The fault diagnosis function of electric ball valves relies on sensors for real-time monitoring. Its core lies in collecting operational data from multiple sensors, combining signal processing with algorithmic analysis to proactively identify potential faults and generate warnings. This process involves sensor selection, data acquisition, signal processing, fault feature extraction, algorithmic diagnosis, and warning output, forming a complete closed-loop monitoring system.
Electric ball valve sensors are the foundation of fault diagnosis. Electric ball valves typically integrate pressure, temperature, flow, vibration, and position sensors. Pressure sensors monitor the pressure differential across the valve; abnormal fluctuations may indicate seal failure or blockage. Temperature sensors detect valve body and media temperatures; localized overheating may indicate increased friction or seal damage. Flow sensors provide real-time feedback on media flow; sudden drops in flow may be caused by ball jamming or seal leakage. Vibration sensors capture the vibration frequency of the actuator during operation; abnormal vibration is often associated with wear or loosening of mechanical components. Position sensors accurately record the ball opening to ensure that the actual valve status is consistent with the control instructions.
Data acquisition requires high accuracy and real-time performance. After sensors convert physical quantities such as pressure, temperature, and flow into electrical signals, they must undergo amplification, filtering, and linearization by signal conditioning circuits to eliminate noise and improve signal quality. Subsequently, the analog signals are converted to digital signals via an A/D converter and analyzed in real time by a microcontroller or dedicated processor. During this process, the sampling frequency and resolution must be adjusted based on the sensor's characteristics. For example, vibration signals require high-frequency sampling to capture transient shocks, while temperature signals can be sampled at a lower rate to reduce the data processing burden.
Signal processing is a key step in fault diagnosis. Techniques such as time-domain analysis, frequency-domain analysis, and wavelet transforms can be used to extract fault signatures from raw signals. For example, time-domain analysis can detect sudden changes in pressure signals, frequency-domain analysis can identify abnormal frequency components in vibration signals, and wavelet transforms are suitable for extracting local features from non-stationary signals. Peaks at specific frequencies in vibration signals may indicate gear wear or bearing failure. Periodic fluctuations in flow signals may be caused by unstable ball rotation.
After fault signatures are extracted, algorithms are used for pattern recognition and fault classification. Rule-based expert systems can determine fault types based on preset thresholds. For example, if the pressure differential consistently exceeds a set value, the system identifies a seal leak. Machine learning-based models, on the other hand, can handle more complex nonlinear relationships. Using training data, they learn signal characteristics under normal and fault conditions, enabling more accurate fault prediction. Deep learning algorithms, such as convolutional neural networks (CNNs), can automatically extract deep features from signals, improving the ability to detect subtle faults.
The early warning output stage converts diagnostic results into actionable instructions. When the algorithm determines a fault risk, the system outputs a warning code via the on-site display, mobile app, or host software. For example, "E01" indicates abnormal pressure and "E02" indicates excessive vibration. This warning can also trigger an emergency shutoff function, closing the valve to prevent further accidents. In critical applications, such as LNG tank outlets, the system can also interact with pressure sensors to automatically close the valve in the event of overpressure, ensuring safety.
The fault diagnosis function of the electric ball valve automates the entire process from data collection to fault warning through the collaboration of sensors and intelligent algorithms. This technology not only improves the reliability and service life of valves, but also provides data support for preventive maintenance of industrial systems, and promotes the development of equipment management towards intelligence.
