Lifetime Based Health Indicator for Bearings using Convolitional Neural Networks

Master's thesis Renewable Energy ENE500 - University of Agder 2019Out of all the components in rotating electrical machinery, bearings have the highest failure rate. Bearingdegradation is a seemingly random process which is hard to both model and predict. Countless of con-dition based methods and algorithms have been proposed in order to accurately diagnose incipient faultsand estimate the remaining useful lifetime of bearings. These methods are often complex and hard to im-plement. In this thesis, a data-driven method of estimating a linear lifetime based health indicator (HI)using convolutional neural networks (CNNs) is proposed. The idea behind the method is to train a CNNmodel to recognize the shapes and distributions of vibration data in order to predict a HI with minimalpre-processing. Two models are presented: A CNN that takes time-series vibration data as input and aCNN that takes vibration frequency spectrum data as input. Finally, HIs are predicted on unique datasetsand their respective remaining useful lifetimes (RULs) are estimated as part of the model validation process.The results show that the models are able to recognize relevant fault features to a certain degree. However, accurate predictions have proven difficult in many cases

Five-Axis Machine Tool Condition Monitoring Using dSPACE Real-Time System

This paper presents the design, development and SIMULINK implementation of the lumped parameter model of C-axis drive from GEISS five-axis CNC machine tool. The simulated results compare well with the experimental data measured from the actual machine. Also the paper describes the steps for data acquisition using ControlDesk and hardware-in-the-loop implementation of the drive models in dSPACE real-time system. The main components of the HIL system are: the drive model simulation and input – output (I/O) modules for receiving the real controller outputs. The paper explains how the experimental data obtained from the data acquisition process using dSPACE real-time system can be used for the development of machine tool diagnosis and prognosis systems that facilitate the improvement of maintenance activities

Methods of condition monitoring and fault detection for electrical machines

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Prognostics of Ball Bearings in Cooling Fans

Ball bearings have been used to support rotating shafts in machines such as wind turbines, aircraft engines, and desktop computer fans. There has been extensive research in the areas of condition monitoring, diagnostics, and prognostics of ball bearings. As the identification of ball bearing defects by inspection interrupts the operation of rotating machines and can be costly, the assessment of the health of ball bearings relies on the use of condition monitoring techniques. Fault detection and life prediction methods have been developed to improve condition-based maintenance and product qualification. However, intermittent and catastrophic system failures due to bearing problems still occur resulting in loss of life and increase of maintenance and warranty costs. Inaccurate life prediction of ball bearings is of concern to industry. This research focuses on prognostics of ball bearings based on vibration and acoustic emission analysis to provide early warning of failure and predict life in advance. The failure mechanisms of ball bearings in cooling fans are identified and failure precursors associated with the defects are determined. A prognostic method based on Bayesian Monte Carlo method and sequential probability ratio test is developed to predict time-to-failure of ball bearings in advance. A benchmark study is presented to demonstrate the application of the developed prognostic method to desktop computer fans. The prognostic method developed in this research can be extended as a general method to predict life of a component or system

On the reliability of electrical drives for safety-critical applications

The aim of this work is to present some issues related to fault tolerant electric drives,which are able to overcome different types of faults occurring in the sensors, in thepower converter and in the electrical machine, without compromising the overallfunctionality of the system. These features are of utmost importance in safety-criticalapplications. In this paper, the reliability of both commercial and innovative driveconfigurations, which use redundant hardware and suitable control algorithms, will beinvestigated for the most common types of fault: besides standard three phase motordrives, also multiphase topologies, open-end winding solutions, multi-machineconfigurations will be analyzed, applied to various electric motor technologies. Thecomplexity of hardware and control strategies will also be compared in this paper, sincethis has a tremendous impact on the investment costs

Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks

In real-world applications - to minimize the impact of failures - machinery is often monitored by various sensors. Their role comes down to acquiring data and sending it to a more powerful entity, such as an embedded computer or cloud server. There have been attempts to reduce the computational effort related to data processing in order to use edge computing for predictive maintenance. The aim of this paper is to push the boundaries even further by proposing a novel architecture, in which processing is moved to the sensors themselves thanks to decrease of computational complexity given by the usage of compressed recurrent neural networks. A sensor processes data locally, and then wirelessly sends only a single packet with the probability that the machine is working incorrectly. We show that local processing of the data on ultra-low power wireless sensors gives comparable outcomes in terms of accuracy but much better results in terms of energy consumption that transferring of the raw data. The proposed ultra-low power hardware and firmware architecture makes it possible to use sensors powered by harvested energy while maintaining high confidentiality levels of the failure prediction previously offered by more powerful mains-powered computational platforms

Development of high efficiency high speed permanent magnet generator

Renewable energy technology is steadily gaining importance in the energy market because of the limited nature of fossil fuels, as well as the political pressures to reduce carbon emissions. To ensure sustainable development, adequate and affordable energy should be made available to satisfy the demand of electric energy. The High Speed Permanent Magnet (HSPM) generator is designed and developed and is expected to deliver 10 kW output power as well as to achieve a speed of 30000 RPM, however, to achieve a compact and efficient design with lower excitation losses, magnetizing currents and rotor losses requires the HSPM generator to be operated at high rated speeds of approximately 30000 RPM. However, at high speeds these machines produce a substantial amount of heat. This makes the thermal management of these machines difficult and complicated, which leads to demagnetization and the reduction of the output power and shortens the lifetime of the critical components such as the bearings. This thesis presents the design and development of the HSPM generator. It also identifies the heat generated by means of electromagnetic, mechanical and core losses. The development of an adequate cooling system (cooling jacket) is presented to avoid hot spots in the generator and thermal damage to the magnets, resulting in demagnetization. The use of pressurized oil air particles as a lubrication method for the bearings of the generator is also considered to avoid: thermal damage and starvation at the rolling element and to address the predominant concern of effectively cooling the HSPM generator ball bearings at elevated speeds. The HSPM generator is designed and developed to operate at a maximum speed of 30000 RPM to deliver 10 kW output power and is subjected to 80~92°C temperature rise with an idle power consumption of ~2kW, enough to cause hot spots on the generator, demagnetization of the magnets and severe impact to the rolling elements of the bearings. The developed cooling jacket and the newly developed oil air mist lubrication arrangement enables the control of the temperature rise of the generator and the temperature rise at the rolling element, respectively. A steady state analysis was also carried out at motor maximum power output to determine its safe operation with the objective of finding an optimal operating condition by performing a parametric study on the effect of cooling. A 3D steady state model of a 10-kW electric permanent magnet machine was generated and investigated with one cooling jacket layout. The end windings and bearings were not considered to simplify the motor model. Numerical analysis is performed with two different coolant flow rates, no flow and maximum flow (3.5 m3 /h) with special emphasis on the maximum motor temperature. The analytical calculations for the role of coolant flowrate on heat transfer characteristics for a high speed generator, showed that the convection heat transfer coefficient increases with an increase in flowrate (0.3 – 3.5 m3 /hr), while the numerical simulations showed that the maximum coolant flowrate conditions achieved lower temperature generation (27.9°C at the front bearing) throughout the generator compared to no coolant flowrate (43.7°C at the front bearing). The detailed understanding of the effects of these parameters on the generator’s temperature field will help in validating the performance of the generator with actual results

Multiple Damage Progression Paths in Model-Based Prognostics

Model-based prognostics approaches employ domain knowledge about a system, its components, and how they fail through the use of physics-based models. Component wear is driven by several different degradation phenomena, each resulting in their own damage progression path, overlapping to contribute to the overall degradation of the component. We develop a model-based prognostics methodology using particle filters, in which the problem of characterizing multiple damage progression paths is cast as a joint state-parameter estimation problem. The estimate is represented as a probability distribution, allowing the prediction of end of life and remaining useful life within a probabilistic framework that supports uncertainty management. We also develop a novel variance control mechanism that maintains an uncertainty bound around the hidden parameters to limit the amount of estimation uncertainty and, consequently, reduce prediction uncertainty. We construct a detailed physics-based model of a centrifugal pump, to which we apply our model-based prognostics algorithms. We illustrate the operation of the prognostic solution with a number of simulation-based experiments and demonstrate the performance of the chosen approach when multiple damage mechanisms are activ