Prognostic and Health Management

By collecting vibration, current, sound, temperature and other signals data from key parts of the equipment, using feature engineering, time-frequency perception, deep learning and other methods to mine the information contained in the signals, comprehensively intelligent monitoring of the overall and local status information of the equipment, diagnosis of the health status of the equipment, and timely maintenance strategies to help enterprises achieve predictive maintenance of equipment.

Home Solutions

Intelligent Factory Management(10+N)

Machine Vision

Key technologies

Vibration signal:Time-frequency sensing technology, TsuC-LS feature matching technology, feature engineering technology, proprietary diagnostic model for high speed, low speed and variable operating conditions;

Electrical signal: CT transform technology, body analysis technology;

Sound signal: low speed heavy duty equipment fault diagnosis technology;

Acoustic emission frequency: low speed and high precision fault diagnosis technology Signal analysisAlarm managementKnowledge baseTrend analysis, pre-AustraliaComprehensive diagnosis and scoringDiagnostic report management.

Application scenarios

Main industry:High-value, mechanical-model complex equipment and industries with high safety requirements: rail transit, Power equipment, petrochemical, military and other fields. The equipment value is high enough, the impact on production, the mechanism model is more complex but large civil industries: CNC machine tools, water conservancy, paper industry, power and energy systems.

Main equipment:Elevator, belt conveyor, steam turbine, gas turbine, centrifugal compressor, reciprocating compressor, screw compressor, centrifugal fan, axial fan, Roots fan, centrifugal pump, reciprocating pump, axial flow pump, screw pump, transmission, etc.