According to the regional division and boundary value setting of the normative standard system, the health status of the equipment can be sensed and quantitatively evaluated in real time through the change and analysis of the state trend.
Quantitative evaluation of equipment health status
Determine the fault direction qualitatively
According to the different physical properties and characteristic analysis of various monitoring parameters of the machine state, the fault nature and fault direction of the equipment can be quickly analyzed and determined.
Real-time warning of device health status
Through status trend analysis, the system automatically identifies abnormal data information, and sends real-time early warning information to the identified equipment post management personnel through wireless communication technology.
Standardize the enterprise equipment standard system
With the increase of equipment condition monitoring time, the continuous accumulation and normalization of data information, a normative standard system for different equipment of enterprises can be gradually established.
Data analysis
Machine state data type
Machine state trend analysis
Machine state spectrum analysis
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Device state sensing and fault diagnosis technology
Adaptive filtering:
For complex industrial scenarios, the data is filtered first and processed later to improve the detection accuracy and reduce the probability of false alarm
Bayesian data fusion:
Fusion of audio data, vibration data, stress data analysis results, state judgment is more accurate, can detect more types of fault
Automatic cluster analysis:
Supports automatic judgment of different scenarios, reducing installation and commissioning workload
Supervised Learning AI:
The operation and judgment of the artificial intelligence algorithm are timely, and the time of data analysis and status judgment is less than 1 second
Machine Learning:
The algorithm automatically learns data changes caused by device aging and ADAPTS to the whole life cycle of the device
Taking a device as a whole, it is necessary to analyze the measuring points under the device as a whole
Need to read the equipment speed (pre-read equipment ledger information, manual input in advance, each input can be automatically saved as the equipment file information), equipment information (pre-read equipment ledger information, no manual input, distinguish rolling bearings or plain bearings)
The second stage: add equipment types (water house, pumping equipment, etc.), establish equipment models, and distinguish the unique failure modes of various types of equipment;
The third stage: Categorize and record the cases of different device types, automatically find and compare the case base during intelligent analysis, and collect the probability of corresponding characteristic information of different faults;
