ArticleSensors (Basel, Switzerland)2017
Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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Who cites it
68 citing papers in PubMed.
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- One-dimensional time-frequency dual-channel visual transformer for bearing fault diagnosis under strong noise and limited data conditions.Scientific reports · 2025Article
- Comparison of deep learning models for predictive maintenance in industrial manufacturing systems using sensor data.Scientific reports · 2025Article
- Research on the Timing of Replacing Worn Milling Cutters by Using Wear Transition Percentage Constructed Based on Spindle Current Clutter Signals.Sensors (Basel, Switzerland) · 2025Article
- A hybrid Bi-LSTM model for data-driven maintenance planning.Autonomous intelligent systems · 2025Article
- A multi-modal deep learning solution for precise pneumonia diagnosis: the PneumoFusion-Net model.Frontiers in physiology · 2025Article
- Novel efficient reservoir computing methodologies for regular and irregular time series classification.Nonlinear dynamics · 2025Article
- Advancing epigenetic profiling in cervical cancer: machine learning techniques for classifying DNA methylation patterns.3 Biotech · 2024Article
- Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Remaining Useful Life Prediction Based on Deep Learning: A Survey.Sensors (Basel, Switzerland) · 2024Review
- AI-Driven Sensing Technology: Review.Sensors (Basel, Switzerland) · 2024Review
- A Fault Detection Approach Based on One-Sided Domain Adaptation and Generative Adversarial Networks for Railway Door Systems.Sensors (Basel, Switzerland) · 2023Article
- Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time-Frequency-Based Features and Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression models directly. Therefore, previous work focuses on feature extraction/fusion methods requiring expensive human labor and high quality expert knowledge. With the development of deep learning methods in the last few years, which redefine representation learning from raw data, a deep neural network structure named Convolutional Bi-directional Long Short-Term Memory networks (CBLSTM) has been designed here to address raw sensory data. CBLSTM firstly uses CNN to extract local features that are robust and informative from the sequential input. Then, bi-directional LSTM is introduced to encode temporal information. Long Short-Term Memory networks(LSTMs) are able to capture long-term dependencies and model sequential data, and the bi-directional structure enables the capture of past and future contexts. Stacked, fully-connected layers and the linear regression layer are built on top of bi-directional LSTMs to predict the target value. Here, a real-life tool wear test is introduced, and our proposed CBLSTM is able to predict the actual tool wear based on raw sensory data. The experimental results have shown that our model is able to outperform several state-of-the-art baseline methods.
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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.