Structural health monitoring of offshore wind turbines: A review through the Statistical Pattern Recognition Paradigm

Offshore Wind has become the most profitable renewable energy source due to the remarkable development it has experienced in Europe over the last decade. In this paper, a review of Structural Health Monitoring Systems (SHMS) for offshore wind turbines (OWT) has been carried out considering the topic as a Statistical Pattern Recognition problem. Therefore, each one of the stages of this paradigm has been reviewed focusing on OWT application. These stages are: Operational Evaluation; Data Acquisition, Normalization and Cleansing; Feature Extraction and Information Condensation; and Statistical Model Development. It is expected that optimizing each stage, SHMS can contribute to the development of efficient Condition-Based Maintenance Strategies. Optimizing this strategy will help reduce labor costs of OWTs׳ inspection, avoid unnecessary maintenance, identify design weaknesses before failure, improve the availability of power production while preventing wind turbines׳ overloading, therefore, maximizing the investments׳ return. In the forthcoming years, a growing interest in SHM technologies for OWT is expected, enhancing the potential of offshore wind farm deployments further offshore. Increasing efficiency in operational management will contribute towards achieving UK׳s 2020 and 2050 targets, through ultimately reducing the Levelised Cost of Energy (LCOE)

2012 PWST Workshop Summary

No abstract availabl

Remote online machine condition monitoring using advanced internet, wireless and mobile communication technologies

A conceptual model with wireless and mobile techniques is developed in this thesis for remote real-time condition monitoring, which is applied for monitoring, diagnosing, and controlling the working conditions of machines. The model has the following major functions: data acquisition, data processing, decision making, and remote communication. The data acquisition module is built up within this model using the sensory technique and data I/O interfaces to acquire the working conditions data of a machine and extract the physical information about the machine (e.g. failure, wear, etc.) for data processing and decision making. The data processing is conducted using digital conversion and feature extraction to process the received analogue condition data and convert the data into the physical quantities of working condition of the machine for sequent fault diagnosis. A real-time fault diagnostic scheme for decision-making is applied based on digital filtering and pattern classification to real-time identify the fault symptom of the machine and provide advice for decision making for maintenance. Process control is implemented to control the operation status of the machine automatically, inform the maintenance personnel diagnostic results and alert the working conditions of the machine. Remote communication with wireless and mobile features greatly advance the machine’s condition monitoring technology with real-time fault diagnostic capacity, by providing a wireless-based platform to enable the implementation of data acquisition, real-time fault diagnosis, and decision making through the Internet, wireless, and mobile phone network. The model integrating above techniques and methods has been applied into the following three areas: (1) Development of a Remote Real-time Condition Monitoring System of Industrial Gearbox, supported by the Stimulation Innovation Success programme (2007-2008); (2) Development of a Remote Control System of Solid Desiccant Dehumidifier for Air Conditioning in Low Carbon Emission Buildings, supported by the Sustainable Construction iNET programme (2009-2010); (3) Development of an Innovative Remote Monitoring System of Thermo-Electric-Generations, supported by the Sustainable Construction iNET programme (2010-2011). The combination of wireless and mobile techniques with data acquisition, real-time fault diagnosis, and decision-making, into a model for remote real-time condition monitoring is a novel contribution to this area

Monitoring Gearbox Using a Wireless Temperature Node Powered by Thermal Energy Harvesting Module

Condition monitoring (CM) of gearbox is a crucial activity due to its importance in power transmission for many industrial applications. Monitoring temperature is an effective mean to collect useful information about the healthy conditions of the gearbox. This study investigates the use of a novel wireless temperature node to monitor and diagnose different faults on a gearbox transmission system under different conditions. The wireless temperature node was fabricated with a novel feature that it is supplied by a thermoelectric generator module mounted on the gearbox house to be monitored so that the measurement system avoids the shortage of using a wired power sources or the requirement for recharging or changing batteries. Moreover, the temperatures from lubricating oils and housing are modelled empirically to implement a model based detection. The results show that this monitoring approach allows a number of common faults: tooth breakage, oil shortage, and shaft misalignment to be separated under different loads, which demonstrates the outstanding performance of the proposed system and thus suitable for online and automated condition monitoring

Remote machine condition monitoring based on power supply measurements

The most widely used rotating machines in the industry are three phase alternative current (AC) induction machines. With the advances in variable speed drive (VSD) technology, they have become even more reliable than their direct current (DC) counterpart. However, inevitably these motors soon begin to fail with time due to mechanical, electrical or thermal stress hence the need for condition monitoring (CM). Condition monitoring systems help keep machines running productively by detecting potential equipment failures before it actually fails. Many condition monitoring methods exist on the market including vibration monitoring; acoustic emission monitoring, thermal monitoring, chemical monitoring, current monitoring but most of these methods require additional sensors and expensive data acquisition system on top of a specialise software tool. This all increases the cost of ownership and maintenance. For more efficient monitoring of induction motor drive systems, this research investigates an innovative remote monitoring system using existing data available in AC drives based on AC motor operating process. This research uses standard automation components already present in most automated control systems. A remote data communication platform is developed, allowing access to the control data remotely over a wireless network and internet using PLC and SCADA system. Remote machine condition monitoring is not a new idea but its application to machine monitoring based on power supply parameters indirectly measured by an inverter is new. To evaluate the basic performance of the platform, the monitoring of shaft misalignment, a typical fault in mechanical system is investigated using an in-house gearbox test rig. It has resulted in a model based detection method based on different speed and load settings against the motor current feedback read by the inverter. The results have demonstrated that the platform is reliable and effective. In addition the monitoring method can be employed to detect and diagnose different degrees of misalignment in real time. This dissertation has major contributions to knowledge which includes: Understanding of real life machine condition monitoring problems for this application, including use of wireless sensor, communication over Industrial Ethernet and network security. The use of standard automation components (PLC and SCADA) in machine condition monitoring. MSc Research (Engineering) Thesis x An improved gearbox test rig platform which has the capability of remote control, acquiring and transferring data for monitoring induction machine drive system. The presented work shows that any machine using automated components such as PLC and SCADA and incorporating motor drive systems and other actuators has the potential to use the automated components for control, condition monitoring and reporting but this will require more tests to be done using the proposed platform