Failure analysis on a water pump based on a low-cost MEMS accelerometer and machine learning classifiers

Abstract

This work presents a failure diagnosis tool for a water pump using a low-cost MEMS accelerometer. It was inserted three types of failures: rotor blade (new and damaged), pump soleplate tightness (stiff or loose), and cavitation, in this case on three conditions: none, incipient and severe, totaling twelve fault combinations. These conditions were tested under two different speeds to perform the diagnosis, totaling twenty-four tests. In all cases, the vibration signals from axes X, Y, and Z were acquired. Some features extracted from the vibration spectra from X-axis were used to compose the dataset. These data were analyzed employing logistic regression, a linear support vector machine (SVM), and an artificial neural network multilayer perceptron (ANN-MLP). We compared these three techniques of machine learning and evaluated which one was able to obtain the most accurate result. Using the ANN-MLP, the system was able to detect all three types of failures inserted, with about 100% of accuracy on the rotor blade condition, 92% for anchorage faults, and about 99% accuracy on cavitation state. As a conclusion, it is demonstrated that this classifier algorithm can be used to process the data from the low-cost MEMS accelerometer in predictive maintenance as an accurate tool.

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

“…The NN performance measurements mentioned above are in accordance with the findings of other studies utilizing ML techniques for the classification or detection of malfunctions in pump equipment [ 17 , 18 , 22 , 23 ] or even the findings of more general studies, such as the results presented in [ 19 , 20 , 21 ].…”

“…A handful of analogous studies have been conducted to detect malfunctions using machine learning techniques in water pumps. Some of the methods of analyzing failures in water pumps include the use of low-cost accelerometers paired with machine learning classifications [ 22 ] and techniques used to forecast water demands [ 23 ]. In a parallel manner, machine learning models have been implemented for the early fault prediction of industrial centrifugal oil and gas pumps [ 24 ].…”