An Investigation of Methods for Flow Measurements in a Gasketed Plate Heat Exchanger for Predictive Maintenance

Abstract: For a company to stay relevant in the long term, it is essential to keep up with the latest technology and continuously consider new innovative solutions for improving products and services. The gasketed plate heat exchanger (GPHE) is a mechanical device, and this work attempts to investigate different possibilities for incorporating sensor devices and predictive maintenance systems to improve the device. The parameters in focus were volume flow rate, pressure, temperature, and sound. Four different methods were evaluated for monitoring the parameters. Firstly, the thermal rig’s sensors provided ground truth data for the volume flow rate, pressure, and temperature. Secondly, an ultrasonic flow meter that mon- itored the volume flow rate. Thirdly, sticker sensors that measured pressure and temperature. Lastly, a microphone recorded the sound in proximity to the GPHE. The ground truth and audio data were used for several predictive tests. The predictive tests were conducted to evaluate and illustrate ideas for how machine learning can be utilized and applied to improve the capabilities of the GPHE. The predictive tests assessed were the possibility of predicting volume flow rate from pressure drop, classifying sounds emitted from the GPHE, and applying anomaly detection models for the monitored parameters. The thesis shows significant potential for the used sensor devices to provide continuous monitoring of the parameters of interest. Furthermore, it proposes several ideas for the usability of machine learning, using the acquired data and engineered features from said data.