ENTERPRISE AI ANALYSIS
Mechanical fault detection method based on GRC-DGWO-BVM algorithm
This article introduces a novel mechanical fault detection algorithm, GrC-DGWO-BVM, designed to enhance detection efficiency and accuracy. It leverages Granular Computing (GrC) for redundant attribute removal, a Ball Vector Machine (BVM) for model construction, and a Modified Grey Wolf Optimization (DGWO) algorithm for optimal parameter selection. Experimental results demonstrate its superior performance compared to existing methods, making it a robust solution for industrial mechanical safety.
Quantifiable Enterprise Impact
Our analysis reveals the significant advancements and tangible benefits this GrC-DGWO-BVM algorithm brings to enterprise operations, especially in critical mechanical fault detection scenarios.
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Detection Accuracy
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Detection Rate
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CPU Time Savings
Deep Analysis & Enterprise Applications
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Granular Computing (GrC)
Ball Vector Machine (BVM)
Modified Grey Wolf Optimization (DGWO)
Granular Computing (GrC)
Granular Computing (GrC) is utilized to process large datasets efficiently by identifying and removing redundant attributes, extracting only useful hidden information. This significantly reduces data complexity and improves model efficiency, especially in big data environments.
Ball Vector Machine (BVM)
The Ball Vector Machine (BVM) forms the core of the fault detection model. It transforms the complex Quadratic Programming (QP) problem of traditional SVMs into a simpler Minimum Enclosing Ball (MEB) problem, drastically reducing training space and time complexity from O(n³) and O(n²) to O(n).
Modified Grey Wolf Optimization (DGWO)
The Modified Grey Wolf Optimization (DGWO) algorithm enhances leader decision-making, preventing premature local optima convergence. It's used to optimize the BVM's penalty factor (Cm) and kernel parameters (σ), ensuring the model achieves peak performance in fault detection accuracy and efficiency.
99.54%
Maximum Detection Accuracy Achieved
GRC-DGWO-BVM Algorithm Workflow
Data Preprocessing & Attribute Reduction (GrC)
→
BVM Model Construction
→
DGWO Parameter Optimization (Cm & σ)
→
Training with Rotating Machinery Dataset
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Fault Detection & Evaluation
| Algorithm | CPU Time (s) | Detection Accuracy (%) | Detection Rate (%) |
|---|---|---|---|
| DT | 0.633 | 86.21 | 97.80 |
| L1-SVM | 21.06 | 92.23 | 99.49 |
| L2-SVM | 19.63 | 91.57 | 99.48 |
| CVM | 1.259 | 85.43 | 97.84 |
| GrC-DGWO-BVM | 1.609 | 93.82 | 99.65 |
Impact on Industrial Safety
The implementation of the GrC-DGWO-BVM algorithm has demonstrated significant advancements in ensuring industrial safety. By providing highly accurate and efficient mechanical fault detection, it minimizes downtime, reduces maintenance costs, and proactively prevents severe safety accidents. Its ability to process large-scale data and adapt to complex systems makes it an ideal solution for modern intelligent manufacturing environments, safeguarding both assets and personnel.
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Strategic Implementation Roadmap
Our phased approach ensures a smooth transition and maximum benefit from the GrC-DGWO-BVM fault detection system.
Phase 1: Data Acquisition & Preprocessing
Establish data pipelines for mechanical sensor data. Apply Granular Computing (GrC) for initial attribute reduction and noise filtering, ensuring a clean and relevant dataset for model training.
Phase 2: Model Training & Optimization
Train the Ball Vector Machine (BVM) with preprocessed data. Utilize the Modified Grey Wolf Optimization (DGWO) algorithm to fine-tune BVM parameters (Cm, σ) for optimal accuracy and efficiency.
Phase 3: System Integration & Testing
Integrate the GrC-DGWO-BVM model into existing monitoring systems. Conduct rigorous testing with real-world rotating machinery datasets to validate performance against baseline methods and ensure robustness.
Phase 4: Deployment & Continuous Monitoring
Deploy the fault detection system across industrial operations. Implement continuous monitoring and feedback loops to adapt the model to new fault patterns and evolving operational conditions.
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