.png){kind=icon}
Return
STEAMCODER: Spatial and Temporal Adaptive Dynamic Convolution Autoencoder for Anomaly Detection
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2023.110929.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The anomaly detection algorithm greatly improves the reliability of equipment operation. Traditional anomaly detection algorithms are mostly designed for large data sets, making it difficult to detect anomalies when where is not enough accumulated equipment data. Therefore, detecting anomalies during the cold-start stage is also challenging. In industrial production, the working conditions of the equipment are constantly adjusted, and the amount or characteristics of data for training also change frequently. Hence, an adaptive algorithm is required to address these problems. We design a novel algorithm called Spatial-TEmporal Adaptive dynaMic Convolution autoencODER for Anomaly Detection (STEAMCODER), specifically. This algorithm first converts the data into a spatial-temporal anomaly feature matrix and then utilizes a dynamic convolution autoencoder to analyze the matrix and detect anomalies. Finally, we conduct extensive experiments to validate the performance of STEAMCODER and the results demonstrated its superiority compared to state-of-the-art algorithms in adaptive anomaly detection, including F1 score and other indicators. Furthermore, STEAMCODER is capable of filtering false positives caused by glitch data, enabling the early detection of equipment anomalies. (c) 2023 Elsevier B.V. All rights reserved.
Keywords:
Anomaly detection
Dynamic CNN
Autoencoder
Journal
K
IF:
7.6
Papers:
1.3W
Citations:
4.5W
Organization
Cited Papers
LogEvent2vec: LogEvent-to-Vector Based Anomaly Detection for Large-Scale Logs in Internet of Things.png)
SENSORS
OAAI
SENSORSIF3.5
OAAIShareSaveElectrothermal Model for Power LEDs Based on the Equivalent Resistance Concept for LED Driver Design
PREAI
ShareSaveShareSaveDeep anomaly detection with self-supervised learning and adversarial trainingPATTERN RECOGNITION
PATTERN RECOGNITIONIF7.6
PREAI
ShareSaveData-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling MethodsSENSORSOAAI
SENSORSIF3.5
OAAIShareSaveShareSaveShareSave
View more