| Summary: | The head and flow rate of a pump characterize the pump performance, which help determine whether maintenance is needed. In the proposed method, instead of a traditional flowmeter and manometer, the operating points are identified using data collected from accelerometers and microphones. The dataset is created from a test rig consisting of a standard centrifugal water pump and measurement system. After implementing preprocessing techniques and Convolutional Neural Networks (CNNs), the trained models are obtained and evaluated. The influence of the sensor location and the performance of different signals or signal combinations are investigated. The proposed method achieves a mean relative error of 7.23% for flow rate and 2.37% for head with the best model. By employing two data augmentation techniques, performance is further improved, resulting in a mean relative error of 3.55% for flow rate and 1.35% for head with the sliding window technique.
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