A new method to detect and quantify correlated alarms with occurrence delays

Hu, Wenkai; Wang, Jiandong; Chen, Tongwen

doi:10.1016/j.compchemeng.2015.05.028

Cited by 49 publications

(6 citation statements)

References 10 publications

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“…There is a good deal of related research on the detection of similarity between signals in the field of signal processing. Wenkai Hu et al [25] propose a new method to detect the correlated alarms. They quantify the correlation level based on the Pearson correlation coefficient and show their method perform better in detection correlated alarms and uncorrelated ones than existing methods.…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Cross-Version Binary Code Similarity Detection Based on Similarity and Correlation Coefficient Features

Guo

Huang

et al. 2020

IEEE Access

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The technique of binary code similarity detection (BCSD) has been applied in many fields, such as malware detection, plagiarism detection and vulnerability search, etc. Existing solutions for the BCSD problem usually compare specific features between binaries based on the control flow graphs of functions from binaries or compute the embedding vector of binary functions and solve the problem based on deep learning algorithms. In this paper, from another research perspective, we propose a new and lightweight method to solve cross-version BCSD problem based on multiple features. It transforms binary functions into vectors and signals and computes the similarity coefficient value and correlation coefficient value for solving cross-version BCSD problem. Without relying on the CFG of functions, deep learning algorithms and other related attributes, our method works directly on the raw bytes of each binary and it can be used as an alternative method to coping with various complex situations that exist in the real-world environment. We implement the method and evaluate it on a custom dataset with about 423,282 samples. The result shows that the method could perform well in cross-version BCSD field, and the recall of our method could reach 96.63%, which is almost the same as the state-of-the-art static solution.

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Section: Related Workmentioning

confidence: 99%

A Lightweight Cross-Version Binary Code Similarity Detection Based on Similarity and Correlation Coefficient Features

Guo

Huang

et al. 2020

IEEE Access

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“…There are many methods to calculate the correlation coefficient between alarm time series, as shown in Table 2. 16,17,24 3. BLOCK MATCHING SIMILARITY ANALYSIS 3.1.…”

Section: Correlation Analysis Of Alarm Time Sequencesmentioning

confidence: 99%

“…Correlation coefficient measurement is an important part of the correlation analysis of alarm time sequences. There are many methods to calculate the correlation coefficient between alarm time series, as shown in Table . ,, …”

Section: Analysis Of Alarm Time Sequencesmentioning

confidence: 99%

“…Yang and Wang proposed a statistical test to evaluate whether two alarms are correlated. Hu , considered the problem of random delays between correlated alarms in their detection method and proposed a method to find associations (another form of correlations) between alarms and mode events. Cheng proposed a modified Smith–Waterman algorithm concerning time stamps to calculate a similarity index of alarm flooding.…”

Section: Introductionmentioning

confidence: 99%

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Novel Correlation Analysis of Alarms Based on Block Matching Similarities

Yang

Chu

et al. 2019

Ind. Eng. Chem. Res.

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Correlation analysis has been widely applied for process detection and alarm analysis. However, traditional correlation analysis methods have been found inefficient due to the influence of process time delays on analysis results. In this paper, we propose an alarm correlation analysis method based on block matching similarities. By converting alarm data sequences to time node sequences, the block matching similarity (BMS) method is able to reduce the calculation burden of the correlation analysis. Considering time delays between the alarm variables, the improved maximal correlation coefficient method is presented to reduce the effect of time delays and to improve the accuracy of the correlation analysis. A numerical case and the Tennessee Eastman process are employed to demonstrate the effectiveness and efficiency of the proposed method.

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“…Two main approaches are known. First, correlation analysis-based techniques are widespread, where the aim is to find frequently co-occurring alarms over a short period of time, which can be considered redundant [9]. Second, the frequently occurring longer operational patterns can be considered to be the symptoms of the same malfunction and can be revealed by frequent pattern mining algorithms [10], as well as applied in terms of alarm prediction and suppression [2].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management

et al. 2022

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Alarm management is an important task to ensure the safety of industrial process technologies. A well-designed alarm system can reduce the workload of operators parallel with the support of the production, which is in line with the approach of Industry 5.0. Using Process Mining tools to explore the operator-related event scenarios requires a goal-oriented log file format that contains the start and the end of the alarms along with the triggered operator actions. The key contribution of the work is that a method is presented that transforms the historical event data of control systems into goal-oriented log files used as inputs of process mining algorithms. The applicability of the proposed process mining-based method is presented concerning the analysis of a hydrofluoric acid alkylation plant. The detailed application examples illustrate how the extracted process models can be interpreted and utilized. The results confirm that applying the tools of process mining in alarm management requires a goal-oriented log-file design.

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A new method to detect and quantify correlated alarms with occurrence delays

Cited by 49 publications

References 10 publications

A Lightweight Cross-Version Binary Code Similarity Detection Based on Similarity and Correlation Coefficient Features

A Lightweight Cross-Version Binary Code Similarity Detection Based on Similarity and Correlation Coefficient Features

Novel Correlation Analysis of Alarms Based on Block Matching Similarities

Simultaneous Process Mining of Process Events and Operator Actions for Alarm Management

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