Condition monitoring systems for point machines have been therefore implemented in some railways, but these condition monitoring systems have limitations for detecting incipient faults. Furthermore, the majority of condition monitoring systems which are currently in use cannot diagnose faults. The ability to diagnose faults is useful to maintenance staff who need to fix problems immediately.
This thesis proposes a methodology to detect and diagnose incipient faults using an advanced algorithm. In the main body of this thesis the author considers a new approach using Wavelet Transforms and Support vector machines for fault detection and diagnosis for railway electrical AC point machines operated in Japan.
The approach is further enhanced with more data sets collected from railway electrical DC point machines operated in Great Britain. Furthermore, a method to express the qualitative features of healthy and faulty waveforms was proposed to test the transferability of the specific algorithm parameters from one instance of a point machine to another, which is tested on railway electrical DC point machines used in Great Britain.
Finally, an approach based on Wavelet Transforms and Neural networks is used to predict the drive force when the point machine is operating. It is focused on technologies that have been quite scarcely investigated in this field, in particular, the use of lubricant oil viscosity monitoring and motor current signature analysis technologies for improving the condition monitoring in gearboxes.
On the one hand, a new solution based on magnetoelastic materials is presented for the monitorization of lubricant oil viscosity. On the other hand, motor current signature analysis MCSA is presented as a counterpart to traditional accelerometers for the monitorization of mechanical anomalies.
Broadly speaking, nowadays the most common maintenance practice for gearboxes is preventive maintenance. Lubricants and different mechanical parts such as gears and bearings are replaced on a periodic basis regardless of their health state.
Steadily, condition based maintenance is gaining importance, as it offers several advantages over preventive maintenance. Still, one of the drawbacks of condition-based maintenance is the need to assess the health state of the component, either by adding sensors on-line or by making punctual measurements by qualified staff periodic inspections. As the price of sensor technologies tends to go down and more electronics is incorporated to the machinery, the trend is to take advantage of the existing on-line monitoring options.
Lubricant oil is similar to the blood that flows inside us. Problems in our body have their reflection in blood, and in an analogous manner, problems in the gearbox will be manifested in the lubricant oil.
That is the reason why lubricant oil must be monitored. However, viscosity measurements are still made via off-line methods, using slow and costly equipment systems.
Therefore, in this Thesis the concept of on-line lubricant oil viscosity condition measurement is examined. In particular, a magnetoelastic kinematic viscosity sensor for on-line or in-line measurements is designed, built and tested.
The main advantage of the sensor prototype proposed is its capability to measure oil viscosity in a wide range of values from 32 cSt up to cSt , which is not known to any other sensor commercially available. Magnetoelastic materials exhibit an intimate coupling between their magnetic and elastic properties, in such a way that a magnetic excitation produces an elastic response of the material and vice versa.
Start date: Sep 1, | PhD Thesis: Condition Monitoring and Predictive Maintenance of Gearbox in Wind Turbines | Gears are widely used in mechanical . ing, this thesis introduces a vibration based condition monitoring system The condition monitoring system's hardware and software was designed following.
The principles governing the magnetoelastic effects and their beneficial use for the design of the on-line magnetoelastic viscosity sensor are explained in the Thesis.