Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference

• URL: http://arxiv.org/abs/2109.05870v1
• Date: Mon, 13 Sep 2021 11:14:19 GMT
• Title: Towards Stochastic Fault-tolerant Control using Precision Learning and Active Inference
• Authors: Mohamed Baioumy, Corrado Pezzato, Carlos Hernandez Corbato, Nick Hawes, Riccardo Ferrari
• Abstract summary: This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes, a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. We propose a fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery.
• Score: 3.6536977425574664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a fault-tolerant control scheme for sensory faults in robotic manipulators based on active inference. In the majority of existing schemes, a binary decision of whether a sensor is healthy (functional) or faulty is made based on measured data. The decision boundary is called a threshold and it is usually deterministic. Following a faulty decision, fault recovery is obtained by excluding the malfunctioning sensor. We propose a stochastic fault-tolerant scheme based on active inference and precision learning which does not require a priori threshold definitions to trigger fault recovery. Instead, the sensor precision, which represents its health status, is learned online in a model-free way allowing the system to gradually, and not abruptly exclude a failing unit. Experiments on a robotic manipulator show promising results and directions for future work are discussed.

Related papers

• Learning a Factorized Orthogonal Latent Space using Encoder-only Architecture for Fault Detection; An Alarm management perspective [0.2455468619225742]
  This paper introduces a novel encoder-based residual design that effectively decouples erroneously identified and deterministic components of process variables. The proposed model employs two distinct encoders to factorize the latent space into two spaces: one for the deterministic part and the other for the part. The proposed model significantly enhances prediction quality while achieving nearly zero false alarms and missed detections.
  arXiv  Detail & Related papers  (2024-08-24T09:00:45Z)
• PREGO: online mistake detection in PRocedural EGOcentric videos [49.72812518471056]
  We propose PREGO, the first online one-class classification model for mistake detection in egocentric videos. PREGO is based on an online action recognition component to model the current action, and a symbolic reasoning module to predict the next actions. We evaluate PREGO on two procedural egocentric video datasets, Assembly101 and Epic-tent, which we adapt for online benchmarking of procedural mistake detection.
  arXiv  Detail & Related papers  (2024-04-02T13:27:28Z)
• Active Foundational Models for Fault Diagnosis of Electrical Motors [0.5999777817331317]
  Fault detection and diagnosis of electrical motors is of utmost importance in ensuring the safe and reliable operation of industrial systems. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples. We propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples.
  arXiv  Detail & Related papers  (2023-11-27T03:25:12Z)
• A Comparison of Residual-based Methods on Fault Detection [6.675805308519987]
  In this study, we compare two residual-based approaches to detect faults in industrial systems. The performance evaluation focuses on three tasks: health indicator construction, fault detection, and health indicator interpretation. The detection results reveal that both models are capable of detecting faults with an average delay of around 20 cycles and maintain a low false positive rate.
  arXiv  Detail & Related papers  (2023-09-05T14:39:27Z)
• Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
  We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism. Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors. To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
  arXiv  Detail & Related papers  (2023-08-06T05:58:45Z)
• Unsupervised Anomaly Detection with Rejection [19.136286864839846]
  Anomaly detectors learn a decision boundary by employing intuitions, which are hard to verify in practice. A way to combat this is by allowing the detector to reject examples with high uncertainty. This requires employing a confidence metric that captures the distance to the decision boundary and setting a rejection threshold to reject low-confidence predictions.
  arXiv  Detail & Related papers  (2023-05-22T16:22:32Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• Sensor Fault Detection and Isolation in Autonomous Nonlinear Systems Using Neural Network-Based Observers [6.432798111887824]
  Sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer.
  arXiv  Detail & Related papers  (2023-04-18T09:05:07Z)
• Uncertainty-Aware AB3DMOT by Variational 3D Object Detection [74.8441634948334]
  Uncertainty estimation is an effective tool to provide statistically accurate predictions. In this paper, we propose a Variational Neural Network-based TANet 3D object detector to generate 3D object detections with uncertainty.
  arXiv  Detail & Related papers  (2023-02-12T14:30:03Z)
• Trajectory Forecasting from Detection with Uncertainty-Aware Motion Encoding [121.66374635092097]
  Trajectories obtained from object detection and tracking are inevitably noisy. We propose a trajectory predictor directly based on detection results without relying on explicitly formed trajectories.
  arXiv  Detail & Related papers  (2022-02-03T09:09:56Z)
• CertainNet: Sampling-free Uncertainty Estimation for Object Detection [65.28989536741658]
  Estimating the uncertainty of a neural network plays a fundamental role in safety-critical settings. In this work, we propose a novel sampling-free uncertainty estimation method for object detection. We call it CertainNet, and it is the first to provide separate uncertainties for each output signal: objectness, class, location and size.
  arXiv  Detail & Related papers  (2021-10-04T17:59:31Z)

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.