Model-Based Anomaly Detection for Discrete Event Systems

Klerx, Timo; Anderka, Maik; Büning, Hans Kleine; Priesterjahn, Steffen

doi:10.1109/ictai.2014.105

Cited by 18 publications

(14 citation statements)

References 10 publications

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“…The objective of this review was to identify, assess, and analyze the state-of-the-art machine applications in BG pattern classifications and anomaly detection: hyperglycemia, hypoglycemia, and GV classification and detection. According to the reviewed literature, the anomaly classification and detection approach could be roughly categorized as either a classifier-based or a model-based approach [19,21]. The classifier-based approach mainly relies on using either a specified threshold or some kinds of rules to classify the BG levels as either normal or abnormal.…”

Section: Discussionmentioning

confidence: 99%

“…The unsupervised approach does not require any reference data labels, where normal behaviors have to be determined dynamically, and the detections are mainly performed with regard to the entire datasets. The model-based strategies can be considered as a diagnosis of the system’s behavior during abnormal situations through modeling and adequately characterizing the system’s behavior during normal situations [19,21]. It uses a system’s model to either estimate or predict the underlying system (process) dynamics to capture anomalies in the data.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

112

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Background Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the differenc...

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

112

View full text Add to dashboard Cite

show abstract

“…On the other hand, smart devices (i.e., sensors, controllers) and wireless networks make the data collection and distribution to computers easier, thus promoting extensive researches on data-based anomaly detection algorithms. Based on the type of data, anomaly detection algorithms can deal with time-series data [ 9 , 10 , 11 ], discrete data [ 12 , 13 , 14 , 15 ], or mixed types of data [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…The shortcoming of window-based algorithms is that it is sensitive to the selection of k , cannot be optimized easily. The Markovian, HMM, or Automata (see [ 15 ]) model-based algorithms focus on modeling the transition of states, which cannot reflect parallel processes, since parallel transitions may result in the same state changes as sequential processes. Recently, machine learning and deep learning based algorithms spring up, such as in [ 10 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches

Sun

Zhen

2020

Sensors

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Anomaly detection for discrete manufacturing systems is important in intelligent manufacturing. In this paper, we address the problem of anomaly detection for the discrete manufacturing systems with complicated processes, including parallel processes, loop processes, and/or parallel with nested loop sub-processes. Such systems can generate a series of discrete event data during normal operations. Existing methods that deal with the discrete sequence data may not be efficient for the discrete manufacturing systems or methods that are dealing with manufacturing systems only focus on some specific systems. In this paper, we take the middle way and seek to propose an efficient algorithm by applying only the system structure information. Motivated by the system structure information that the loop processes may result in repeated events, we propose two algorithms—centralized pattern relation table algorithm and parallel pattern relation table algorithm—to build one or multiple relation tables between loop pattern elements and individual events. The effectiveness of the proposed algorithms is tested by two artificial data sets that are generated by Timed Petri Nets. The experimental results show that the proposed algorithms can achieve higher AUC and F1-score, even with smaller sized data set compared to the other algorithms and that the parallel algorithm achieves the highest performance with the smallest data set.

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“…Twitter released its own open-source anomaly detection algorithms for time series data, it is capable of detecting spatial and temporal anomalies, and has gotten a relatively high score in the Numenta Anomaly Benchmark (NAB) scoring mechanism [ 7 , 8 ]. In addition, there are a number of model-based methods applied to specific fields, examples include detection for cloud data center temperatures [ 9 ], ATM fraud detection [ 10 ], anomaly detection in aircraft engine measurements [ 11 ], and some excellent work based on an accurate forecasting solutions with application to the water sector [ 12 , 13 ] so on.…”

Section: Introductionmentioning

confidence: 99%

Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Ding

Gao

et al. 2018

Sensors

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Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.

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Model-Based Anomaly Detection for Discrete Event Systems

Cited by 18 publications

References 10 publications

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches

Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

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