Control valve diagnostics with industry 4.0 enabled valve couplings
Control valves are critical components in industrial processes, responsible for controlling flow rates, pressures, and temperatures. These valves need to work reliably and accurately, and any malfunction can result in significant downtime, lost production, or even safety hazards. Therefore, it’s essential to detect early signs of problems in control valves and fix them before they cause severe consequences. However, traditional diagnostic methods fall short in providing sufficient data for intelligent process monitoring, making them inadequate for Industry 4.0 applications. To address this issue, Wesa-Control, a German engineering company, has developed new measurement couplings that allow for direct measurements of the control valve’s condition under process conditions, enabling a proactive approach to maintenance.
In the past, the primary diagnostic methods for control valves were end limit switches or position transmitters that indicate the actuator’s position. However, these measurements don’t provide enough information about the control valve’s quality or condition, and early detection of faults is challenging without any data collection or analysis. Moreover, analog-controlled control valves with intelligent positioners can measure the travel distance, provide open/close signals, measure the running time, and detect pressure changes in the actuator, generating some insights into the valve’s condition. However, these measurements are still indirect, and the measurement results can be influenced by friction forces in the actuator. As a result, it’s challenging to pinpoint the cause of any changes in the valve’s performance, whether it’s caused by the valve or the actuator. Furthermore, measurement couplings that rely on running time measurements are inadequate for quality determination in end positions and not always suitable for control valve diagnostics.
To overcome these limitations, Wesa-Control has developed two new measurement couplings, Torsion Measurement Coupling (TMC) and Linear Measurement Coupling (LMC), that can measure the torque or pressing forces directly in control valves, providing accurate and reliable data on the valve’s quality and condition. These measurement couplings are complementary to existing measurement methods, filling the gaps in control valve diagnostics and facilitating a proactive approach to maintenance.
The Torsion Measurement Coupling (TMC) can be installed in any rotary actuator, measuring the torsional forces generated by the valve shaft. The TMC’s measurement data can detect deviations from the normal condition and pinpoint potential issues, such as buildup or wear, before they cause significant problems. Moreover, the TMC’s data can help diagnose faults in the actuator or the valve, allowing for targeted maintenance or replacement. The TMC is easy to install, requires no maintenance, and can be integrated into the control system, providing real-time data on the valve’s condition and performance.
The Linear Measurement Coupling (LMC) can be installed in any linear or actuated valve, measuring the pressing force generated by the valve stem or the piston rod. The LMC’s measurement data can detect deviations from the normal condition and pinpoint potential issues, such as bending or jamming, before they cause significant problems. Moreover, the LMC’s data can help diagnose faults in the actuator or the valve, allowing for targeted maintenance or replacement. The LMC is easy to install, requires no maintenance, and can be integrated into the control system, providing real-time data on the valve’s condition and performance.
These measurement couplings can be installed in existing control valves, allowing for retroactive diagnostics or incorporated into new valves, providing a proactive maintenance solution. The measurement data can be analyzed using data analytics tools, such as machine learning algorithms, to detect patterns and anomalies, generating insights into the control valve’s performance and potential issues. Moreover, the data can be used for predictive maintenance, scheduling maintenance activities based on the valve’s actual condition, rather than a preset maintenance schedule, saving time and resources.
(This is an English summary of the original article)