ArticleSensors (Basel, Switzerland)2022
Spiking Neural Networks for Structural Health Monitoring.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed, 13 citations in OpenAlex.
- Sensor-Based Tracking and Localization of In-Line Inspection Tools in Oil and Gas Pipelines: A Review.Sensors (Basel, Switzerland) · 2026Review
- Diagnostics of unmanned aerial vehicle with recurrence based approach of piezo-element voltage signals.Scientific reports · 2024Article
- A Novel Robotic Controller Using Neural Engineering Framework-Based Spiking Neural Networks.Sensors (Basel, Switzerland) · 2024Article
- Seismic assessment of bridges through structural health monitoring: a state-of-the-art review.Bulletin of earthquake engineering · 2024Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
This paper presents the first implementation of a spiking neural network (SNN) for the extraction of cepstral coefficients in structural health monitoring (SHM) applications and demonstrates the possibilities of neuromorphic computing in this field. In this regard, we show that spiking neural networks can be effectively used to extract cepstral coefficients as features of vibration signals of structures in their operational conditions. We demonstrate that the neural cepstral coefficients extracted by the network can be successfully used for anomaly detection. To address the power efficiency of sensor nodes, related to both processing and transmission, affecting the applicability of the proposed approach, we implement the algorithm on specialised neuromorphic hardware (Intel ® Loihi architecture) and benchmark the results using numerical and experimental data of degradation in the form of stiffness change of a single degree of freedom system excited by Gaussian white noise. The work is expected to open a new direction of SHM applications towards non-Von Neumann computing through a neuromorphic approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.