Toward improving the real-time property and stability of the micro-pipetting process of an automatic enzyme-linked immunoassay analysis system, the fault detection of the process was studied by monitoring the pressure. A new method that combines the absolute degree of grey coefficients (ADGC) with the double extremes method (DEM) for monitoring pressure is proposed. The ADGC is used to demonstrate the variation of the pressure signal. In particular, the sectioning used is more flexible and is not constrained by the key timings of the micro-pipetting process. The data for a single point can be calculated in real time to obtain the double extremes (the extremes of the pressure and the first derivative) using the DEM. The DEM can therefore be performed without the need to reduce the sampling frequency, thereby guarantying the information integrity. By comparing the double extremes with the preset threshold, the fault types such as tip blockage, presence of air bubbles, and sample shortage can be distinguished. Moreover, a grey filtering technique based on the GM (1, 1) model is used to improve the stability of the pressure sensor output and significantly reduce the real-time computation requirements. Experiments were performed to verify the feasibility of the combined pressure monitoring method, and it was confirmed that it could be used to ensure real-time integrity of the information of the pressure curve. It can thus be confidently concluded that the method can be used to improve the accuracy and stability of monitoring the pressure of micro-pipetting.
Real-time Blood Pressure Monitoring in Porcine Tibial Artery Using LC Resonant Pressure Sensor
