Telemetry data play a pivotal role in ensuring the success of spacecraft missions and safeguarding the integrity of spacecraft systems. Therefore, the timely detection and subsequent notification of any abnormal events related to the functionality of...
Anomalies are infrequent in nature, but detecting these anomalies could be crucial for the proper functioning of any system. The rarity of anomalies could be a challenge for their detection as detection models are required to depend on the relations...
With the rapid development of emerging technologies such as self-media, the Internet of Things, and cloud computing, massive data applications are crossing the threshold of the era of real-time analysis and value realization, which makes data streams...
Context—Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence and machine learning techniques to either log file...
Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series stre...
Smart agriculture technologies are effective instruments for increasing farm sustainability and production. They generate many spatial, temporal, and time-series data streams that, when analysed, can reveal several issues on farm productivity and eff...
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topi...
Anomaly detection in safety-critical systems often operates under severe label constraints, where only a small subset of normal and anomalous samples can be reliably annotated, while large unlabeled data streams are contaminated and high-dimensional....
With the proliferation of the Internet of Things, a large amount of data is generated constantly by industrial systems, corresponding in many cases to critical tasks. It is particularly important to detect abnormal data to ensure the accuracy of data...
Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved i...
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal heal...
The early detection and tracing of anomalous operations in battery packs are critical to improving performance and ensuring safety. This paper presents a data-driven approach for online anomaly detection in battery packs that uses real-time voltage a...
Background: Genetic variation provides a foundation for understanding evolution. With the rise of artificial intelligence, machine learning has emerged as a powerful tool for identifying genomic footprints of evolutionary processes through simulation...
The spatial–temporal correlation is an important feature of sensor data in wireless sensor networks (WSNs). Most of the existing works based on the spatial–temporal correlation can be divided into two parts: redundancy reduction and anomaly detection...
In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KP...
There is a growing interest in safety warning of underground mining due to the huge threat being faced by those working in underground mining. Data acquisition of sensors based on Internet of Things (IoT) is currently the main method, but the data an...
Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, th...
Structural health inspection systems are widely used to manage and maintain infrastructure that involves massive sensor devices. However, these sensors receive the natural environment or external factors in the long-term exposure to the outdoor envir...
Network traffic datasets are essential for the construction of traffic models, often using machine learning (ML) techniques. Among other applications, these models can be employed to solve complex optimization problems or to identify anomalous behavi...
Anomaly detection is the problem of finding patterns in data that do not conform to an a priori expected behavior. This is related to the problem in which some samples are distant, in terms of a given metric, from the rest of the dataset, where these...
Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly...
Big data has emerged as a fundamental component in various domains, enabling organizations to extract valuable insights and make informed decisions. However, ensuring data quality is crucial for effectively using big data. Thus, big data quality has...
Anomaly detection on time series data has been successfully used in power grid operation and maintenance, flow detection, fault diagnosis, and other applications. However, anomalies in time series often lack strict definitions and labels, and existin...
To effectively detect and identify the anomaly data in massive satellite telemetry data sets, the novel detection and identification method based on the pseudo-period was proposed in this paper. First, the raw data were compressed by extracting the s...
Addressing data anomalies (e.g., garbage data, outliers, redundant data, and missing data) plays a vital role in performing accurate analytics (billing, forecasting, load profiling, etc.) on smart homes’ energy consumption data. From the litera...
Cyber threats to industrial control systems (ICSs) have increased as information and communications technology (ICT) has been incorporated. In response to these cyber threats, we are implementing a range of security equipment and specialized training...
This work presents a new methodology for machine tools anomaly detection via operational data processing. The previous methodology has been field tested on a milling-boring machine in a real production environment. This paper also describes the data...
The development and integration of information technology and industrial control networks have expanded the magnitude of new data; detecting anomalies or discovering other valid information from them is of vital importance to the stable operation of...
Recently, OT (operational technology) networks of industrial control systems have been combined with IT networks. Therefore, OT networks have inherited the vulnerabilities and attack paths existing in IT networks. Consequently, attacks on industrial...
Industrial anomaly detection, which relies on the analysis of industrial internet of things (IIoT) sensor data, is a critical element for guaranteeing the quality and safety of industrial manufacturing. Current solutions normally apply edge–clo...
Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse...
This paper investigates the use of an unsupervised hybrid statistical–local outlier factor algorithm to detect anomalies in time-series flight data. Flight data analysis is an activity carried out by airlines primarily as a means of improving t...
The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data an...
1 February 2023
Data analysis is an important part of aero engine health management. In order to complete accurate condition monitoring, it is necessary to establish more effective analysis tools. Therefore, an integrated algorithm library dedicated for engine anoma...
When analyzing smart metering data, both reading errors and frauds can be identified. The purpose of this analysis is to alert the utility companies to suspicious consumption behavior that could be further investigated with on-site inspections or oth...
Anomaly detection is a common analytical task aimed at identifying rare cases that differ from the majority of typical cases in a dataset. In the management of photovoltaic (PV) power generation systems, it is essential for electric power companies t...
Time series data are significant, and are derived from temporal data, which involve real numbers representing values collected regularly over time. Time series have a great impact on many types of data. However, time series have anomalies. We introdu...
Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detec...
Industrial control systems (ICS) are critical networks directly linked to the value of core national and societal assets, yet they are increasingly becoming primary targets for numerous cyberattacks today. The ICS network, a fusion of operational tec...
Structural health monitoring (SHM) has been extensively utilized in civil infrastructures for several decades. The status of civil constructions is monitored in real time using a wide variety of sensors; however, determining the true state of a struc...
Disease surveillance is used to monitor ongoing control activities, detect early outbreaks, and inform intervention priorities and policies. However, data from disease surveillance that could be used to support real-time decisionmaking remain largely...
Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most set...
This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the...
The effect of the application of machine learning on data streams is influenced by concept drift, drift deviation, and noise interference. This paper proposes a data stream anomaly detection algorithm combined with control chart and sliding window me...
Anomaly-based attack detection methods depend on some form of machine learning to detect data falsification attacks in smart living cyber–physical systems. However, there is a lack of studies that consider the presence of attacks during the tra...
Multivariate time series data (MTSD) anomaly detection due to complex spatio-temporal dependencies among sensors and pervasive environmental noise. The existing methods struggle to balance anomaly detection accuracy with robustness against data conta...
Blockchain and Data Mining are not simply buzzwords, but rather concepts that are playing an important role in the modern Information Technology (IT) revolution. Blockchain has recently been popularized by the rise of cryptocurrencies, while data min...
Detecting anomalies in engine and machinery data during ship operations is crucial for maintaining the safety and efficiency of the vessel. We conducted experiments using device data from the maritime industry, consisting of time series records from...
Anomaly detection in structured data presents significant challenges, particularly in scenarios with extreme class imbalance. The Siamese Neural Network (SNN) is traditionally recognized for its ability to measure pairwise similarities, rather than b...
Anomaly detection has an important impact on the development of unmanned aerial vehicles, and effective anomaly detection is fundamental to their utilization. Traditional anomaly detection discriminates anomalies for single-dimensional factors of sen...
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