Enhancement of reliability in condition monitoring techniques in wind turbines

The majority of electrical failures in wind turbines occur in the semiconductor components (IGBTs) of converters. To increase reliability and decrease the maintenance costs associated with this component, several health-monitoring methods have been proposed in the literature. Many laboratory-based tests have been conducted to detect the failure mechanisms of the IGBT in their early stages through monitoring the variations of thermo-sensitive electrical parameters. The methods are generally proposed and validated with a single-phase converter with an air-cored inductive or resistive load. However, limited work has been carried out considering limitations associated with measurement and processing of these parameters in a three-phase converter. Furthermore, looking at just variations of the module junction temperature will most likely lead to unreliable health monitoring as different failure mechanisms have their own individual effects on temperature variations of some, or all, of the electrical parameters. A reliable health monitoring system is necessary to determine whether the temperature variations are due to the presence of a premature failure or from normal converter operation. To address this issue, a temperature measurement approach should be independent from the failure mechanisms. In this paper, temperature is estimated by monitoring an electrical parameter particularly affected by different failure types. Early bond wire lift-off is detected by another electrical parameter that is sensitive to the progress of the failure. Considering two separate electrical parameters, one for estimation of temperature (switching off time) and another to detect the premature bond wire lift-off (collector emitter on-state voltage) enhance the reliability of an IGBT could increase the accuracy of the temperature estimation as well as premature failure detection

Condition monitoring of bearing faults using the stator current and shrinkage methods

Producción CientíficaCondition monitoring of bearings is an open issue. The use of the stator current to monitor induction motors has been validated as a very advantageous and practical way to detect several types of faults. Nevertheless, for bearing faults, the use of vibrations or sound generally offers better results in the accuracy of the detection, although with some disadvantages related to the sensors used for monitoring. To improve the performance of bearing monitoring, it is proposed to take advantage of more information available in the current spectra, beyond the usually employed, incorporating the amplitude of a significant number of sidebands around the first eleven harmonics, growing exponentially the number of fault signatures. This is especially interesting for inverter-fed motors. But, in turn, this leads to the problem of overfitting when applying a classifier to perform the fault diagnosis. To overcome this problem, and still exploit all the useful information available in the spectra, it is proposed to use shrinkage methods, which have been lately proposed in machine learning to solve the overfitting issue when the problem has many more variables than examples to classify. A case study with a motor is shown to prove the validity of the proposal.CAPES (process BEX552269/2011-5

Condition Monitoring of Power Cables

A National Grid funded research project at Southampton has investigated possible methodologies for data acquisition, transmission and processing that will facilitate on-line continuous monitoring of partial discharges in high voltage polymeric cable systems. A method that only uses passive components at the measuring points has been developed and is outlined in this paper. More recent work, funded through the EPSRC Supergen V, UK Energy Infrastructure (AMPerES) grant in collaboration with UK electricity network operators has concentrated on the development of partial discharge data processing techniques that ultimately may allow continuous assessment of transmission asset health to be reliably determined

The effect of tool fixturing quality on the design of condition monitoring systems for detecting tool conditions

Condition monitoring systems of machining processes are essential technology for improving productivity and automation. Tool wear monitoring of cutting tools is one of the important applications in this area. In this paper, the effect of collet fixturing quality on the design of condition monitoring systems to detect tool wear is discussed. The paper investigates the difference in the system's behaviour and the changes in the condition monitoring system when the cutting tool is not rigidly fastened to the collet. A group of sensors, namely acoustic emission, force, strain, vibration and sound, are used to design the condition monitoring system. Automated Sensor and Signal Processing Selection (ASPS) approach1 is implemented to address the effect of the tool holding device (collet) on the monitoring system and the most sensitive sensors and signal processing method to detect tool wear. The results prove that the change in the fixturing quality could have significant effect on the design of the condition monitoring system and the behaviour of the system

Structural health monitoring and bridge condition assessment

Thesis (Ph.D.) University of Alaska Fairbanks, 2016This research is mainly in the field of structural identification and model calibration, optimal sensor placement, and structural health monitoring application for large-scale structures. The ultimate goal of this study is to identify the structure behavior and evaluate the health condition by using structural health monitoring system. To achieve this goal, this research firstly established two fiber optic structural health monitoring systems for a two-span truss bridge and a five-span steel girder bridge. Secondly, this research examined the empirical mode decomposition (EMD) method’s application by using the portable accelerometer system for a long steel girder bridge, and identified the accelerometer number requirements for comprehensively record bridge modal frequencies and damping. Thirdly, it developed a multi-direction model updating method which can update the bridge model by using static and dynamic measurement. Finally, this research studied the optimal static strain sensor placement and established a new method for model parameter identification and damage detection.Chapter 1: Introduction -- Chapter 2: Structural Health Monitoring of the Klehini River Bridge -- Chapter 3: Ambient Loading and Modal Parameters for the Chulitna River Bridge -- Chapter 4: Multi-direction Bridge Model Updating using Static and Dynamic Measurement -- Chapter 5: Optimal Static Strain Sensor Placement for Bridge Model Parameter Identification by using Numerical Optimization Method -- Chapter 6: Conclusions and Future Work

On-line condition monitoring of transition assets

There are a number of medium voltage (MV) power distribution cable networks worldwide that are constructed predominantly of mass impregnated paper cables - London being one of these. Paper insulated lead covered (PILC) cables were extensively laid in the 50s and 60s before the introduction of cheaper polymeric alternatives that were sufficiently reliable. The current operational state of these networks has shown a gradual increase in failure rates of the previously reliable paper cables that are drawing to the end of their expected design life. Utilities are faced with the prospect of the impending failure of large sections of their prized asset and are keen to develop tools to better understand the health of their hardware. The analysis of partial discharge (PD) signals produced by the cables has been identified as a economically viable option to provide continuous condition monitoring of PILC cable circuits. Clearly, a comprehensive understanding of how PD activity relates to the various failure mechanisms exhibited by cable circuits in the field is required. A recently published technique for PD source discrimination was coupled with an understanding of the experiment and applied to the experiment data to isolate the signals specific to each degradation mechanism [1]. This technique has been applied to both rotation machines and transformer systems with promising results. PD signal discrimination is seen as the first step towards an autonomous condition monitoring futur

Condition monitoring benefit for offshore wind turbines

As more offshore wind parks are commissioned, the focus will inevitably shift from a planning, construction and warranty focus to an operation, maintenance and investment payback focus. In this latter case, both short-term risks associated with wind turbine component assemblies, and longterm risks related to structural integrity of the support structure, are highly important. This research focuses on the role of condition monitoring to lower costs associated with short-term reliability and long-term asset integrity. This enables comparative estimates of life cycle costs and reduction in uncertainty, both of which are of value to investors