DeepAD: A Generic Framework Based on Deep Learning for Time Series Anomaly Detection

Buda, Teodora Sandra; Çağlayan, Bora; Assem, Haytham

doi:10.1007/978-3-319-93034-3_46

Cited by 52 publications

(28 citation statements)

References 11 publications

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“…As an advanced extension to ANNs, deep neural networks have recently been utilized in a broad range of successful academic and industrial modeling applications. Some practical examples include image and object classification and recognition [96][97][98][99] , (medical) image and video analysis [100][101][102][103] , and speech and face recognition [104][105][106][107][108] . Specifically, convolutional neural networks (CNNs) [109] proposed by Hubel and Wiesel in 1962 [110] , have been frequently used among other deep learning techniques.…”

Section: Deep Learningmentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

253

152

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In the last two decades, improvements in materials, sensors and machine learning technologies have led to a rapid extension of electronic nose (EN) related research topics with diverse applications. The food and beverage industry, agriculture and forestry, medicine and health-care, indoor and outdoor monitoring, military and civilian security systems are the leading fields which take great advantage from the rapidity, stability, portability and compactness of ENs. Although the EN technology provides numerous benefits, further enhancements in both hardware and software components are necessary for utilizing ENs in practice. This paper provides an extensive survey of the EN technology and its wide range of application fields, through a comprehensive analysis of algorithms proposed in the literature, while exploiting related domains with possible future suggestions for this research topic.

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Section: Deep Learningmentioning

confidence: 99%

Electronic Nose and Its Applications: A Survey

Karakaya

Ulucan

Türkan

2019

Int. J. Autom. Comput.

253

152

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“…However, the application of time series anomaly detection techniques in industrial environments was studied mostly in more recent works [20,21], also tackling specific applications such as natural gas [22], building power consumption [23], or water management systems [24]. Some authors also proposed generic anomaly detection frameworks [25,26].…”

Section: Related Workmentioning

confidence: 99%

Application of Unsupervised Anomaly Detection Techniques to Moisture Content Data from Wood Constructions

Faura

Štepec

Cankar

et al. 2021

Forests

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Wood is considered one of the most important construction materials, as well as a natural material prone to degradation, with fungi being the main reason for wood failure in a temperate climate. Visual inspection of wood or other approaches for monitoring are time-consuming, and the incipient stages of decay are not always visible. Thus, visual decay detection and such manual monitoring could be replaced by automated real-time monitoring systems. The capabilities of such systems can range from simple monitoring, periodically reporting data, to the automatic detection of anomalous measurements that may happen due to various environmental or technical reasons. In this paper, we explore the application of Unsupervised Anomaly Detection (UAD) techniques to wood Moisture Content (MC) data. Specifically, data were obtained from a wood construction that was monitored for four years using sensors at different positions. Our experimental results prove the validity of these techniques to detect both artificial and real anomalies in MC signals, encouraging further research to enable their deployment in real use cases.

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“…This renders most of the current SOTA DNN models incompatible with the approach and thus, limits the ability to use off-the-shelf architectures for the task at hand. Buda et al (2018) [21] leveraged the capabilities of statistical models to aid the neural network in producing accurate forecasting results. These forecasting results were ultimately used for anomaly detection.…”

Section: Related Workmentioning

confidence: 99%