Acoustic emission and signal processing for fault detection and location in composite materials

The renewable energy industry is in a constant improvement in order to compete and cover any evolving opportunity presented. Nowadays one of those remarkable competitive advantages is focused on maintenance management and terms as operating and maintenance costs, availability, reliability, safety, lifetime, etc. The objectives of this paper are focused on the blades of a wind turbine. A structural health monitoring study is presented, that starts with the collection and analysis of data coming from different non-destructive tests. Signals from acoustic emissions are studied by a novel signal processing approach to detect cracks on the surface of the blades. The case study proposes a new localization method using macro-fibre composite sensors and actuators. The monitoring system uses three sensors strategically located on the blade section. Among the main difficulties involved in this first approach, the modal separation of the wave is taken into account for its importance when drawing conclusions concerning the crack. This effect is the result of the blade breakdown, producing different signals at multiple frequencies. Another drawback is associated to the direction of the fibres in the composite material. This is known as slowness profile, a function depending on the propagation speed. On the other hand, the main novelty of the approach presented is that it is able to predict the failure. In addition, it can be considered an accurate analysis as the solution will be always a single point obtained from a graphical method, i.e. the location of the crack can be detected with precision. The results are also checked quantitatively using nonlinear equations

Novel approaches for maintenance management on wind turbines.

The renewable energy industry is in a constant improvement in order to cover the current demands. Companies are competing to take advantage of any evolving opportunity presented. Nowadays one of those remarkable competitive advantages focuses on maintenance management and some terms such as operating and maintenance costs, availability, reliability, safety, lifetime, etc. emerge. Wind turbines (WT) are one of the fastest growing sources of renewable energy production. The number of WTs and their complexity has increased in recent years, reducing the reliability of systems and raising the maintenance costs due to the occurrence of non-monitored failures. There are case studies that present specific faults and consequent maintenance activities on WTs but they depend on the model considered, the geographic and environmental changes that occur in different wind farms, etc. Techniques such as condition monitoring (CM) are employed to detect and identify these failures/faults at earlier stages, maximising the productivity performance, minimising possible downtimes of the WT, and increasing the reliability, availability, maintainability and safety (RAMS) levels. CM is implemented from basic operations of the equipment to study. The system provides the condition, the state of a characteristic parameter that represents the health of the component(s) being monitored. Reliable data acquisition can be achieved with the optimal type and placement of sensors as well as employing the appropriate number of them. Conditioning also reduces the susceptibility to interferences during the features transport. Data processing, sorting and manipulation according to the objectives pursued, are usually performed by a digital signal processor. Then it can be shown via a screen display, stored or transmitted to another system. As part of some fault detection and diagnosis (FDD) approaches, features are extracted via CM. FDD is based on different methods employed to obtain the information needed from these features. For example, the most used technique for CM in WTs is vibration, while the most studied components are mechanical components such as gearboxes, blades or bearings. FDD relies on the number and type of sensors used and the processing and simplification methods employed to extract the information from the signals. Once information is obtained, an electronic measuring system provides the suitable data to an observer or other technical control systems. Therefore the three main block functions in a measurement system are data acquisition, data processing and data distribution. The information about the variables measured is turned into an electrical signal. The main advantages offered by these FDD systems are: The prediction, reduction and elimination of downtimes. The reduction of energy, maintenance and operating costs. The use of monitoring alert notifications

“Vibration-based tools for the optimisation of large-scale industrial wind turbines devices”

Wind turbines (WT) maintenance management must be in continuous improvement to develop reliability, availability, maintainability and safety (RAMS) programs, and to achieve time and cost reductions in large-scale industrial wind turbines. The optimisation of the operation reliability involves the supervisory control and data acquisition to guarantee these correct levels of RAMS. A fault detection and diagnosis methodology (FDD) is proposed for mechanical devices of a WT. The method applies the wavelet and Fourier analysis to vibration signals. The signals collected contain information on failures found in the gearbox-generator set. The information is initially tested by the fast Fourier transform (FFT) to ensure the accuracy of the information. Then, a pattern based on energies that relates each failure with different frequency bands is created. This pattern uses the wavelet transform as the main technique. A number of turbines of the same type were instrumented in the same wind farm. The data collected from the individual turbines was fused and analysed together in order to determine the overall performance. It is expected that data fusion allow a significant improvement since the information gained from various condition monitoring systems can be enhanced. Effort will also focus on the application of dependable embedded computer systems for a reliable implementation

Pattern recognition by wavelet transforms using macro fibre composites transducers

This paper presents a novel pattern recognition approach for a non-destructive test based on macro fibre composite transducers applied in pipes. A fault detection and diagnosis (FDD) method is employed to extract relevant information from ultrasound signals by wavelet decomposition technique. The wavelet transform is a powerful tool that reveals particular characteristics as trends or breakdown points. The FDD developed for the case study provides information about the temperatures on the surfaces of the pipe, leading to monitor faults associated with cracks, leaks or corrosion. This issue may not be noticeable when temperaturas are not subject to sudden changes, but it can cause structural problems in the medium and long-term. Furthermore, the case study is completed by a statistical method based on the coefficient of determination. The main purpose will be to predict future behaviours in order to set alarm levels as a part of a structural health monitoring system