arXiv research

Ranger improves DNNs' fault resilience without re-computation.

Mitigates faults in DNNs by clipping activation values, improving their resilience.

TensorFI injects faults in TensorFlow programs to assess their reliability.

Automated design of resilient, efficient DNNs for hardware.

The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves verification, validation, and testing, end-to-end assessment of AV systems under accidental faults in realistic driving scenarios has been lar…

Machine Learning (ML) is making a strong resurgence in tune with the massive generation of unstructured data which in turn requires massive computational resources. Due to the inherently compute- and power-intensive structure of Neural Networks (NNs), hardware accelerators emerge as a promising solution. However, with …

Predictive Q-learning algorithm for IoT networks with human operators.

Fault-tolerant federated learning for non-uniform data.

Paper proposes FTT-NAS to create fault-tolerant CNNs for edge devices.

Financial markets can be seen as complex systems that are constantly evolving and sensitive to external disturbance, such as systemic risks and economic instabilities. Analysis of resilient market performance, therefore, becomes useful for investors. From a systems perspective, this paper proposes a novel function-base…

Distributed machine learning algorithms enable learning of models from datasets that are distributed over a network without gathering the data at a centralized location. While efficient distributed algorithms have been developed under the assumption of faultless networks, failures that can render these algorithms nonfu…

Paper models entropy-based impact of soft errors on neural network inference.

Study shows BNN inference accelerators are vulnerable to soft errors, causing significant misclassification.

Develops a data-driven fault diagnosis framework for time-series data.

Neural network residuals isolate and locate unknown faults.

Meta-reinforcement learning improves fault-adaptive control efficiency.

Ensemble models struggle with detecting mild faults.

Proposes KIL-AdaVAE for fault detection and segmentation of unknown fault types.

Residual generation helps diagnose engine faults using neural networks.

TS-Fault benchmarks TSF models against structural faults.

We introduce a differential geometric framework for describing families of quantum error-correcting codes and for understanding quantum fault tolerance. This work unifies the notion of topological fault tolerance with fault tolerance in other kinds of quantum error-correcting codes. In particular, we use fibre bundles …

Generative model predicts vehicle faults up to 1000 hours in advance.

Quantum computing improves fault diagnosis in industrial processes.

This paper presents a novel and flexible solution for fault prediction based on data collected from SCADA system. Fault prediction is offered at two different levels based on a data-driven approach: (a) generic fault/status prediction and (b) specific fault class prediction, implemented by means of two different machin…

Study designs neural networks for fault localization, state estimation, and optimal PMU placement in power systems.

Paper robustifies reinforcement learning agents against action space perturbations.

New method detects bearing faults using multivariate statistical process control.

Bayesian Recurrent Neural Networks improve fault detection and identification in manufacturing.

Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning models such as deep neural networks are considered promising due to their ability to directly learn from labeled fault data; however, it is kn…

Few-shot learning improves bearing fault diagnosis with limited data.

This paper proposes a novel Gaussian process approach to fault removal in time-series data. Fault removal does not delete the faulty signal data but, instead, massages the fault from the data. We assume that only one fault occurs at any one time and model the signal by two separate non-parametric Gaussian process model…

New PCA method detects faults using occupation kernels.

The cost of wind energy can be reduced by using SCADA data to detect faults in wind turbine components. Normal behavior models are one of the main fault detection approaches, but there is a lack of consensus in how different input features affect the results. In this work, a new taxonomy based on the causal relations b…

Paper addresses fault-tolerance in distributed machine learning with stochastic gradient descent.

The paper investigates how reducing memory supply voltage improves DNN accuracy under bit-cell faults.

The Monte Carlo dropout method has proved to be a scalable and easy-to-use approach for estimating the uncertainty of deep neural network predictions. This approach was recently applied to Fault Detection and Di-agnosis (FDD) applications to improve the classification performance on incipient faults. In this paper, we …

Noncritical soft-faults and model deviations are a challenge for Fault Detection and Diagnosis (FDD) of resident Autonomous Underwater Vehicles (AUVs). Such systems may have a faster performance degradation due to the permanent exposure to the marine environment, and constant monitoring of component conditions is requi…

Recent trends focusing on Industry 4.0 concept and smart manufacturing arise a data-driven fault diagnosis as key topic in condition-based maintenance. Fault diagnosis is considered as an essential task in rotary machinery since possibility of an early detection and diagnosis of the faulty condition can save both time …

We analyze the adversarial examples problem in terms of a model's fault tolerance with respect to its input. Whereas previous work focuses on arbitrarily strict threat models, i.e., $ε$-perturbations, we consider arbitrary valid inputs and propose an information-based characteristic for evaluating tolerance to diverse …

Diverse fault types, fast re-closures, and complicated transient states after a fault event make real-time fault location in power grids challenging. Existing localization techniques in this area rely on simplistic assumptions, such as static loads, or require much higher sampling rates or total measurement availabilit…

Fault-tolerant neural networks inspired by biological error correction codes.

A method uses ITD and XGBoost for precise power transformer fault diagnosis.

Fault detection problem for closed loop uncertain dynamical systems, is investigated in this paper, using different deep learning based methods. Traditional classifier based method does not perform well, because of the inherent difficulty of detecting system level faults for closed loop dynamical system. Specifically, …

We adopted an approach based on an LSTM neural network to monitor and detect faults in industrial multivariate time series data. To validate the approach we created a Modelica model of part of a real gasoil plant. By introducing hacks into the logic of the Modelica model, we were able to generate both the roots and cau…

Paper tackles fault classification in time series data with deep neural networks.

Despite the great achievements of deep neural networks (DNNs), the vulnerability of state-of-the-art DNNs raises security concerns of DNNs in many application domains requiring high reliability.We propose the fault sneaking attack on DNNs, where the adversary aims to misclassify certain input images into any target lab…

Thanks to digitization of industrial assets in fleets, the ambitious goal of transferring fault diagnosis models fromone machine to the other has raised great interest. Solving these domain adaptive transfer learning tasks has the potential to save large efforts on manually labeling data and modifying models for new ma…

Paper introduces a new index to measure financial and workplace resilience of firms.