Fast, Robust, and Versatile Event Detection through HMM Belief State Gradient Measures

Event detection is a critical feature in data-driven systems as it assists with the identification of nominal and anomalous behavior. Event detection is increasingly relevant in robotics as robots operate with greater autonomy in increasingly unstructured environments. In this work, we present an accurate, robust, fast, and versatile measure for skill and anomaly identification. A theoretical proof establishes the link between the derivative of the log-likelihood of the HMM filtered belief state and the latest emission probabilities. The key insight is the inverse relationship in which gradient analysis is used for skill and anomaly identification. Our measure showed better performance across all metrics than related state-of-the art works. The result is broadly applicable to domains that use HMMs for event detection.Comment: 8 pages, 7 figures, double col, ieee conference forma

CNN YOLO를 이용한 열화상기반 영상과 점구름 기반 엔드밀 감시 시스템

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2022. 8. 안성훈.As adoption of smart-factory system in manufacturing becoming inevitable, autonomous monitoring system in the field of machining has become viral nowadays. Among various methods in autonomous monitoring, vision-based monitoring is the most sought-after. This system uses vision sensors integrated with detection models developed through deep learning. However, the disadvantage of being greatly affected by optical conditions, such as ambient lighting or reflective materials, critically affects the performance in terms of monitoring. Instead of vision sensors, LiDAR, which provides depth map by measuring light returning time using infrared radiation (IR) directly to the object, can be complementary method. The study presents a LiDAR ((Light Detection and Ranging)-based end mill state monitoring system, which renders strengths of both vision and LiDAR detecting. This system uses point cloud and IR intensity data acquired by the LiDAR while object detection algorithm developed based on deep learning is engaged during the detection stage. The point cloud data is used to detect and determine the length of the endmill while the IR intensity is used to detect the wear present on the endmill. Convolutional neural network based You Only Look Once (YOLO) algorithm is selected as an object detection algorithm for real-time monitoring. Also, the quality of point cloud has been improved using data prep-processing method. Finally, it is verified that end mill state has been monitored with high accuracy at the actual machining environment.제조 분야에서 스마트 팩토리 시스템의 도입으로 인해 가공 과정의 무인 모니터링 시스템이 필연적으로 도입되고 있다. 무인 모니터링의 다양한 방법 중 비전 기반 모니터링이 가장 많이 쓰이고 있다. 해당 비전 기반 시스템의 경우 딥 러닝을 통해 개발된 감지 모델과 통합된 비전 센서를 사용한다. 하지만 주변 조명이나 반사 물질과 같은 광학적 조건에 크게 영향을 받는 단점은 모니터링 측면에서 성능에 치명적인 영향을 미치기에 이를 보완하는 대안이 필요하다. 이 연구에서는 비전 센서 대신 적외선(IR)을 물체에 직접 조사하여 빛의 왕복 시간을 측정하여 깊이 정보를 측정하는 LiDAR를 이용하여 비전 센서의 한계를 보완하는 시스템을 소개한다. 또한 비전과 LiDAR 감지의 장점을 모두 제공하는 LiDAR 기반 엔드밀 상태 모니터링 시스템을 제시한다. 이 시스템은 LiDAR에서 획득한 점 구름 정보 및 IR 강도 데이터를 사용하며, 딥 러닝을 기반으로 개발된 객체 감지 알고리즘은 감지 단계와 엔드밀의 길이를 감지하고 측정하는 데 사용되며 IR 강도는 엔드밀에 존재하는 마모 혹은 파손 정보를 감지하는 데 사용된다. 실시간 모니터링을 위한 객체 감지 알고리즘으로 YOLO(You Only Look Once) 알고리즘을 기반으로 하는 컨볼루션 신경망이 선택되었으며 데이터 전처리를 통해 포인트 클라우드의 품질을 향상시켰다. 마지막으로 실제 가공 환경에서 엔드밀 상태를 높은 정확도로 모니터링하는 과정을 진행하였다.1. Introduction . 1 1.1 Tool monitoring in CNC machines 1 1.2 LiDAR and point cloud map. 5 1.3 IR intensity application 7 2. System modelling 9 2.1 End mill monitoring system overview 9 2.2 Hardware setup . 11 2.3 End mill failure monitoring 15 2.4 YOLO setup 18 3. Data processing . 19 3.1 Confidence score. 19 3.2 Noise removal 20 3.3 Point cloud accumulation. 22 3.4 IR intensity monitoring 26 4. Experiments and results . 28 4.1 Data gathering 28 4.2 Training 30 4.3 Results . 32 5. Conclusion . 39 Reference 41 Abstract (In Korean) 43석

Convolutional-Based Encoder–Decoder Network for Time Series Anomaly Detection during the Milling of 16MnCr5

Machine learning methods have widely been applied to detect anomalies in machine and cutting tool behavior during lathe or milling. However, detecting anomalies in the workpiece itself have not received the same attention by researchers. In this article, the authors present a publicly available multivariate time series dataset which was recorded during the milling of 16MnCr5. Due to artificially introduced, realistic anomalies in the workpiece, the dataset can be applied for anomaly detection. By using a convolutional autoencoder as a first model, good results in detecting the location of the anomalies in the workpiece were achieved. Furthermore, milling tools with two different diameters where used which led to a dataset eligible for transfer learning. The objective of this article is to provide researchers with a real-world time series dataset of the milling process which is suitable for modern machine learning research topics such as anomaly detection and transfer learning

Prestressing wire breakage monitoring using sound event detection

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real-world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over-fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real-time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real-world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real-time monitoring of bridges, ultimately leading to safer and longer-lasting infrastructure

Burr detection and classification using RUSTICO and image processing

.Machined workpieces must satisfy quality standards such as avoid the presence of burrs in edge finishing to reduce production costs and time. In this work we consider three types of burr that are determined by the distribution of the edge shape on a microscopic scale: knife-type (without imperfections), saw-type (presence of small splinters that could be accepted) and burr-breakage (substantial deformation that produces unusable workpieces). The proposed method includes RUSTICO to classify automatically the edge of each piece according to its burr type. Experimental results validate its effectiveness, yielding a 91.2% F1-Score and identifying completely the burr-breakage type.S