023-04-01
Series:	Electronics
Subjects:	autoencoder anomaly detection clustering time series unsupervised
Online Access:	https://www.mdpi.com/2079-9292/12/9/1970

Similar Items

UNSUPERVISED PROBABILISTIC ANOMALY DETECTION OVER NOMINAL SUBSYSTEM EVENTS THROUGH A HIERARCHICAL VARIATIONAL AUTOENCODER
by: Alexandre Trilla, et al.
Published: (2023-01-01)

Do Autoencoders Need a Bottleneck for Anomaly Detection?
by: Bang Xiang Yong, et al.
Published: (2022-01-01)

Anomaly Detection of Water Level Using Deep Autoencoder
by: Isack Thomas Nicholaus, et al.
Published: (2021-10-01)

Modified Autoencoder Training and Scoring for Robust Unsupervised Anomaly Detection in Deep Learning
by: Nicholas Merrill, et al.
Published: (2020-01-01)

Anomaly detection in images with shared autoencoders
by: Haoyang Jia, et al.
Published: (2023-01-01)

Cutting anomaly detection in end-milling by multimodal variational autoencoder
by: Kazuya ODA, et al.
Published: (2023-01-01)

BAE: Anomaly Detection Algorithm Based on Clustering and Autoencoder
by: Dongqi Wang, et al.
Published: (2023-08-01)

Deep Convolutional Clustering-Based Time Series Anomaly Detection
by: Gavneet Singh Chadha, et al.
Published: (2021-08-01)

Anomaly detection by using a combination of generative adversarial networks and convolutional autoencoders
by: Xukang Luo, et al.
Published: (2022-11-01)

Anomaly Detection through Grouping of SMD Machine Sounds Using Hierarchical Clustering
by: Young Jong Song, et al.
Published: (2023-06-01)

Comparing Autoencoder and Isolation Forest in Network Anomaly Detection
by: Timotej Smolen, et al.
Published: (2023-05-01)

ViV-Ano: Anomaly Detection and Localization Combining Vision Transformer and Variational Autoencoder in the Manufacturing Process
by: Byeonggeun Choi, et al.
Published: (2022-07-01)

Anomaly detection in radiotherapy plans using deep autoencoder networks
by: Peng Huang, et al.
Published: (2023-03-01)

Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
by: Fatemeh Esmaeili, et al.
Published: (2023-03-01)

Autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
by: Jonas Kohne, et al.
Published: (2023-01-01)

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network
by: Shanshan Jiang, et al.
Published: (2023-06-01)

Anomaly Detection using LSTM AutoEncoder
by: Mahmoud Moallem, et al.
Published: (2019-04-01)

Hyperspectral anomaly detection via memory‐augmented autoencoders
by: Zhe Zhao, et al.
Published: (2023-12-01)

Interpretability-Aware Industrial Anomaly Detection Using Autoencoders
by: Rui Jiang, et al.
Published: (2023-01-01)

Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images
by: Vasilii Mosin, et al.
Published: (2022-11-01)

Unsupervised Anomaly Detection Approach for Time-Series in Multi-Domains Using Deep Reconstruction Error
by: Tsatsral Amarbayasgalan, et al.
Published: (2020-07-01)

Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
by: Lin Wang, et al.
Published: (2022-01-01)

Using Autoencoders for Anomaly Detection and Transfer Learning in IoT
by: Chin-Wei Tien, et al.
Published: (2021-07-01)

Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data
by: Umaporn Yokkampon, et al.
Published: (2022-01-01)

Comparative Study on Three Autoencoder‐Based Deep Learning Algorithms for Geochemical Anomaly Identification
by: Bin Feng, et al.
Published: (2022-11-01)

Quantile Autoencoder With Abnormality Accumulation for Anomaly Detection of Multivariate Sensor Data
by: Seunghyoung Ryu, et al.
Published: (2022-01-01)

Convolutional Autoencoder-Based Anomaly Detection for Photovoltaic Power Forecasting of Virtual Power Plants
by: Taeseop Park, et al.
Published: (2023-07-01)

Anomaly-PTG: A Time Series Data-Anomaly-Detection Transformer Framework in Multiple Scenarios
by: Gang Li, et al.
Published: (2022-11-01)

Stacked Denoising Autoencoder With Density-Grid Based Clustering Method for Detecting Outlier of Wind Turbine Components
by: Zexian Sun, et al.
Published: (2019-01-01)

Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders
by: Quentin Ferré, et al.
Published: (2021-09-01)

Unsupervised Outlier Detection via Transformation Invariant Autoencoder
by: Zhen Cheng, et al.
Published: (2021-01-01)

Dynamic Negative Sampling Autoencoder for Hyperspectral Anomaly Detection
by: Jingxuan Wang, et al.
Published: (2022-01-01)

An autoencoder-based model for forest disturbance detection using Landsat time series data
by: Gaoxiang Zhou, et al.
Published: (2021-09-01)

An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series
by: Tareq Tayeh, et al.
Published: (2022-04-01)

Unsupervised Satellite Image Time Series Clustering Using Object-Based Approaches and 3D Convolutional Autoencoder
by: Ekaterina Kalinicheva, et al.
Published: (2020-06-01)

Anomaly Detection for Agricultural Vehicles Using Autoencoders
by: Esma Mujkic, et al.
Published: (2022-05-01)

Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach
by: Jesmeen M. Z. H., et al.
Published: (2021-12-01)

Clustering of LMS Use Strategies with Autoencoders
by: María J. Verdú, et al.
Published: (2023-06-01)

Unsupervised Deep Anomaly Detection for Industrial Multivariate Time Series Data
by: Wenqiang Liu, et al.
Published: (2024-01-01)

LSTM-Based Autoencoder with Maximal Overlap Discrete Wavelet Transforms Using Lamb Wave for Anomaly Detection in Composites
by: Syed Haider Mehdi Rizvi, et al.
Published: (2024-03-01)