A fault prognosis strategy for an external gear pump using Machine Learning algorithms and synthetic data generation methods - Kayal Lakshmanan (Applied Mathematical Modelling, 2023)
Fault prognosis is an important area of research that aims to predict and diagnose faults in complex systems. The sudden failure of industrial components can have adverse consequences for an organisation in terms of time, cost and workflow. It is, therefore, critical to ensure the maintenance of equipment components in optimal condition in order to avoid downtime that may cause significant disruption. Due to this reason, in recent years, there has been an increasing interest in creating innovative methods for fault prognosis that can increase system reliability and reduce maintenance costs. Gear pumps are widely utilised in a variety of industrial applications, and their reliability and effectiveness are crucial for achieving optimal system performance. Gear pumps, on the other hand, are prone to malfunctions and failures, which can result in substantial downtime and maintenance costs. The challenge is to develop a fault prognosis approach that is reliable and accurate enough to detect and diagnose defects in a gear pump before they cause system failures. This paper proposes a novel computational strategy for the fault prognosis of an external gear pump using Machine Learning (ML) approaches. Due to the unavailability of sufficient experimental datain the vicinity of failure mechanisms, a novel approach to generating a high-fidelity in-silico dataset via a Computational Fluid Dynamic (CFD) model of the gear pump in healthy and faulty working conditions is presented. However, considering the computational demand for rerunning the same CFD simulations, novel synthetic data generation techniques are implemented by perturbing the frequency content of the time series to recreate other working conditions and constructing degradation behaviour using linear and cubice interpolation methods for run-to-failure scenarios. The synthetically created datasets are used to train the underlying ML metamodel for fault prognosis. Two types of ML algorithms are employed for fault prognosis: Multilayer Perceptron (MLP) and Support Vector Machine (SVM) algorithms. A series of numerical examples are shown, allowing us to infer that the proposed modelling technique is feasible in an industrial setting and that employing the MLP algorithm delivers superior fault prognosis results when compared to SVM. Furthermore, datasets generated using the cubic interpolation method have lower prediction errorsthan datasets generated using the linear interpolation method due to the smoother degradation behaviour in the data.