scholarly journals A Combined Anomaly and Trend Detection System for Industrial Robot Gear Condition Monitoring

2021 ◽  
Vol 11 (21) ◽  
pp. 10403
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Anomaly Detection ◽  
Condition Monitoring ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Health Indicators ◽  
Automated System ◽  
Trend Detection ◽  
Detection Methods ◽  
Application Data

Conditions monitoring of industrial robot gears has the potential to increase the productivity of highly automated production systems. The huge amount of health indicators needed to monitor multiple gears of multiple robots requires an automated system for anomaly and trend detection. In this publication, such a system is presented and suitable anomaly detection and trend detection methods for the system are selected based on synthetic and real world industrial application data. A statistical test, namely the Cox-Stuart test, appears to be the most suitable approach for trend detection and the local outlier factor algorithm or the long short-term neural network performs best for anomaly detection in the application of industrial robot gear condition monitoring in the presented experiments.

scholarly journals A Method for Health Indicator Evaluation for Condition Monitoring of Industrial Robot Gears

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 80
Author(s):  
Corbinian Nentwich ◽  
Gunther Reinhart
Keyword(s):  
Condition Monitoring ◽  
Production Systems ◽  
Industrial Robot ◽  
Health Indicators ◽  
Industrial Robots ◽  
Health Indicator ◽  
Vibration Data ◽  
Z Scores ◽  
Lower Temperature ◽  
Indicator Evaluation

Condition monitoring of industrial robots has the potential to decrease downtimes in highly automated production systems. In this context, we propose a new method to evaluate health indicators for this application and suggest a new health indicator (HI) based on vibration data measurements, Short-time Fourier transform and Z-scores. By executing the method, we find that the proposed health indicator can detect varying faults better, has lower temperature sensitivity and works better in instationary velocity regimes compared to several state-of-the-art HIs. A discussion of the validity of the results concludes our contribution.

scholarly journals Semi-Supervised Machine Condition Monitoring by Learning Deep Discriminative Audio Features

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2471
Author(s):  
Iordanis Thoidis ◽  
Marios Giouvanakis ◽  
George Papanikolaou
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Convolutional Neural Network ◽  
Condition Monitoring ◽  
Feature Learning ◽  
Detection Methods ◽  
Mechanical Equipment ◽  
Learning Approach ◽  
Audio Features ◽  
Front End

In this study, we aim to learn highly descriptive representations for a wide set of machinery sounds and exploit this knowledge to perform condition monitoring of mechanical equipment. We propose a comprehensive feature learning approach that operates on raw audio, by supervising the formation of salient audio embeddings in latent states of a deep temporal convolutional neural network. By fusing the supervised feature learning approach with an unsupervised deep one-class neural network, we are able to model the characteristics of each source and implicitly detect anomalies in different operational states of industrial machines. Moreover, we enable the exploitation of spatial audio information in the learning process, by formulating a novel front-end processing strategy for circular microphone arrays. Experimental results on the MIMII dataset demonstrate the effectiveness of the proposed method, reaching a state-of-the-art mean AUC score of 91.0%. Anomaly detection performance is significantly improved by incorporating multi-channel audio data in the feature extraction process, as well as training the convolutional neural network on the spatially invariant front-end. Finally, the proposed semi-supervised approach allows the concise modeling of normal machine conditions and accurately detects system anomalies, compared to existing anomaly detection methods.

scholarly journals Towards Bio-Inspired Anomaly Detection Using the Cursory Dendritic Cell Algorithm

Algorithms ◽  
2021 ◽  
Vol 15 (1) ◽  
pp. 1
Author(s):  
Carlos Pinto ◽  
Rui Pinto ◽  
Gil Gonçalves
Keyword(s):  
Dendritic Cell ◽  
Anomaly Detection ◽  
Production Systems ◽  
Detection System ◽  
Artificial Immune Systems ◽  
Object Oriented ◽  
Research Area ◽  
Detection Problem ◽  
Network Intrusion ◽  
Dendritic Cell Algorithm

The autonomous and adaptable identification of anomalies in industrial contexts, particularly in the physical processes of Cyber-Physical Production Systems (CPPS), requires using critical technologies to identify failures correctly. Most of the existing solutions in the anomaly detection research area do not consider such systems’ dynamics. Due to the complexity and multidimensionality of CPPS, a scalable, adaptable, and rapid anomaly detection system is needed, considering the new design specifications of Industry 4.0 solutions. Immune-based models, such as the Dendritic Cell Algorithm (DCA), may provide a rich source of inspiration for detecting anomalies, since the anomaly detection problem in CPPS greatly resembles the functionality of the biological dendritic cells in defending the human body from hazardous pathogens. This paper tackles DCA limitations that may compromise its usage in anomaly detection applications, such as the manual characterization of safe and danger signals, data analysis not suitable for online classification, and the lack of an object-oriented implementation of the algorithm. The proposed approach, the Cursory Dendritic Cell Algorithm (CDCA), is a novel variation of the DCA, developed to be flexible and monitor physical industrial processes continually while detecting anomalies in an online fashion. This work’s contribution is threefold. First, it provides a comprehensive review of Artificial Immune Systems (AIS), focusing on AIS applied to the anomaly detection problem. Then, a new object-oriented architecture for the DCA implementation is described, enabling the modularity and abstraction of the algorithm stages into different classes (modules). Finally, the CDCA for the anomaly detection problem is proposed. The CDCA was successfully validated in two industrial-oriented dataset benchmarks for physical anomaly and network intrusion detection, the Skoltech Anomaly Benchmark (SKAB) and M2M using OPC UA. When compared to other algorithms, the proposed approach exhibits promising classification results. It was placed fourth on the SKAB scoreboard and presented a competitive performance with the incremental Dendritic Cell Algorithm (iDCA).

scholarly journals Wireless Fault Detection System for an Industrial Robot Based on Statistical Control Chart

2017 ◽  
Vol 7 (6) ◽  
pp. 3421
Author(s):  
Alaa Abdulhady Jaber ◽  
Robert Bicker
Keyword(s):  
Fault Detection ◽  
Control Chart ◽  
Production Systems ◽  
Detection System ◽  
Industrial Robot ◽  
Vibration Signal ◽  
Cost Effective ◽  
Base Station ◽  
Industrial Robots ◽  
Statistical Control

Industrial robots are now commonly used in production systems to improve productivity, quality and safety in manufacturing processes. Recent developments involve using robots cooperatively with production line operatives. Regardless of application, there are significant implications for operator safety in the event of a robot malfunction or failure, and the consequent downtime has a significant impact on productivity in manufacturing. Machine healthy monitoring is a type of maintenance inspection technique by which an operational asset is monitored and the data obtained is analysed to detect signs of degradation and thus reducing the maintenance costs. Developments in electronics and computing have opened new horizons in the area of condition monitoring. The aim of using wireless electronic systems is to allow data analysis to be carried out locally at field level and transmitting the results wirelessly to the base station, which as a result will help to overcome the need for wiring and provides an easy and cost-effective sensing technique to detect faults in machines. So, the main focuses of this research is to develop an online and wireless fault detection system for an industrial robot based on statistical control chart approach. An experimental investigation was accomplished using the PUMA 560 robot and vibration signal capturing was adopted, as it responds immediately to manifest itself if any change is appeared in the monitored machine, to extract features related to the robot health conditions. The results indicate the successful detection of faults at the early stages using the key extracted parameters.

Threat Hunting in Windows Using Big Security Log Data

2020 ◽  
pp. 168-188 ◽  
Author(s):  
Mohammad Rasool Fatemi ◽  
Ali A. Ghorbani
Keyword(s):  
Intrusion Detection ◽  
Anomaly Detection ◽  
Detection System ◽  
Intrusion Detection Systems ◽  
Detection Methods ◽  
Sources Of Information ◽  
Detection Systems ◽  
System Logs ◽  
Analysis System ◽  
Anomaly Detection System

System logs are one of the most important sources of information for anomaly and intrusion detection systems. In a general log-based anomaly detection system, network, devices, and host logs are all collected and used together for analysis and the detection of anomalies. However, the ever-increasing volume of logs remains as one of the main challenges that anomaly detection tools face. Based on Sysmon, this chapter proposes a host-based log analysis system that detects anomalies without using network logs to reduce the volume and to show the importance of host-based logs. The authors implement a Sysmon parser to parse and extract features from the logs and use them to perform detection methods on the data. The valuable information is successfully retained after two extensive volume reduction steps. An anomaly detection system is proposed and performed on five different datasets with up to 55,000 events which detects the attacks using the preserved logs. The analysis results demonstrate the significance of host-based logs in auditing, security monitoring, and intrusion detection systems.

scholarly journals Environment Monitoring for Anomaly Detection System Using Smartphones

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3834 ◽  
Author(s):  
Van Khang Nguyen ◽  
Éric Renault ◽  
Ruben Milocco
Keyword(s):  
Anomaly Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Low Cost ◽  
Mean Shift ◽  
Detection Methods ◽  
The Road ◽  
Current Threshold ◽  
Grubbs Test ◽  
Outlier Tests

Currently, the popularity of smartphones with networking capabilities equipped with various sensors and the low cost of the Internet have opened up great opportunities for the use of smartphones for sensing systems. One of the most popular applications is the monitoring and the detection of anomalies in the environment. In this article, we propose to enhance classic road anomaly detection methods using the Grubbs test on a sliding window to make it adaptive to the local characteristics of the road. This allows more precision in the detection of potholes and also building algorithms that consume less resources on smartphones and adapt better to real conditions by applying statistical outlier tests on current threshold-based anomaly detection methods. We also include a clustering algorithm and a mean shift-based algorithm to aggregate reported anomalies on data to the server. Experiments and simulations allow us to confirm the effectiveness of the proposed methods.

scholarly journals Enabling real-time road anomaly detection via mobile edge computing

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771989131 ◽  
Author(s):  
Zengwei Zheng ◽  
Mingxuan Zhou ◽  
Yuanyi Chen ◽  
Meimei Huo ◽  
Dan Chen
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Detection System ◽  
Edge Computing ◽  
Detection Methods ◽  
Data Sets ◽  
Mobile Edge Computing ◽  
Real World Data ◽  
Subsequent Work ◽  
The Road

To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.

scholarly journals Design and Implementation of Automated Steganography Image-Detection System for the KakaoTalk Instant Messenger

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 103
Author(s):  
Jun Park ◽  
Youngho Cho
Keyword(s):  
Detection System ◽  
Automated Detection ◽  
Automated System ◽  
Detection Methods ◽  
Image Detection ◽  
Social Network Service ◽  
Detection Algorithms ◽  
Instant Messenger ◽  
And Control ◽  
Better Than

As the popularity of social network service (SNS) messengers (such as Telegram, WeChat or KakaoTalk) grows rapidly, cyberattackers and cybercriminals start targeting them, and from various media, we can see numerous cyber incidents that have occurred in the SNS messenger platforms. Especially, according to existing studies, a novel type of botnet, which is the so-called steganography-based botnet (stego-botnet), can be constructed and implemented in SNS chat messengers. In the stego-botnet, by using various steganography techniques, every botnet communication and control (C&C) messages are secretly embedded into multimedia files (such as image or video files) frequently shared in the SNS messenger. As a result, the stego-botnet can hide its malicious messages between a bot master and bots much better than existing botnets by avoiding traditional botnet-detection methods without steganography-detection functions. Meanwhile, existing studies have focused on devising and improving steganography-detection algorithms but no studies conducted automated steganography image-detection system although there are a large amount of SNS chatrooms on the Internet and thus may exist many potential steganography images on those chatrooms which need to be inspected for security. Consequently, in this paper, we propose an automated system that detects steganography image files by collecting and inspecting all image files shared in an SNS chatroom based on open image steganography tools. In addition, we implement our proposed system based on two open steganography tools (Stegano and Cryptosteganography) in the KakaoTalk SNS messenger and show our experimental results that validate our proposed automated detection system work successfully according to our design purposes.

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