FBMTP: An Automated Fault and Behavioral Anomaly Detection and Isolation Tool for PLC-Controlled Manufacturing Systems

2017 ◽  
Vol 47 (12) ◽  
pp. 3397-3417 ◽  
Author(s):  
Arup Ghosh ◽  
Shiming Qin ◽  
Jooyeoun Lee ◽  
Gi-Nam Wang
Keyword(s):  
Anomaly Detection ◽  
Manufacturing Systems ◽  
Behavioral Anomaly Detection

Behavioral anomaly detection of malware on home routers

2017 ◽  
Author(s):  
Ni An ◽  
Alexander Duff ◽  
Gaurav Naik ◽  
Michalis Faloutsos ◽  
Steven Weber ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Behavioral Anomaly Detection

scholarly journals Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5766
Author(s):  
Xinmiao Sun ◽  
Ruiqi Li ◽  
Zhen Yuan
Keyword(s):  
Anomaly Detection ◽  
Manufacturing Systems ◽  
Sequence Data ◽  
Discrete Event ◽  
Intelligent Manufacturing ◽  
System Structure ◽  
Data Set ◽  
Structure Information ◽  
Discrete Manufacturing ◽  
Repeated Events

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.

A Secure Wireless Sensor Network Framework for Manufacturing Monitoring

2010 ◽  
Vol 20-23 ◽  
pp. 249-253
Author(s):  
Yu Xin Mao ◽  
Wen Ya Tian
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Anomaly Detection ◽  
Sensor Network ◽  
Manufacturing Systems ◽  
Point Of View ◽  
Wireless Sensor ◽  
Layered Architecture ◽  
Detection Technology ◽  
Overall Performance

Wireless sensor networks are going to play a more and more important role in applications of next generation manufacturing systems. In this paper, we present a secure and intelligent wireless sensor network framework for manufacturing monitoring. We try to solve the problem of manufacturing monitoring from a new point of view. We introduce the layered architecture of the wireless sensor network framework in detail. Considering the vulnerable feature of Wireless sensor network, we also propose a secure mechanism based on the structure of wireless sensor network by using the anomaly detection technology. The major contribution of this paper is that we integrate wireless sensor network with secure and intelligent technologies to improve the overall performance of manufacturing monitoring.

A Blended System Dynamics-Discrete Event Physics-Based Model for Anomaly Detection in Cyber-Physical Manufacturing Systems

2020 ◽  
Author(s):  
Hong Yu ◽  
Ajay Raghavan ◽  
Saman Mostafavi ◽  
Deokwoo Jung ◽  
Yukinori Sasaki ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
System Dynamics ◽  
Discrete Event Simulation ◽  
Manufacturing Systems ◽  
Discrete Event ◽  
Continuous Variables ◽  
Event Simulation ◽  
Computational Overhead ◽  
Discrete Manufacturing System ◽  
Discrete Manufacturing

Abstract Being able to quickly detect anomalies and reason about their root causes in critical manufacturing systems can significantly reduce the analysis time to bring operations back online, thus reducing expensive unplanned downtime. Machine learning-based anomaly detection approaches often need significant amounts of labeled data for training and are challenging to scale for manufacturing deployments. A robust blended system dynamics and discrete event simulation physics-based modeling methodology is proposed for the task of automated anomaly detection. The blended model consists of discrete event simulation (DES) components for the discrete manufacturing process modeling, and system dynamics (SD) components for continuous variables. The methodology strikes a balance between the computational overhead for online monitoring and the level of details required to perform anomaly detection tasks. The implementation of models takes an object-oriented approach, allowing multiple components of a smart factory to be robustly described in a modular, extendable and reconfigurable manner. The proposed methodology is applied to and validated by data collected from a real commercial manufacturing plant. A production line is modeled with DES components and heat transfer is modeled with SD. The blended model is then utilized for anomaly detection. It is demonstrated that the model-based approach is effective not only for detecting but also explaining particular types of anomalies in a commercial discrete manufacturing system.

scholarly journals Anomaly Detection and Diagnosis In Manufacturing Systems

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Kalyani Zope ◽  
Kuldeep Singh ◽  
Sri Harsha Nistala ◽  
Arghya Basak ◽  
Pradeep Rathore ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Manufacturing Systems ◽  
Predictive Maintenance ◽  
Sensor Data ◽  
Statistical Techniques ◽  
Industrial Systems ◽  
Learning Techniques ◽  
Detection And Diagnosis

Multivariate sensor data collected from manufacturing and process industries represents actual operational behavior and can be used for predictive maintenance of the plants. Anomaly detection and diagnosis, that forms an integral part of predictive maintenance, in industrial systems is however challenging due to their complex behavior, interactions among sensors, corrective actions of control systems and variability in anomalous behavior. While several statistical techniques for anomaly detection have been in use for a long time, these are not particularly suited for temporal (or contextual) anomalies that are characteristic of multivariate time series sensor data. On the other hand, several machine learning and deep learning techniques for anomaly detection gained significant interest in the recent years. Further, anomaly diagnosis that involves localization of the faults did not receive much attention. In this work, we compare the anomaly detection and diagnosis capabilities, in semi-supervised mode, of several statistical, machine learning and deep learning techniques on two systems viz. the interacting quadruple tank system and the continuous stirred tank reactor (CSTR) system both of which are representative of the complexity of large industrial systems. The techniques studied include principal component analysis (PCA), Mahalanobis distance (MD), one-class support vector machine (OCSVM), isolation forest, elliptic envelope, dense auto-encoder and long short term memory auto-encoder (LSTM AE). The study revealed that MD and LSTM-AE have the highest anomaly detection capability, followed closely by PCA and OCSVM. The above techniques also exhibited good diagnosis capability. The study indicates that statistical techniques in spite of their simplicity could be as powerful as machine learning and deep learning techniques, and may be considered for anomaly detection and diagnosis in manufacturing systems.

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