An SVM-based solution for fault detection in wind turbines

Research into fault diagnosis in machines with a wide range of variable loads and speeds, such as wind turbines, is of great industrial interest. Analysis of the power signals emitted by wind turbines for the diagnosis of mechanical faults in their mechanical transmission chain is insufficient. A successful diagnosis requires the inclusion of accelerometers to evaluate vibrations. This work presents a multi-sensory system for fault diagnosis in wind turbines, combined with a data-mining solution for the classification of the operational state of the turbine. The selected sensors are accelerometers, in which vibration signals are processed using angular resampling techniques and electrical, torque and speed measurements. Support vector machines (SVMs) are selected for the classification task, including two traditional and two promising new kernels. This multi-sensory system has been validated on a test-bed that simulates the real conditions of wind turbines with two fault typologies: misalignment and imbalance. Comparison of SVM performance with the results of artificial neural networks (ANNs) shows that linear kernel SVM outperforms other kernels and ANNs in terms of accuracy, training and tuning times. The suitability and superior performance of linear SVM is also experimentally analyzed, to conclude that this data acquisition technique generates linearly separable datasets.Projects, CENIT-2008-1028, TIN2011-24046, IPT-2011-1265-020000 and DPI2009-06124-E/DPI of the Spanish Ministry of Economy and Competitivenes

A vrtual sensor for online fault detection of multitooth-tools

The installation of suitable sensors close to the tool tip on milling centres is not possible in industrial environments. It is therefore necessary to design virtual sensors for these machines to perform online fault detection in many industrial tasks. This paper presents a virtual sensor for online fault detection of multitooth tools based on a Bayesian classifier. The device that performs this task applies mathematical models that function in conjunction with physical sensors. Only two experimental variables are collected from the milling centre that performs the machining operations: the electrical power consumption of the feed drive and the time required for machining each workpiece. The task of achieving reliable signals from a milling process is especially complex when multitooth tools are used, because each kind of cutting insert in the milling centre only works on each workpiece during a certain time window. Great effort has gone into designing a robust virtual sensor that can avoid re-calibration due to, e.g., maintenance operations. The virtual sensor developed as a result of this research is successfully validated under real conditions on a milling centre used for the mass production of automobile engine crankshafts. Recognition accuracy, calculated with a k-fold cross validation, had on average 0.957 of true positives and 0.986 of true negatives. Moreover, measured accuracy was 98%, which suggests that the virtual sensor correctly identifies new cases.Red de Supervision y Diagnosis de Sistemas Complejos (DPI2009-06124-E) of the Spanish Ministry of Science and Innovatio

Report about analyzed and clustered factory typologies. Deliverable D1.1

Deliverable D1.1 provides a detailed study of the current status of manufacturing in the three sectors to which the three REEMAIN demo sites belong: biscuits manufacturing, iron casting and denim textile. Besides, D1.1 contains also an analysis on the applicability of innovative energy and resource efficient technologies to the three manufacturing sectors, and a review of the most innovative practices put in place by the most advanced companies in the field. Finally, the deliverable contains a brief survey of techniques for mapping resource flows, that will be used throughout the implementation of the REEMAIN project. The description of the three manufacturing processes (Chapters 2, 3 and 4) thoroughly describes the different manufacturing process steps according to the state of the art, and it includes also the most common options/alternatives for technological solutions in the different sectors. In addition (Chapter 5), an analysis of energy (electricity, gas) and resource (water, raw materials) consumption is provided for each process. In Chapter 6, a set of energy-related technologies, mostly drawn from those identified in Task 3.1 as “highly interesting” within the Technology Roadmap for Efficient Manufacturing, have been assessed with regards to their applicability to the three sectors, at large, that REEMAIN demo sites belong to: bakery, iron & steel and textile manufacturing. These technologies include Solar Cooling, Solar Thermal Collectors, Solar Concentrators, Solar PV, ORC, CHPC, Hot Water Storage and Electrochemical Storage. Finally, on one hand, Chapter 7 investigates the most advanced technical solutions available for biscuits manufacturing, iron casting and denim textile that are already setting the path for resource and energy efficient manufacturing. On the other hand, Chapter 8 describes the main techniques and conceptual approaches available for mapping resource flows, and thus to identify gaps and rooms for improvements into existing manufacturing processes, and to assess the effective progress granted by the introduction of technological innovations into the different process steps