The following are the core technical paths and application plans to achieve preventive maintenance of rice mill reducers:
1. Multi-modal data collection and perception
Data is the basis for fault warning. Deploy low-cost IoT sensors at key nodes of the rice mill reducer (such as main shaft, fan, reducer, etc.) to build a multi-dimensional status awareness system:
Vibration monitoring: Deploy IEPE-type acceleration sensors to capture high-frequency vibration waveforms. For example, in the early stages of motor bearing failure, abnormal high-frequency vibrations of 9kHz-12kHz will occur, which require high-sampling rate sensors to accurately capture.
Temperature monitoring: Real-time monitoring of equipment surface and bearing seat temperatures. If the local temperature rises abnormally (such as the temperature difference between each bearing seat > 8°C) or the oil temperature suddenly rises by 10°C, it usually indicates lubrication failure or uneven load.
Current monitoring: The Hall effect current sensor collects current changes to reflect the load fluctuation of the equipment and the insulation status of the motor.
2. Edge computing and AI intelligent analysis
The industrial site environment is complex (such as heavy dust and high noise), and edge computing and artificial intelligence algorithms need to be combined for data processing and fault diagnosis:
Signal processing and feature extraction: Wavelet packet transform (WPT) is used to decompose the original signal and extract characteristic parameters such as energy entropy and kurtosis coefficient. For strong noise backgrounds, techniques such as physically guided variational mode decomposition can be used to suppress modal aliasing and improve the accuracy of weak fault feature extraction.
Deep learning and prediction models: Combining temporal deep learning models such as LSTM (long short-term memory network) and GRU with physical degradation models to construct an equipment health index (EHI). When the health index continuously exceeds the baseline value, the system automatically triggers an early warning and can predict the remaining service life (RUL) of components.
Cloud-edge collaboration architecture: The edge is responsible for real-time feature extraction and second-level anomaly judgment, and the cloud uses machine learning algorithms for health assessment and global fault diagnosis to achieve efficient collaboration of "local diagnosis and cloud analysis".
3. Non-destructive testing and internal defect assessment
For highly integrated reducers with compact internal structures, traditional disassembly inspection can easily destroy assembly accuracy. The introduction of high-resolution industrial CT non-destructive testing solutions can accurately obtain complete geometric information of complex structures such as internal gear meshing and bearing balls without damaging components. Combined with AI deep learning, it automatically identifies micron-level internal defects and provides accurate data support for scientific assessment of remaining life.
4. Business closed loop and operation and maintenance ecological construction
The ultimate purpose of fault warning is to guide maintenance actions. Modern predictive maintenance systems can be deeply integrated with an enterprise"s MES/CMMS (manufacturing execution/computerized maintenance management) system:
Automatic order dispatch and spare parts linkage: When the system generates an early warning, it automatically pushes a work order containing fault details and countermeasures, and associates the spare parts inventory, forming a complete closed loop of "alarm → diagnosis → dispatch → review".
Pay-per-performance model: For small and medium-sized rice enterprises, equipment manufacturers can provide a "Predictive Maintenance as a Service (PMaaS)" model, which charges service fees only when major failures are successfully avoided, greatly reducing users" trial-and-error risks and financial pressure.
