Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

The thesis describes a novel computational strategy of Predictive Maintenance (fault diagnosis and fault prognosis) with ML and Deep Learning applications for an FG304 series external gear pump, also known as a domino pump. Due to the unavailability of a sufficient amount of experimental data, a novel approach of generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic model of the gear pump in a healthy and various faulty working conditions (e.g., clogging, radial gap variations, viscosity variations, etc.). The synthetic data generation technique is implemented by perturbing the frequency content of the time series to recreate other environmental conditions. These synthetically generated datasets are used to train the underlying ML metamodel. In addition, various types of feature extraction methods considered to extract the most discriminatory information from the data. For fault diagnosis, three types of ML classification algorithms are employed, namely Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Hyper-parameters of the ML algorithms is optimised with a staggered approach. In addition, a real case study of fault diagnosis and fault prognosis of an external gear pump considering noisy measurements to understand the sensitivity of the employed ML algorithms by adding noise on the training dataset and test dataset. A series of numerical examples are presented, enabling us to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy and for fault prognosis, the use of MLP algorithm provides the best prediction results.