A fault detection method for railway point systems

Failures of railway point systems (RPSs) often lead to service delays or hazardous situations. A condition monitoring system can be used by railway infrastructure operators to detect the early signs of the deteriorated condition of RPSs and thereby prevent failures. This paper presents a methodology for early detection of the changes in the measurement of the current drawn by the motor of the point operating equipment (POE) of an RPS, which can be used to warn about a possible failure in the system. The proposed methodology uses the one-class support vector machine classification method with the similarity measure of edit distance with real penalties. The technique has been developed taking into account specific features of the data of infield RPSs and therefore is able to detect the changes in the measurements of the current of the POE with greater accuracy compared with the commonly used threshold-based technique. The data from infield RPSs, which relate to incipient failures of RPSs, were used after the deficiencies in the data labelling were removed using expert knowledge. In addition, possible improvements in the proposed methodology were identified in order for it to be used as an automatic online condition monitoring system

Multivariable Analysis for Advanced Analytics of Wind Turbine Management

Operation and maintenance tasks on the wind turbines have an essen- tial role to ensure the correct condition of the system and to minimize losses and increase the productivity. The condition monitoring systems installed on the main components of the wind turbines provide information about the tasks that should be carried out over the time. A novel statistical methodology for multivariable analysis of big data from wind turbines is presented in this paper. The objective is to analyse the necessary information from the condition monitoring systems installed in wind farms. The novel approach filters the main parameters from the collected signals and uses advanced computational techniques for evaluating the data and giving mean- ing to them. The main advantage of the approach is the possibility of the big data analysis based on the main information available

Optimal Management of Marine Inspection with Autonomous Underwater Vehicles

t New technologies and system communications are being applied in the industry, improving the efficiency and effectiveness. This paper is focused on novel technologies, software and materials that allow to explore deep ocean floor. Autonomous underwater vehicles require planning navigation models and algorithms. Sensors equipped in underwater vehicles allow to inspect and analyse inaccessible areas. Monitor and control measurement process is required to ensure suitable underwater operations. This paper presents a model using the main inspection process variables. The model calculates the field of view of the autonomous underwater vehicle to be determined according to the type of sensor, the orientation and the distance from the floor. This study aims at stabilising the fundaments to develop an autonomous route for the autonomous underwater vehicles and optimize its operation performance

Smart Farming: Intelligent Management Approach for Crop Inspection and Evaluation Employing Unmanned Aerial Vehicles

This work presents an unmanned aerial vehicle management platform encompassed in the concept of smart farming. Automates inspections of different crops and monitors the status of the plantation is done by IoT, analyzing an area on an online map that provides air and weather restrictions. Intelligent route management algorithms are employed to generate the optimal inspection route and waypoints, maximizing the multispectral images capture. These multispectral images can be subsequently processed according to algorithms based on phytosanitary index formulas and regressions obtained with artificial neural networks. Reports are generated with analysis of the results by this approach, for example: optimal collection time, water stress, maturity index, etc.Dirección General de Universidades, Investigación e Innovación of Castilla-La Mancha, under Research Grant (Ref.: SBPLY/19/180501/000102).No data JCR 20190.184 SJR (2019), Q3 242/393 Computer Science (miscellaneous)No data IDR 2019UE

Artificial Intelligence for Concentrated Solar Plant Maintenance Management

Concentrated Solar Power (CSP) is an alternative to the conventional energy sources which has had significant advances nowadays. A proper predictive maintenance program for the absorber pipes is required to detect defects in the tubes at an early stage, in order to reduce corrective maintenance costs and increase the reliability, availability, and safety of the concentrator solar plant. This paper presents a novel approach based on signal processing employing neuronal network to determine effectively the temperature of pipe, using only ultrasonic transducers. The main novelty presented in this paper is to determine the temperature of CSP without requiring additional sensors. This is achieved by using existing ultrasonic transducers which is mainly designed for inspection of the absorber tubes. It can also identify suddenly changes in the temperature of the CSP, e.g. due to faults such as corrosion, which generate hot spots close to welds

A Condition Monitoring System for Blades of Wind Turbine Maintenance Management

Wind energy is one of the most competitive and efficient renewable energy. It requires an efficient management system to reduce costs, predict failures and increase the production. The main objective of this paper is to design the appropriate tests and develop a condition monitoring system (CMS) to display the surface temperature of any body state using infrared radiation. The data obtained from this system lead to identify the state of the surface. The CMS is used for maintenance management of wind turbines because it is necessary an effective system to display the surface temperature to reduce the energy losses. This paper analyses numerous scenarios and experiments on different surfaces in preparation for actual measurements of blade surfaces

New Approaches on Maintenance Management for Wind Turbines Based on Acoustic Inspection

Nowadays, maintenance management is changing due to the new technologies in inspection and monitorization systems to reduce the production costs for the companies and risks for the operator. Maintenance management is a key factor in some industries as renewable energy, due to the high-cost consequences of a wrong failure detection in a wind turbine. Therefore, advances in condition monitoring systems are required for an early failure diagnosis. This paper contributes to the actual wind turbines diagnosis methods with a novel non-destructive inspection system based on acoustic analysis of the wind turbine condition. The paper presents a condition monitoring system based on an acoustic sensor embedded in an unmanned aerial vehicle to collect acoustic signals emitted by the wind turbine. The signals are sent to a ground remote-control centre, and then they are analysed. This data acquisition system needs of a qualitative and quantitative analysis to classify and identify the condition of the wind turbine. Wavelet transforms are employed for filtering the signals and pattern recognition. Several scenarios are considered and analysed considering the main mechanical parts and components of a wind turbine

Supervisory Control and Data Acquisition Analysis for Wind Turbine Maintenance Management

Wind energy is growing to become a competitive energy source. An efficient wind turbine maintenance management is required for ensuring the reliability of the energy production and the costs reduction. Supervisory control and data acquisition system provide information about the condition of the wind turbine by signals of the different subsystems and alarm activations in case of failure or malfunction. Due to the volume and variety of the data, operators require advanced analytics to control the performance of the wind turbines and the identification and prediction of failures. The novelty proposed in this work is based on statistical analysis for analyzing supervisory control and data acquisition data to optimize the use of the data in neural networks. The first phase is the alarm analysis, quantifying the critical alarms regarding on the number and time of activation. A filtering algorithm is developed for considering only interest periods with enough range to make the study. The second phase is based on the initial data treatment, classifying alarms and signals identifying the interest time periods. Neural network is defined and trained for evaluating the signal trends, with the aim of detecting the alarm activations cause. This information will be used in the maintenance management plan for programming maintenance tasks

Fuzzy Logic Applied to SCADA Systems

This article focuses on the monitoring of a wind farm in real time based on big data collected by Supervisory Control and Data Acquisition (SCADA) system. The decision- making of the type of maintenance to be applied can be insured by SCADA system. This system generates alarms based on the collected data. False alarms cause false interventions by the maintenance team resulting in loss of production and costs. The reduction of these false alarms makes it possible to contribute better to the management of the maintenance of the wind farm. In this paper, we propose a new approach for the identification of alarms by Fuzzy Logic based on the data collected by the SCADA system. The alarms generated in this case can be divided into two categories: orange alarms corresponding to faults requiring the intervention of preventive maintenance and red alarms corresponding to critical states that can cause system failures

SCADA and Artificial neural networks for maintenance management

Nowadays, the reliability of the wind turbines is essential to ensure the efficiency and the benefits of the wind energy. The SCADA system installed in a wind turbine generates lot of data that need to be processed. The information obtained from these data can be used for improving the operation and management, obtaining more reliable systems. The SCADA systems operate through different control rules that are predefined. However, a static control of the wind turbine can generate a miscorrelation between the control and the real conditions of the wind turbine. For example, two wind turbines can be separated several kilometers in the same wind farm, therefore, the operation conditions must be different and the control strategy should not be unique. This research work presents a method based on neural networks for a dynamic generation of the control strategy. The method suggests that the thresholds used for generating alarms can vary and, therefore, the control of the wind turbine will be adapted to each specific wind turbine