ArticleSensors (Basel, Switzerland)2021
Bearing Fault Diagnosis Using Multidomain Fusion-Based Vibration Imaging and Multitask Learning.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Towards a Resilience-Oriented Framework for Fault Diagnosis Under Varying Operating Conditions.Sensors (Basel, Switzerland) · 2026Article
- A Deep Learning Method for Bearing Cross-Domain Fault Diagnostics Based on the Standard Envelope Spectrum.Sensors (Basel, Switzerland) · 2024Article
- WPD-Enhanced Deep Graph Contrastive Learning Data Fusion for Fault Diagnosis of Rolling Bearing.Micromachines · 2023Article
- A Review on Rolling Bearing Fault Signal Detection Methods Based on Different Sensors.Sensors (Basel, Switzerland) · 2022Review
- Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault Diagnosis.Sensors (Basel, Switzerland) · 2022Article
- Bearing Fault Feature Extraction Method Based on Enhanced Differential Product Weighted Morphological Filtering.Sensors (Basel, Switzerland) · 2022Article
- Bearing Fault Diagnosis Using Lightweight and Robust One-Dimensional Convolution Neural Network in the Frequency Domain.Sensors (Basel, Switzerland) · 2022Article
- Deep Unsupervised Domain Adaptation with Time Series Sensor Data: A Survey.Sensors (Basel, Switzerland) · 2022Review
- Multi-Stream Convolutional Neural Networks for Rotating Machinery Fault Diagnosis under Noise and Trend Items.Sensors (Basel, Switzerland) · 2022Article
- A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Statistical features extraction from bearing fault signals requires a substantial level of knowledge and domain expertise. Furthermore, existing feature extraction techniques are mostly confined to selective feature extraction methods namely, time-domain, frequency-domain, or time-frequency domain statistical parameters. Vibration signals of bearing fault are highly non-linear and non-stationary making it cumbersome to extract relevant information for existing methodologies. This process even became more complicated when the bearing operates at variable speeds and load conditions. To address these challenges, this study develops an autonomous diagnostic system that combines signal-to-image transformation techniques for multi-domain information with convolutional neural network (CNN)-aided multitask learning (MTL). To address variable operating conditions, a composite color image is created by fusing information from multi-domains, such as the raw time-domain signal, the spectrum of the time-domain signal, and the envelope spectrum of the time-frequency analysis. This 2-D composite image, named multi-domain fusion-based vibration imaging (
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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.