ArticleSensors (Basel, Switzerland)2023
Machine Fault Detection Using a Hybrid CNN-LSTM Attention-Based Model.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
22 citing papers in PubMed.
- Cross-Condition Fault Diagnosis of Rolling Bearings Based on Time-Frequency Ridge Extraction and Uncertainty-Guided Distribution-Regularised Convolutional Wasserstein Autoencoder.Sensors (Basel, Switzerland) · 2026Article
- Lower Limb Motion Classification of Actions in Confined Environments Based on Multi-Source Signal Fusion and Muscle Symmetry Features.Sensors (Basel, Switzerland) · 2026Article
- Hyperbolic Hypergraph Neural Networks for Hierarchical Fault Diagnosis in Rotating Machinery.Sensors (Basel, Switzerland) · 2026Article
- Entropy-Driven Intelligent Diagnosis for SMR Loss of Coolant Accidents: A CNN-LSTM-Attention Hybrid Model for Break Size Assessment.Entropy (Basel, Switzerland) · 2026Article
- Information-Theoretic Channel Selection and Spatiotemporal Deep Learning for Early Fault Detection in Microsatellite Thermal Control Systems.Entropy (Basel, Switzerland) · 2026Article
- Building-level energy prediction and control based on BIM and IIoT technologies.Scientific reports · 2026Article
- Attention-enhanced spatiotemporal deep learning for predictive maintenance in oil and gas assets: towards Maintenance 5.0.Scientific reports · 2026Article
- A dual-stream deep learning architecture for business impact scoring and alert escalation.PloS one · 2026Article
- Fault Diagnosis of Static Eccentricity in Marine Diesel Generators Using 2D Short-Time Fourier Transform of Three-Phase Currents.Sensors (Basel, Switzerland) · 2025Article
- End-to-End Architecture for Real-Time IoT Analytics and Predictive Maintenance Using Stream Processing and ML Pipelines.Sensors (Basel, Switzerland) · 2025Article
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- MYSTETH-home-based heart monitoring.Frontiers in digital health · 2025Article
- Rotor angle stability of a microgrid generator through polynomial approximation based on RFID data collection and deep learning.Scientific reports · 2024Article
- Audio-Based Engine Fault Diagnosis with Wavelet, Markov Blanket, ROCKET, and Optimized Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2024Article
- From COVID-19 to monkeypox: a novel predictive model for emerging infectious diseases.BioData mining · 2024Article
- Application of the Nadaraya-Watson estimator based attention mechanism to the field of predictive maintenance.MethodsX · 2024Article
- A deep learning-based approach for axle counter in free-flow tolling systems.Scientific reports · 2024Article
- A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation.Sensors (Basel, Switzerland) · 2024Review
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- Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques.Frontiers in artificial intelligence · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
The predictive maintenance of electrical machines is a critical issue for companies, as it can greatly reduce maintenance costs, increase efficiency, and minimize downtime. In this paper, the issue of predicting electrical machine failures by predicting possible anomalies in the data is addressed through time series analysis. The time series data are from a sensor attached to an electrical machine (motor) measuring vibration variations in three axes: X (axial), Y (radial), and Z (radial X). The dataset is used to train a hybrid convolutional neural network with long short-term memory (CNN-LSTM) architecture. By employing quantile regression at the network output, the proposed approach aims to manage the uncertainties present in the data. The application of the hybrid CNN-LSTM attention-based model, combined with the use of quantile regression to capture uncertainties, yielded superior results compared to traditional reference models. These results can benefit companies by optimizing their maintenance schedules and improving the overall performance of their electric machines.
Indexed as
Identifiers
What OpenQuestion holds
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.