Automated Real Time Anomaly Detection Model for Operation and Production Data at Scale

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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APF-PF: The new real-time anomaly detection model of massive log flow

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess.2016.7883198 ◽

2016 ◽

Author(s):

Qing Wu ◽

Guoping Wu

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Detection Model

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Self-Adjusting Anomaly Detection Model for Well Operation and Production in Real-Time

10.2118/195234-ms ◽

2019 ◽

Author(s):

Jon Snyder ◽

Stuart Scott ◽

Rashid Kassim

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Detection Model

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A novel abnormal detection method for bearing temperature based on spatiotemporal fusion

Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit ◽

10.1177/09544097211022105 ◽

2021 ◽

pp. 095440972110221

Author(s):

Yong Zhi Liu ◽

Yi Sheng Zou ◽

Yu Wu ◽

Hao Yang Zhang ◽

Guo Fu Ding

Keyword(s):

Decision Making ◽

Anomaly Detection ◽

Real Time ◽

Early Warning ◽

High Speed ◽

Entropy Method ◽

High Speed Train ◽

Detection Model ◽

Time Prediction ◽

Model Based

The existing bearing temperature fault detection and early warning system has a high false alarm rate and insufficient early warning ability. For this reason, in this study, a method for detecting the abnormal bearing temperature of high-speed trains based on spatiotemporal fusion decision-making was proposed. First, the temperature characteristics of similar bearings were compared and analyzed with different spatial distributions. Then, a bearing abnormal temperature rise detection model based on the analytic hierarchy process (AHP) entropy method was proposed. Second, the temperature characteristics of the same bearings were compared and analyzed with different time distributions. A real-time prediction model of high-speed train bearing temperature anomalies based on Bi-directional Long Short-Term Memory (BILSTM) was proposed. Finally, the D-S evidence theory was used to combine the anomaly detection model based on the AHP entropy method and the anomaly detection model based on BILSTM real-time prediction. Through the comprehensive diagnosis and decision-making of high-speed train bearings from two dimensions of space and time, a more comprehensive and accurate anomaly detection model was realized. The experimental results showed that the spatiotemporal comparison fusion decision model successfully eliminated the misjudgment phenomenon of single-dimension model diagnosis and that it has good early warning ability.

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ReRe: A Lightweight Real-Time Ready-to-Go Anomaly Detection Approach for Time Series

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) ◽

10.1109/compsac48688.2020.0-226 ◽

2020 ◽

Author(s):

Ming-Chang Lee ◽

Jia-Chun Lin ◽

Ernst Gunner Gan

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Real Time ◽

Detection Approach

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Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽

10.3390/s21144836 ◽

2021 ◽

Vol 21 (14) ◽

pp. 4836

Author(s):

Liping Zhang ◽

Yifan Hu ◽

Qiuhua Tang ◽

Jie Li ◽

Zhixiong Li

Keyword(s):

Decision Making ◽

Data Base ◽

Real Time ◽

Manufacturing Industry ◽

Job Shop ◽

Data Driven ◽

Dispatching Rules ◽

Shop Floor ◽

Production Data ◽

Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

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