Sravani Vemulapalli, Venkata Santhosh Krishnan
Department of Electronics and Communication Engineering, Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal 576104, India.
Centre for Excellence in Cyber Physical System, Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India.
Sensors (Basel). 2023 Jul 24;23(14):6633. doi: 10.3390/s23146633.
Sensors and transducers play a vital role in the productivity of any industry. A sensor that is frequently used in industries to monitor flow is an orifice flowmeter. In certain instances, faults can occur in the flowmeter, hindering the operation of other dependent systems. Hence, the present study determines the occurrence of faults in the flowmeter with a model-based approach. To do this, the model of the system is developed from the transient data obtained from computational fluid dynamics. This second-order transfer function is further used for the development of linear-parameter-varying observers, which generates the residue for fault detection. With or without disturbance, the suggested method is capable of effectively isolating drift, open-circuit, and short-circuit defects in the orifice flowmeter. The outcomes of the LPV observer are compared with those of a neural network. The open- and short-circuit faults are traced within 1 s, whereas the minimum time duration for the detection of a drift fault is 5.2 s and the maximum time is 20 s for different combinations of threshold and slope.
传感器和变送器在任何行业的生产效率中都起着至关重要的作用。工业中常用于监测流量的一种传感器是孔板流量计。在某些情况下，流量计可能会出现故障，从而妨碍其他相关系统的运行。因此，本研究采用基于模型的方法来确定流量计中故障的发生情况。为此，从计算流体动力学获得的瞬态数据中开发系统模型。该二阶传递函数进一步用于开发线性参数变化观测器，该观测器生成用于故障检测的残差。无论有无干扰，所提出的方法都能够有效地隔离孔板流量计中的漂移、开路和短路缺陷。将线性参数变化观测器的结果与神经网络的结果进行比较。开路和短路故障在1秒内被检测到，而对于不同的阈值和斜率组合，检测漂移故障的最短持续时间为5.2秒，最长时间为20秒。
