A general anomaly detection framework for fleet-based condition monitoring of machines

• URL: http://arxiv.org/abs/1912.12941v3
• Date: Tue, 7 Jan 2020 11:06:06 GMT
• Title: A general anomaly detection framework for fleet-based condition monitoring of machines
• Authors: Kilian Hendrickx, Wannes Meert, Yves Mollet, Johan Gyselinck, Bram Cornelis, Konstantinos Gryllias, Jesse Davis
• Abstract summary: Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring.
• Score: 16.51849885526826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial intelligence in an effort to avoid time-consuming manual analysis and handcrafted feature extraction. Many of these only analyze a single machine and require a large historical data set. In practice, this can be difficult and expensive to collect. However, some industrial condition monitoring applications involve a fleet of similar operating machines. In most of these applications, it is safe to assume healthy conditions for the majority of machines. Deviating machine behavior is then an indicator for a machine fault. This work proposes an unsupervised, generic, anomaly detection framework for fleet-based condition monitoring. It uses generic building blocks and offers three key advantages. First, a historical data set is not required due to online fleet-based comparisons. Second, it allows incorporating domain expertise by user-defined comparison measures. Finally, contrary to most black-box artificial intelligence techniques, easy interpretability allows a domain expert to validate the predictions made by the framework. Two use-cases on an electrical machine fleet demonstrate the applicability of the framework to detect a voltage unbalance by means of electrical and vibration signatures.

Related papers

• Statistical Batch-Based Bearing Fault Detection [0.0]
  In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep learning methods.
  arXiv  Detail & Related papers  (2024-07-24T12:45:02Z)
• Condition monitoring and anomaly detection in cyber-physical systems [0.483420384410068]
  This paper presents a comparative analysis of recent machine learning approaches for robust, cost-effective anomaly detection in cyber-physical systems. For supervised cases, we achieve near-perfect accuracy of 98 percent. In contrast, the best-case accuracy in the unsupervised cases was 63 percent.
  arXiv  Detail & Related papers  (2023-01-22T00:58:01Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Artificial Intelligence based tool wear and defect prediction for special purpose milling machinery using low-cost acceleration sensor retrofits [0.0]
  This paper demonstrates the application of an acceleration sensor to allow for convenient condition monitoring of such a special purpose machine. We examine a variety of conditions including blade wear and blade breakage as well as improper machine mounting or insufficient transmission belt tension.
  arXiv  Detail & Related papers  (2022-02-07T11:02:48Z)
• LogLAB: Attention-Based Labeling of Log Data Anomalies via Weak Supervision [63.08516384181491]
  We present LogLAB, a novel modeling approach for automated labeling of log messages without requiring manual work by experts. Our method relies on estimated failure time windows provided by monitoring systems to produce precise labeled datasets in retrospect. Our evaluation shows that LogLAB consistently outperforms nine benchmark approaches across three different datasets and maintains an F1-score of more than 0.98 even at large failure time windows.
  arXiv  Detail & Related papers  (2021-11-02T15:16:08Z)
• 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)
• 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)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Predictive maintenance on event logs: Application on an ATM fleet [0.6961253535504979]
  In some applications, outputs from sensors are not available, and event logs generated by the machine are used instead. We first study the approaches used in the literature to solve predictive maintenance problems and present a new public dataset containing the event logs from 156 machines.
  arXiv  Detail & Related papers  (2020-11-22T12:12:14Z)
• Predictive Maintenance for Edge-Based Sensor Networks: A Deep Reinforcement Learning Approach [68.40429597811071]
  The risk of unplanned equipment downtime can be minimized through Predictive Maintenance of revenue generating assets. A model-free Deep Reinforcement Learning algorithm is proposed for predictive equipment maintenance from an equipment-based sensor network context. Unlike traditional black-box regression models, the proposed algorithm self-learns an optimal maintenance policy and provides actionable recommendation for each equipment.
  arXiv  Detail & Related papers  (2020-07-07T10:00:32Z)

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.