Analysing software failure using runtime verification and LTL
- URL: http://arxiv.org/abs/2405.01545v1
- Date: Thu, 15 Feb 2024 13:37:45 GMT
- Title: Analysing software failure using runtime verification and LTL
- Abstract summary: A self-healing software system is designed to detect, diagnose, and automatically recover from faults or errors without human intervention.
This research proposes a method using runtime verification to diagnose four types of errors at the component level.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: A self-healing software system is an advanced computer program or system designed to detect, diagnose, and automatically recover from faults or errors without human intervention. These systems are typically employed in mission-critical applications where downtime can have significant financial or operational consequences. Failure detection is one of the important steps in the self-healing system. In this research, a method using runtime verification is proposed to diagnose four types of errors at the component level. The simulation on mRUBIS shows that the suggested method has the necessary efficiency in detecting the occurrence of failures.
Related papers
- Can Large Language Models Generate Observability-Aware Code? [51.30941152293301]
We present a systematic study of observability in agent-generated systems.<n>Our results reveal a consistent gap between diagnostic semantics at the source level and fault signals at runtime.<n>We introduce an observability-oriented skill, which can serve as a guidance to improve both diagnostic semantics and fault-signal exposure.
arXiv Detail & Related papers (2026-07-07T03:20:52Z) - Honest to a Fault: Root-Causing Fault Attacks with Pre-Silicon RISC Pipeline Characterization [4.83186491286234]
This study aims to characterize and diagnose the impact of faults within the RISC-V instruction set and pipeline stages, while tracing fault propagation from the circuit level to the AI/ML application software.
This analysis resulted in discovering a novel vulnerability through controlled clock glitch parameters, specifically targeting the RISC-V decode stage.
arXiv Detail & Related papers (2025-03-05T20:08:12Z) - 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) - 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) - 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) - PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows.
Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
arXiv Detail & Related papers (2023-01-25T16:34:43Z) - Machine learning-based approach for online fault Diagnosis of Discrete
Event System [0.0]
The problem is the online diagnosis of Automated Production Systems with sensors and actuators delivering discrete binary signals.
We propose a Machine Learning-based approach of a diagnostic system.
arXiv Detail & Related papers (2022-10-24T08:56:13Z) - A Robust and Explainable Data-Driven Anomaly Detection Approach For
Power Electronics [56.86150790999639]
We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer.
The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data.
A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
arXiv Detail & Related papers (2022-09-23T06:09:35Z) - Monitoring of Perception Systems: Deterministic, Probabilistic, and
Learning-based Fault Detection and Identification [21.25149064251918]
We formalize the problem of runtime fault detection and identification in perception systems.
We provide a set of deterministic, probabilistic, and learning-based algorithms that use diagnostic graphs to perform fault detection and identification.
We conclude the paper with an experimental evaluation, which recreates several realistic failure modes in the LGSVL open-source autonomous driving simulator.
arXiv Detail & Related papers (2022-05-22T19:08:45Z) - A platform for causal knowledge representation and inference in
industrial fault diagnosis based on cubic DUCG [0.0]
It is important to control the operation of the system to accurately grasp the operation status of the system and find out the failure in time.
The occurrence of system failure is a gradual process, and the occurrence of the current system failure may depend on the previous state of the system.
The fault diagnosis technology based on time series can monitor the operating status of the system in real-time, detect the abnormal operation of the system within the allowable time interval, diagnose the root cause of the fault and predict the status trend.
arXiv Detail & Related papers (2022-03-24T02:06:22Z) - Detecting Faults during Automatic Screwdriving: A Dataset and Use Case
of Anomaly Detection for Automatic Screwdriving [80.6725125503521]
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.
arXiv Detail & Related papers (2021-07-05T11:46:00Z) - Monitoring and Diagnosability of Perception Systems [21.25149064251918]
We propose a mathematical model for runtime monitoring and fault detection and identification in perception systems.
We demonstrate our monitoring system, dubbed PerSyS, in realistic simulations using the LGSVL self-driving simulator and the Apollo Auto autonomy software stack.
arXiv Detail & Related papers (2020-11-11T23:03:14Z)
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.