A Biological Immunity-Inspired Novelty Detection Algorithm for Rotor System Monitoring

2005 ◽  
Vol 293-294 ◽  
pp. 71-78 ◽  
Author(s):  
Yong Gui Dong ◽  
Ensheng Dong ◽  
Huibo Jia ◽  
Wener Lv
Keyword(s):  
Time Series ◽  
Negative Selection ◽  
Time Series Data ◽  
Detection Efficiency ◽  
Novelty Detection ◽  
Rotor System ◽  
Series Data ◽  
Selection Algorithm ◽  
Detection Techniques ◽  
Negative Selection Algorithm

In case of mechanical system health monitoring, a need to develop normal-knowledge based novelty detection techniques is increasing. The negative selection algorithm, which is inspired from the operation mechanism of human immune system, is one of such approaches. Our approach is to apply the idea for the anomaly detection in the vibration time series of the rotor system. A real-valued negative selection algorithm based on Euclidean distance, as well as cosine similarity, has been implemented. By means of adding the corresponding coverage radius to each antibody elements, the detection efficiency of each antibody element is increased. The detection efficiency is evaluated with simulated data as well as vibration signal sampled from one rotor system. The results indicate that the algorithm can efficiently detect the anomaly in time series data. Moreover, the number of detectors in antibody set is less enough for potential application in online signal monitoring.

scholarly journals Comparison of outlier detection techniques in non-stationary time series data

2021 ◽  
Vol 27 (1) ◽  
pp. 55-60
Author(s):  
Sampson Twumasi-Ankrah ◽  
Simon Kojo Appiah ◽  
Doris Arthur ◽  
Wilhemina Adoma Pels ◽  
Jonathan Kwaku Afriyie ◽  
...  
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Mahalanobis Distance ◽  
Time Series Data ◽  
Principal Component ◽  
Stationary Time Series ◽  
Series Data ◽  
Distance Method ◽  
Detection Techniques ◽  
Stationary Time

This study examined the performance of six outlier detection techniques using a non-stationary time series dataset. Two key issues were of interest. Scenario one was the method that could correctly detect the number of outliers introduced into the dataset whiles scenario two was to find the technique that would over detect the number of outliers introduced into the dataset, when a dataset contains only extreme maxima values, extreme minima values or both. Air passenger dataset was used with different outliers or extreme values ranging from 1 to 10 and 40. The six outlier detection techniques used in this study were Mahalanobis distance, depth-based, robust kernel-based outlier factor (RKOF), generalized dispersion, Kth nearest neighbors distance (KNND), and principal component (PC) methods. When detecting extreme maxima, the Mahalanobis and the principal component methods performed better in correctly detecting outliers in the dataset. Also, the Mahalanobis method could identify more outliers than the others, making it the "best" method for the extreme minima category. The kth nearest neighbor distance method was the "best" method for not over-detecting the number of outliers for extreme minima. However, the Mahalanobis distance and the principal component methods were the "best" performed methods for not over-detecting the number of outliers for the extreme maxima category. Therefore, the Mahalanobis outlier detection technique is recommended for detecting outlier in nonstationary time series data.

scholarly journals Comparative performance of selected variability detection techniques in photometric time series data

2016 ◽  
Vol 464 (1) ◽  
pp. 274-292 ◽  
Author(s):  
K. V. Sokolovsky ◽  
P. Gavras ◽  
A. Karampelas ◽  
S. V. Antipin ◽  
I. Bellas-Velidis ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Comparative Performance ◽  
Detection Techniques

Visualizing Change: Using Cartographic Animation to Explore Remotely-Sensed Data

2001 ◽  
pp. 30-42 ◽  
Author(s):  
Mark Harrower
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Time Series Data ◽  
Series Data ◽  
Remotely Sensed Data ◽  
Process Of Change ◽  
Detection Techniques ◽  
Space Time Structure ◽  
Current Change ◽  
Temporal And Spatial

This research describes a geovisualization tool that is designed to facilitate exploration of satellite time-series data. Current change-detection techniques are insufficient for the task of representing the complex behaviors and motions of geographic processes because they emphasize the outcomes of change rather than depict the process of change itself. Cartographic animation of satellite data is proposed as a means of visually summarizing the complex behaviors of geographic entities. Animation provides a means for better understanding the complexity of geographic change because it can represent both the state of a geographic system at a given time (i.e. its space-time structure) and the behavior of that system over time (i.e. trends). However, a simple animation of satellite time-series data is often insufficient for this task because it overwhelms the viewer with irrelevant detail or presents data at an inappropriate temporal and spatial resolution. To solve this problem, dynamic temporal and spatial aggregation tools are implemented with the geovisualization system to allow analysts to change the resolution of their data on the fly. These tools provide (1) a means of detecting structures or trends that may be exhibited only at certain scales and (2) a method for smoothing or filtering unwanted noise from the satellite data. This research is grounded in a delineation of the nature of change, and proposes a framework of four kinds of geographic change: location, size/extent, attribute and existence. Each of these kinds of change may be continuous (a process) or discrete (an event).

scholarly journals A Review on Outlier/Anomaly Detection in Time Series Data

2021 ◽  
Vol 54 (3) ◽  
pp. 1-33
Author(s):  
Ane Blázquez-García ◽  
Angel Conde ◽  
Usue Mori ◽  
Jose A. Lozano
Keyword(s):  
Time Series ◽  
Outlier Detection ◽  
Time Series Data ◽  
State Of The Art ◽  
Series Data ◽  
Detection Techniques ◽  
The Past ◽  
Time Series Mining ◽  
Detection Of Outliers ◽  
Unsupervised Outlier Detection

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

scholarly journals Evaluation metrics for anomaly detection algorithms in time-series

2019 ◽  
Vol 11 (2) ◽  
pp. 113-130 ◽  
Author(s):  
György Kovács ◽  
Gheorghe Sebestyen ◽  
Anca Hangan
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Confusion Matrix ◽  
Series Data ◽  
Time Data ◽  
Temporal Dimension ◽  
Detection Techniques ◽  
Detection Algorithms

Abstract Time-series are ordered sequences of discrete-time data. Due to their temporal dimension, anomaly detection techniques used in time-series have to take into consideration time correlations and other time-related particularities. Generally, in order to evaluate the quality of an anomaly detection technique, the confusion matrix and its derived metrics such as precision and recall are used. These metrics, however, do not take this temporal dimension into consideration. In this paper, we propose three metrics that can be used to evaluate the quality of a classification, while accounting for the temporal dimension found in time-series data.

Negative Selection Algorithm Using Natural Frequency for Novelty Detection under Temperature Variations

2010 ◽  
Vol 163-167 ◽  
pp. 2747-2750
Author(s):  
Miao Li ◽  
Wei Xin Ren
Keyword(s):  
Natural Frequency ◽  
Negative Selection ◽  
Euclidean Distance ◽  
Natural Frequencies ◽  
Novelty Detection ◽  
Selection Algorithm ◽  
Temperature Variations ◽  
Negative Selection Algorithm ◽  
Environment Effects ◽  
Anomalous Condition

The vibration features are affected by damage in structure and environmental conditions while the bridges are in the operation. Environment effects should not be ignored in making correct diagnoses of structures. Negative selection algorithm inspired by immune system has the capability for self-nonself discrimination. Temperature effect on natural frequency is analyzed in the paper, and the algorithm based on Euclidean distance is applied to natural frequencies of structures under temperature variations. The results indicate that negative selection algorithm using natural frequency passes the false-positive tests, and effectively detect the anomalous condition of structure under varying temperature.

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