Deep Learning Fault Diagnosis for Hoisting Steel Wire Ropes
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Solution Overview
Problem
Current fault diagnosis technologies for critical components in mine hoisting systems, such as steel wire ropes and bearings, have limited capabilities in modeling and representing fault features, relying heavily on manual extraction and are inadequate for real-time monitoring, which can lead to safety and operational issues.
Innovation Solution
A multiple-state health monitoring apparatus and method utilizing deep learning techniques, including Convolutional Neural Networks (CNN) for image data and Recurrent Neural Networks (RNN) for noise data, to simulate fault conditions and perform intelligent fault diagnosis of steel wire ropes and bearings, reducing dependence on manual signals and experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional fault diagnosis technologies (FFT, wavelet transform, EMD) are used, then manual extraction of fault features is required, but the fault feature modeling and representation capabilities are poor and the system greatly depends on manual extraction and diagnostic experience
Solution Approach 1:
The patent replaces traditional signal processing methods (FFT, wavelet transform, EMD) with a deep learning-based neural network system. The neural network automatically extracts fault features from vibration signals, eliminating the need for manual feature extraction and diagnostic expertise. This substitution transforms the mechanical/manual process into an automated intelligent system that learns optimal feature representations directly from data.
Solution Approach 2:
The neural network system performs self-learning and automatic feature extraction without requiring manual intervention or expert diagnostic knowledge. The system trains on historical data and autonomously identifies fault patterns, making the diagnostic process self-sufficient and reducing dependence on human operators with specialized knowledge.
2Reliability
If steel wire rope is frequently replaced to prevent faults, then safety is maintained, but service life is reduced and operational efficiency decreases
Solution Approach 1:
The system performs preliminary fault detection and early warning by continuously monitoring vibration signals and analyzing them through the trained neural network. By identifying incipient faults before they lead to catastrophic failure, the system enables planned maintenance interventions rather than premature replacement, extending the actual service life while maintaining safety.
Solution Approach 2:
The system implements continuous feedback monitoring of the steel wire rope's health status through vibration analysis. The neural network processes real-time signals and provides feedback on the rope's condition, enabling dynamic adjustment of maintenance timing based on actual wear and fault development rather than fixed replacement schedules.
3Measurement precision
If bearing is monitored using traditional methods, then fault detection is possible, but the fault feature representation is inadequate and diagnostic accuracy is limited
Solution Approach 1:
The patent replaces traditional signal processing techniques (FFT, wavelet transform, EMD) with a deep learning-based neural network system. The neural network automatically extracts fault features from vibration signals, eliminating the need for manual feature extraction and diagnostic expertise. This substitution transforms the mechanical/manual process into an automated intelligent system that learns optimal feature representations directly from data.
4Reliability
If real-time monitoring is implemented without on-site data collection, then safety and reliability are enhanced, but the monitoring process complexity increases
Solution Approach 1:
The system uses vibration sensors to capture copies of the mechanical signals generated by the steel wire rope and bearing during operation. These signal copies are then processed by the neural network system, which creates a virtual model of the component's health state without requiring physical inspection or on-site data collection personnel, thereby reducing operational complexity while maintaining high reliability.
Data Source
AI summary
A multiple-state health monitoring apparatus for critical components in a hoisting system includes a frame. The frame is a square structure formed by welding a plurality of rectangular steels. A steel wire rope is arranged around a periphery of the square structure. A power system, a friction-and-wear apparatus, a brake-and-wear apparatus, and a tensioning apparatus are sequentially mounted from left to right on a bottom layer of the square structure. A bearing signal collection system, a tension sensor, an excitation apparatus, and a steel-wire-rope image collection system are sequentially mounted from left to right on a top layer of the square structure. The steel wire rope sequentially passes through all the apparatuses or systems and is driven by the power system to perform circling. All the apparatuses or systems are used to monitor an operation status of the steel wire rope.