Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving

• URL: http://arxiv.org/abs/2107.01955v1
• Date: Mon, 5 Jul 2021 11:46:00 GMT
• Title: Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving
• Authors: B{\l}a\.zej Leporowski, Daniella Tola, Casper Hansen and Alexandros Iosifidis
• Abstract summary: Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest. We present a use case of using ML models for detecting faults during automated screwdriving operations.
• Score: 80.6725125503521
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.

Related papers

• The voraus-AD Dataset for Anomaly Detection in Robot Applications [24.583911041699086]
  anomaly detection (AD) delivers a practical solution, using only normal data to learn to detect unusual events. We introduce a dataset that allows training and benchmarking of anomaly detection methods for robotic applications. We present MVT-Flow as a new baseline method for anomaly detection.
  arXiv  Detail & Related papers  (2023-11-08T15:39:27Z)
• Representing Timed Automata and Timing Anomalies of Cyber-Physical Production Systems in Knowledge Graphs [51.98400002538092]
  This paper aims to improve model-based anomaly detection in CPPS by combining the learned timed automaton with a formal knowledge graph about the system. Both the model and the detected anomalies are described in the knowledge graph in order to allow operators an easier interpretation of the model and the detected anomalies.
  arXiv  Detail & Related papers  (2023-08-25T15:25:57Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection [0.2446672595462589]
  Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. Real-time monitoring offers a solution for detecting faults without the need for manual observation. We propose a Machine Learning (ML) approach to model normal working operation and detect anomalies.
  arXiv  Detail & Related papers  (2021-10-01T12:25:25Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Machine Learning Model Drift Detection Via Weak Data Slices [5.319802998033767]
  We propose a method that utilizes feature space rules, called data slices, for drift detection. We provide experimental indications that our method is likely to identify that the ML model will likely change in performance, based on changes in the underlying data.
  arXiv  Detail & Related papers  (2021-08-11T16:55:34Z)
• AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset [80.6725125503521]
  This report describes a dataset created using a UR3e series robot and OnRobot Screwdriver. The resulting data contains 2042 samples of normal and anomalous robot operation. Brief ML benchmarks using this data are also provided, showcasing the data's suitability and potential for further analysis and experimentation.
  arXiv  Detail & Related papers  (2021-02-02T09:59:23Z)
• A Novel Anomaly Detection Algorithm for Hybrid Production Systems based on Deep Learning and Timed Automata [73.38551379469533]
  DAD:DeepAnomalyDetection is a new approach for automatic model learning and anomaly detection in hybrid production systems. It combines deep learning and timed automata for creating behavioral model from observations. The algorithm has been applied to few data sets including two from real systems and has shown promising results.
  arXiv  Detail & Related papers  (2020-10-29T08:27:43Z)
• Out-Of-Bag Anomaly Detection [0.9449650062296822]
  Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems. We propose a novel model-based anomaly detection method, that we call Out-of-Bag anomaly detection. We show our method can improve the accuracy and reliability of an ML system as data pre-processing step via a case study on home valuation.
  arXiv  Detail & Related papers  (2020-09-20T06:01:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.