SmartFault-M Hybrid CNN–LSTM Attention Multi-Domain is a M.Tech project topic for Mechanical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
SmartFault-M Hybrid CNN–LSTM Attention Multi-Domain Project Details
| Abstract |
This project framework provides research direction and implementation support for developing an advanced fault diagnostic model tailored for complex industrial mechatronic systems. Industrial mechatronics involve intricate interactions among mechanical, electrical, and control sub-systems, yielding multidimensional, non-stationary sensor data. Traditional single-domain diagnostic approaches often fail to capture the multi-scale and temporal characteristics of these faults. To address this limitation, this study guides the structuring of a hybrid dual-stream deep learning framework, designated as SmartFault-M. The architecture utilizes parallel streams to process time-domain and frequency-domain feature maps simultaneously. Within this framework, Convolutional Neural Networks (CNN) extract spatial features, while Long Short-Term Memory (LSTM) networks capture temporal dependencies. An integrated attention mechanism is
employed to emphasize highly informative features and suppress noise. The methodology is validated using the Case Western Reserve University (CWRU) bearing dataset under diverse fault states. This project development support focuses on evaluating the model's performance, which achieves a high diagnostic accuracy of 98.25%, significantly outperforming conventional CNN, LSTM, and standard hybrid CNN-LSTM baselines. The framework serves as an academic guide for implementing robust, multi-domain structural health monitoring systems.
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| Reference Paper |
SmartFault-M: A Hybrid CNN–LSTM with Attention for Multi-Domain Fault Diagnosis in Industrial Mechatronic Systems |
| Domain |
Mechanical Engineering |
| Sub-Domain |
Mechatronics & Robotics / Vibration & Noise / Structural Health Monitoring |
| PDF Download |
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