Multi-temporal sequential pattern mining based improvement of alarm management systems

Richard, Karoly; Abonyi, János

doi:10.1109/smc.2016.7844838

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Hu et al [11] held that static alarm analysis was not enough to realize alarm location and used time sequence alarm information and corresponding dynamic time matrix to realize alarm correlation analysis, so as to complete alarm location. Karoly and Abonyi [12] took multiperiod alarm sequences to mine alarm data and complete the compression of alarm data. However, the above research studies have not focused on the network management data, especially when the types of terminal nodes are increasing and the alarm messages are massive and sparse, the redundancy of the algorithm mining results is high, and the alarm transmission analysis is difficult.…”

Section: Related Work E Intrusion Detection System Was First Proposed Bymentioning

confidence: 99%

Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Hóng

Wang

2021

Mobile Information Systems

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In many colleges and universities, MOOCs have been applied in many courses, including ideological and political course, which is very important for college students’ ideological and moral education. Ideological and political MOOCs break the limitations of time and space, and students can conveniently and quickly learn ideological and political courses through the network. However, due to the openness of MOOCs, there may be some abnormal access behaviors, affecting the normal process of MOOCs. Therefore, in this paper, we propose a detection method of abnormal access behavior of ideological and political MOOCs in colleges and universities. Based on deep learning, the network behavior detection model is established to distinguish whether the network behavior is normal, so as to detect the abnormal access network behavior. In order to prove the effectiveness and efficiency of the proposed algorithm, the algorithm is compared with the other two network abnormal behavior detection methods, and the results prove that the proposed method can effectively detect the abnormal access behavior in ideological and political MOOCs.

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Section: Related Work E Intrusion Detection System Was First Proposed Bymentioning

confidence: 99%

Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Hóng

Wang

2021

Mobile Information Systems

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“…The original MATLAB model of the simulator (published in Chen et al) contained 5 disturbances: (1) step change in the composition of C 2 H 6 in the fresh C 2 H 4 feed stream from 0.001 to 0.003 mole fraction, (2) loss of column feed for 5 minutes, (3) loss of fresh HAc feed stream for 5 minutes, (4) loss of fresh O 2 feed stream, and (5) an analyser is off‐line (except the O 2 analyser), while Károly and Abonyi studied the effects of 3 malfunctions (1) loss of HAc feed, (2) loss of O 2 feed, and (3) loss of column feed and studied the effect of product changes with the use of the so‐called Operating State Matrix, containing the randomly generated values of the following manipulated variables: Operating state start time (min) Operating state end time (min) Set point of the reactor output temperature (150−165° C ) H 2 O composition in the column's bottom (9−18 % ) Vaporiser feed (2.2−2.6

\frac{k m o l}{m i n}

) Change of the C 2 H 6 concentration of the C 2 H 4 feed from 0.1% to 0.3% (not range based, only 2 states) …”

Section: Case Studymentioning

confidence: 99%

Understanding the importance of process alarms based on the analysis of deep recurrent neural networks trained for fault isolation

Dörgő

Pigler

Abonyi

2018

Journal of Chemometrics

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The identification of process faults is a complex and challenging task due to the high amount of alarms and warnings of control systems. To extract information about the relationships between these discrete events, we utilise multitemporal sequences of alarm and warning signals as inputs of a recurrent neural network-based classifier and visualise the network by principal component analysis. The similarity of the events and their applicability in fault isolation can be evaluated based on the linear embedding layer of the network, which maps the input signals into a continuous-valued vector space. The method is demonstrated in a simulated vinyl acetate production technology. The results illustrate that with the application of recurrent neural network-based sequence learning not only accurate fault classification solutions can be developed, but the visualisation of the model can give useful hints for hazard analysis. KEYWORDS deep learning, fault classification, visualisation of discrete eventsNomenclature: RNN, -recurrent neural network; LSTM, -long short-term memory; PCA, -principal component analysis; VAc, -vinyl acetate; y, -type of fault, = { c 1 , … , c n c } , related to the kth sequence of events.;̂k, -predicted class label of the faults; c j , -jth type fault; n c , -number of fault types; s, -state of the technology; pv, -index of the process variable; a, -the related state signals; e, -event; st, -starting time of an event; et, -ending time of an event; k , -kth sequence of states; S, -the set of states; R, -arbitrary temporal predicate between events; E, -equal temporal predicates between events; B, -before temporal predicates between events; D, -during temporal predicates between events; O, -overlap temporal predicates between events; x k , -the numerical representation of k sequence; P(y k = c j |x k ), -the conditional probability of the given fault; h k ,vector of the activities of the LSTM units; n U , -the number of LSTM units in the RNN; T, -length of the input sequence; W s , -weight matrix of the output layer of the network (referring to the applied softmax function); C t k , -cell state of the tth LSTM unit; t k , -forget gate of the tth LSTM unit; W f , -weight matrix of the forget gate; b, -the bias vector of the corresponding neurons; i t k , -input gate of the tth LSTM unit; o t k , -output gate of the tth LSTM unit; W o , -weight matrix of the output gate; oh t k , -one-hot binary vector representation of the tth symbol of the kth sequence; n s , -the number of states in the D temporal database; n R , -number of types of temporal predicates between events; n o , -number of bits in the one-hot binary vector; n e , -dimension of the embedding layer; W, -weight matrix of embedding layer; sim(s i , s j ), -similarity of the alarms; d max , -the maximum Euclidean distance among the rows of the W matrix;

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“…Cheng et al [6] use a modified Smith-Waterman algorithm to calculate a similarity index of alarm floods by considering the time stamp information. Karoly and Abonyi [19] propose a multi-temporal sequence mining concept to extract patterns and formulate rules for alarm suppression. Xu et al [30] introduce a data driven method for alarm flood pattern matching.…”

Section: Related Workmentioning

confidence: 99%

Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause

Wunderlich

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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In view of the increasing amount of information in the form of alarms, messages or also acoustic signals, the operators of systems are exposed to more workload and stress than ever before. We develop a concept for the reduction of alarm floods in industrial plants, in order to prevent the operators from being overwhelmed by this flood of information. The concept is based on two phases. On the one hand, a learning phase in which a causal model is learned and on the other hand an operating phase in which, with the help of the causal model, the root cause of the alarm sequence is diagnosed. For the causal model, a Bayesian network is used which maps the interrelations between the alarms. Based on this causal model the root cause of an alarm flood can be determined using inference. This not only helps the operator at work, but also increases the safety and speed of the repair. Additionally it saves money and reduces outage time. We implement, describe and evaluate the approach using a demonstrator of a manufacturing plant in the SmartFactoryOWL.

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Multi-temporal sequential pattern mining based improvement of alarm management systems

Cited by 12 publications

References 20 publications

Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Abnormal Access Behavior Detection of Ideological and Political MOOCs in Colleges and Universities

Understanding the importance of process alarms based on the analysis of deep recurrent neural networks trained for fault isolation

Concept for Alarm Flood Reduction with Bayesian Networks by Identifying the Root Cause

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