Deep LSTM Transfer Learning for Personalized ECG Anomaly Detection on Wearable Devices

2021 ◽  
pp. 348-357
Author(s):  
Xin Deng ◽  
Jianqiao Zhou ◽  
Bin Xiao ◽  
Xiaohong Xiang ◽  
Yi Yuan
Keyword(s):  
Anomaly Detection ◽  
Transfer Learning ◽  
Wearable Devices

scholarly journals An Enterprise Time Series Forecasting System for Cloud Applications Using Transfer Learning

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1590
Author(s):  
Arnak Poghosyan ◽  
Ashot Harutyunyan ◽  
Naira Grigoryan ◽  
Clement Pang ◽  
George Oganesyan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning ◽  
Performance Monitoring ◽  
Nonlinear Problems ◽  
Time Series Forecasting ◽  
Stationary Time Series ◽  
Learning Gain ◽  
Application Performance ◽  
Cloud Applications

The main purpose of an application performance monitoring/management (APM) software is to ensure the highest availability, efficiency and security of applications. An APM software accomplishes the main goals through automation, measurements, analysis and diagnostics. Gartner specifies the three crucial capabilities of APM softwares. The first is an end-user experience monitoring for revealing the interactions of users with application and infrastructure components. The second is application discovery, diagnostics and tracing. The third key component is machine learning (ML) and artificial intelligence (AI) powered data analytics for predictions, anomaly detection, event correlations and root cause analysis. Time series metrics, logs and traces are the three pillars of observability and the valuable source of information for IT operations. Accurate, scalable and robust time series forecasting and anomaly detection are the requested capabilities of the analytics. Approaches based on neural networks (NN) and deep learning gain an increasing popularity due to their flexibility and ability to tackle complex nonlinear problems. However, some of the disadvantages of NN-based models for distributed cloud applications mitigate expectations and require specific approaches. We demonstrate how NN-models, pretrained on a global time series database, can be applied to customer specific data using transfer learning. In general, NN-models adequately operate only on stationary time series. Application to nonstationary time series requires multilayer data processing including hypothesis testing for data categorization, category specific transformations into stationary data, forecasting and backward transformations. We present the mathematical background of this approach and discuss experimental results based on implementation for Wavefront by VMware (an APM software) while monitoring real customer cloud environments.

Spacecraft Time-Series Anomaly Detection Using Transfer Learning

2021 ◽  
Author(s):  
Sriram Baireddy ◽  
Sundip R. Desai ◽  
James L. Mathieson ◽  
Richard H. Foster ◽  
Moses W. Chan ◽  
...  
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Transfer Learning

Anomaly detection of power consumption in yarn spinning using transfer learning

2021 ◽  
Vol 152 ◽  
pp. 107015
Author(s):  
Chuqiao Xu ◽  
Junliang Wang ◽  
Jie Zhang ◽  
Xiaoou Li
Keyword(s):  
Anomaly Detection ◽  
Power Consumption ◽  
Transfer Learning

scholarly journals Transfer Learning Strategies for Anomaly Detection in IoT Vibration Data

2021 ◽  
Author(s):  
Clemens Heistracher ◽  
Anahid Jalali ◽  
Indu Strobl ◽  
Axel Suendermann ◽  
Sebastian Meixner ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Learning Strategies ◽  
Transfer Learning ◽  
Training Data ◽  
Sensor Data ◽  
Classification Models ◽  
Vibration Data ◽  
Starting Point ◽  
Wide Range ◽  
Anomaly Classification

<div>Abstract—An increasing number of industrial assets are equipped with IoT sensor platforms and the industry now expects data-driven maintenance strategies with minimal deployment costs. However, gathering labeled training data for supervised tasks such as anomaly detection is costly and often difficult to implement in operational environments. Therefore, this work aims to design and implement a solution that reduces the required amount of data for training anomaly classification models on time series sensor data and thereby brings down the overall deployment effort of IoT anomaly detection sensors. We set up several in-lab experiments using three peristaltic pumps and investigated approaches for transferring trained anomaly detection models across assets of the same type. Our experiments achieved promising effectiveness and provide initial evidence that transfer learning could be a suitable strategy for using pretrained anomaly classification models across industrial assets of the same type with minimal prior labeling and training effort. This work could serve as a starting point for more general, pretrained sensor data embeddings, applicable to a wide range of assets.</div>

scholarly journals Latent-Insensitive Autoencoders for Anomaly Detection

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 112
Author(s):  
Muhammad S. Battikh ◽  
Artem A. Lenskiy
Keyword(s):  
Anomaly Detection ◽  
Qualitative Analysis ◽  
Performance Improvement ◽  
Transfer Learning ◽  
Loss Functions ◽  
Training Process ◽  
Theoretical Justification ◽  
Significant Performance ◽  
Ablation Study ◽  
Similar Domain

Reconstruction-based approaches to anomaly detection tend to fall short when applied to complex datasets with target classes that possess high inter-class variance. Similar to the idea of self-taught learning used in transfer learning, many domains are rich with similar unlabeled datasets that could be leveraged as a proxy for out-of-distribution samples. In this paper we introduce the latent-insensitive autoencoder (LIS-AE) where unlabeled data from a similar domain are utilized as negative examples to shape the latent layer (bottleneck) of a regular autoencoder such that it is only capable of reconstructing one task. We provide theoretical justification for the proposed training process and loss functions along with an extensive ablation study highlighting important aspects of our model. We test our model in multiple anomaly detection settings presenting quantitative and qualitative analysis showcasing the significant performance improvement of our model for anomaly detection tasks.

scholarly journals Anomaly detection of industrial control systems based on transfer learning

2021 ◽  
Vol 26 (6) ◽  
pp. 821-832
Author(s):  
Weiping Wang ◽  
Zhaorong Wang ◽  
Zhanfan Zhou ◽  
Haixia Deng ◽  
Weiliang Zhao ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Control Systems ◽  
Transfer Learning ◽  
Industrial Control ◽  
Industrial Control Systems
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