Model of the Floating Bearing Bushing Movement in an External Gear Pump and the Relation to Its Parameterization

This article presents the modeling, simulation and experimental validation of the movement of the floating bearing bushing in an external gear pump. As a starting point, a complete pump parameterization was carried out through standard tests, and these parameters were used in a first bond graph model in order to simulate the gear pump behavior. This model was experimentally validated under working conditions in field tests. Then, a sophisticated bond graph model of the movement of the floating bushing was developed from the equations that define its lubrication. Finally, as a result, both models were merged by integrating the dynamics of the floating bushing bearing with the variation of the characteristic parameters (loss coefficients). Finally, the final model was experimentally validated both in laboratory and field tests by assembling the pump in a drilling machine to drive the auxiliary movements. The novelty of this article is the conception and construction of a simple and experimentally validated tool for the study of a gear pump, which relates its macroscopic behavior as a black box (defined by the loss coefficients) to the internal changes of the unit (defined by its internal lubrication).

The Influence of Radial and Axial Gaps on Volumetric Efficiency of External Gear Pumps

The design of gear pumps and motors is focused on more efficient units which are possible to achieve using advanced numerical simulation techniques. The flow that appears inside the gear pump is very complex, despite the simple design of the pump itself. The identification of fluid flow phenomena in areas inside the pump, considering the entire range of operating parameters, is a major challenge. This paper presents the results of simulation studies of leakages in axial and radial gaps in an external gear pump carried out for different gap shapes and sizes, as well as various operating parameters. To investigate the processes that affect pump efficiency and visualize the fluid flow phenomena during the pump’s operation, a CFD model was built. It allows for a detailed analysis of the impact of the gears’ eccentricity on leakages and pressure build-up on the circumference. Performed simulations made it possible to indicate the relationship between leakages resulting from the axial and radial gap, which has not been presented so far. To verify the CFD model, experimental investigations on the volumetric efficiency of the external gear pump were carried out. Good convergence of results was obtained; therefore, the presented CFD model is a universal tool in the study of flow inside external gear pumps.

Parameterization, Modeling, and Validation in Real Conditions of an External Gear Pump

This article presents a methodology for predicting the fluid dynamic behavior of a gear pump over its operating range. Complete pump parameterization was carried out through standard tests, and these parameters were used to create a bond graph model to simulate the behavior of the unit. This model was experimentally validated under working conditions in field tests. To carry this out, the pump was used to drive the auxiliary movements of a drilling machine, and the experimental data were compared with a simulation of the volumetric behavior under the same conditions. This paper aims to describe a method for characterizing any hydrostatic pump as a “black box” model predicting its behavior in any operating condition. The novelty of this method is based on the correspondence between the variation of the parameters and the internal changes of the unit when working in real conditions, that is, outside a test bench.

Efficiency of External Gear Pump

The purpose of the research was to analyze the load characteristics of external gear pumps. As the initial data, the results of factory tests of the NMSH and SH pumps, provided by HMS Livgidromash JSC, were used. The load characteristics of the gear pump NMSH32-10 were constructed, and the dependence of this pump efficiency on the dimensionless pressure and Hersey number was given. The efficiency of the SH 40-4 pump was recalculated according to the test data. Findings of research show that for diesel fuel and oil, the calculated efficiency differs from the values indicated in the technical data sheet by more than 25%, while for fuel oil they differ slightly. This testifies to the non-monotonic dependence between the SH 40-4 pump efficiency and the viscosity. In the entire investigated range of pressures, with an increase in the viscosity of the pumped liquid, the efficiency first increases and then noticeably decreases. The paper presents the dependence of the volumetric and mechanical efficiency of the SH 40-4 pump on the Hersey number at three values of the viscosity of the pumped liquid.

Predictive Maintenance of an External Gear Pump using Machine Learning Algorithms

The importance of Predictive Maintenance is critical for engineering industries, such as manufacturing, aerospace and energy. Unexpected failures cause unpredictable downtime, which can be disruptive and high costs due to reduced productivity. This forces industries to ensure the reliability of their equip-ment. In order to increase the reliability of equipment, maintenance actions, such as repairs, replacements, equipment updates, and corrective actions are employed. These actions affect the flexibility, quality of operation and manu-facturing time. It is therefore essential to plan maintenance before failure occurs.Traditional maintenance techniques rely on checks conducted routinely based on running hours of the machine. The drawback of this approach is that maintenance is sometimes performed before it is required. Therefore, conducting maintenance based on the actual condition of the equipment is the optimal solu-tion. This requires collecting real-time data on the condition of the equipment, using sensors (to detect events and send information to computer processor).Predictive Maintenance uses these types of techniques or analytics to inform about the current, and future state of the equipment. In the last decade, with the introduction of the Internet of Things (IoT), Machine Learning (ML), cloud computing and Big Data Analytics, manufacturing industry has moved forward towards implementing Predictive Maintenance, resulting in increased uptime and quality control, optimisation of maintenance routes, improved worker safety and greater productivity.The present 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. In the absence of a comprehensive set of experimental data, synthetic data generation techniques are implemented for Predictive Maintenance by perturbing the frequency content of time series generated using High-Fidelity computational techniques. In addition, various types of feature extraction methods considered to extract most discriminatory informations 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 (NB) algorithms. For prognosis, ML regression algorithms, such as MLP and SVM, are utilised. Although significant work has been reported by previous authors, it remains difficult to optimise the choice of hyper-parameters (important parameters whose value is used to control the learning process) for each specific ML algorithm. For instance, the type of SVM kernel function or the selection of the MLP activation function and the optimum number of hidden layers (and neurons).It is widely understood that the reliability of ML algorithms is strongly depen-dent upon the existence of a sufficiently large quantity of high-quality training data. In the present thesis, due to the unavailability of experimental data, a novel high-fidelity in-silico dataset is generated via a Computational Fluid Dynamic (CFD) model, which has been used for the training of the underlying ML metamodel. In addition, a large number of scenarios are recreated, ranging from healthy to faulty ones (e.g. clogging, radial gap variations, axial gap variations, viscosity variations, speed variations). Furthermore, the high-fidelity dataset is re-enacted by using degradation functions to predict the remaining useful life (fault prognosis) of an external gear pump.The thesis explores and compares the performance of MLP, SVM and NB algo-rithms for fault diagnosis and MLP and SVM for fault prognosis. In order to enable fast training and reliable testing of the MLP algorithm, some predefined network architectures, like 2n neurons per hidden layer, are used to speed up the identification of the precise number of neurons (shown to be useful when the sample data set is sufficiently large). Finally, a series of benchmark tests are presented, enabling to conclude that for fault diagnosis, the use of wavelet features and a MLP algorithm can provide the best accuracy, and the MLP al-gorithm provides the best prediction results for fault prognosis. In addition, benchmark examples are simulated to demonstrate the mesh convergence for the CFD model whereas, quantification analysis and noise influence on training data are performed for ML algorithms.