Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches

Xinmiao Sun; Ruiqi Li; Zhen Yuan

doi:10.3390/s20205766

Anomaly Detection in Discrete Manufacturing Systems by Pattern Relation Table Approaches

Sensors ◽

10.3390/s20205766 ◽

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.

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

Volume 2A: Advanced Manufacturing ◽

10.1115/imece2020-24586 ◽

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.

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Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4052762 ◽

2021 ◽

pp. 1-31

Author(s):

Joseph Cohen ◽

Baoyang Jiang ◽

Jun Ni

Keyword(s):

Machine Learning ◽

Fault Diagnosis ◽

Random Forest ◽

Anomaly Detection ◽

Unsupervised Learning ◽

Learning Algorithms ◽

Discrete Event ◽

Machine Learning Algorithms ◽

Programmable Logic ◽

Discrete Manufacturing

Abstract Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based random forest and extreme random forest classifiers achieve excellent performance with a precision and recall score of 0.96 for multi-fault classification. Additionally, the unsupervised learning approach yields anomaly detection rates of 98% with false alarms under 3% with a training set 99% smaller than the supervised learning classifiers. These results obtained on a real use case are promising to enable prognostic tools in industrial automation systems in the future

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

10.1115/1.0004528v ◽

2021 ◽

Author(s):

HONG YU ◽

Ajay Raghavan ◽

Deokwoo Jung ◽

Saman Mostafavi ◽

Yukinori Sasaki ◽

...

Keyword(s):

Anomaly Detection ◽

System Dynamics ◽

Manufacturing Systems ◽

Discrete Event

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8th IFAC International Workshop on Intelligent Manufacturing Systems, 2007

10.3182/20070523-3-es-4908 ◽

2007 ◽

Keyword(s):

Manufacturing Systems ◽

International Workshop ◽

Intelligent Manufacturing ◽

Intelligent Manufacturing Systems

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10th IFAC Workshop on Intelligent Manufacturing Systems (2010)

10.3182/20100701-2-pt-4011 ◽

2010 ◽

Keyword(s):

Manufacturing Systems ◽

Intelligent Manufacturing ◽

Intelligent Manufacturing Systems

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Discrete Event System Control in Max-Plus Algebra: Application to Manufacturing Systems

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2021.04.014 ◽

2020 ◽

Vol 53 (4) ◽

pp. 143-150

Author(s):

Gabriel Freitas Oliveira ◽

Renato Markele Ferreira Candido ◽

Vinicius Mariano Gonçalves ◽

Carlos Andrey Maia ◽

Bertrand Cottenceau ◽

...

Keyword(s):

Manufacturing Systems ◽

Discrete Event ◽

Discrete Event System ◽

System Control ◽

Event System

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Outlier Detection Transilience-Probabilistic Model for Wind Tunnels Based on Sensor Data

Sensors ◽

10.3390/s21072532 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2532

Author(s):

Encarna Quesada ◽

Juan J. Cuadrado-Gallego ◽

Miguel Ángel Patricio ◽

Luis Usero

Keyword(s):

Anomaly Detection ◽

Outlier Detection ◽

Research Work ◽

Industrial Sector ◽

Sensor Data ◽

Correct Selection ◽

Wind Tunnels ◽

Data Set ◽

First Case ◽

Anomaly Analysis

Anomaly Detection research is focused on the development and application of methods that allow for the identification of data that are different enough—compared with the rest of the data set that is being analyzed—and considered anomalies (or, as they are more commonly called, outliers). These values mainly originate from two sources: they may be errors introduced during the collection or handling of the data, or they can be correct, but very different from the rest of the values. It is essential to correctly identify each type as, in the first case, they must be removed from the data set but, in the second case, they must be carefully analyzed and taken into account. The correct selection and use of the model to be applied to a specific problem is fundamental for the success of the anomaly detection study and, in many cases, the use of only one model cannot provide sufficient results, which can be only reached by using a mixture model resulting from the integration of existing and/or ad hoc-developed models. This is the kind of model that is developed and applied to solve the problem presented in this paper. This study deals with the definition and application of an anomaly detection model that combines statistical models and a new method defined by the authors, the Local Transilience Outlier Identification Method, in order to improve the identification of outliers in the sensor-obtained values of variables that affect the operations of wind tunnels. The correct detection of outliers for the variables involved in wind tunnel operations is very important for the industrial ventilation systems industry, especially for vertical wind tunnels, which are used as training facilities for indoor skydiving, as the incorrect performance of such devices may put human lives at risk. In consequence, the use of the presented model for outlier detection may have a high impact in this industrial sector. In this research work, a proof-of-concept is carried out using data from a real installation, in order to test the proposed anomaly analysis method and its application to control the correct performance of wind tunnels.

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Population Subdivision and Molecular Sequence Variation: Theory and Analysis of Drosophila ananassae Data

Genetics ◽

10.1093/genetics/165.3.1385 ◽

2003 ◽

Vol 165 (3) ◽

pp. 1385-1395

Author(s):

Claus Vogl ◽

Aparup Das ◽

Mark Beaumont ◽

Sujata Mohanty ◽

Wolfgang Stephan

Keyword(s):

Sequence Data ◽

Isolation By Distance ◽

Allele Frequencies ◽

Drosophila Ananassae ◽

Population Subdivision ◽

Variation Theory ◽

Peripheral Populations ◽

Molecular Variation ◽

Data Set ◽

Evolutionary Forces

Abstract Population subdivision complicates analysis of molecular variation. Even if neutrality is assumed, three evolutionary forces need to be considered: migration, mutation, and drift. Simplification can be achieved by assuming that the process of migration among and drift within subpopulations is occurring fast compared to mutation and drift in the entire population. This allows a two-step approach in the analysis: (i) analysis of population subdivision and (ii) analysis of molecular variation in the migrant pool. We model population subdivision using an infinite island model, where we allow the migration/drift parameter 0398; to vary among populations. Thus, central and peripheral populations can be differentiated. For inference of 0398;, we use a coalescence approach, implemented via a Markov chain Monte Carlo (MCMC) integration method that allows estimation of allele frequencies in the migrant pool. The second step of this approach (analysis of molecular variation in the migrant pool) uses the estimated allele frequencies in the migrant pool for the study of molecular variation. We apply this method to a Drosophila ananassae sequence data set. We find little indication of isolation by distance, but large differences in the migration parameter among populations. The population as a whole seems to be expanding. A population from Bogor (Java, Indonesia) shows the highest variation and seems closest to the species center.

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