Early Warning Of Failure Research Articles

Wind Turbines Condition Monitoring and Fault Identification using Acoustic Emission Testing Method

Acoustic Emission (AE) technique can successfully applied for condition monitoring of low speed rotating components such as rolling bearing and gearbox of Wind Turbines. This technique is able to detect very small energy released rates from incipient defect in a very early stage. Wide range of signal processing methods can be apply for diagnosing faults and fatigues in AE spectrums and the changes in wave forms are very significant to recognize the failures. Condition monitoring and Fault identification (CMFI) of wind turbine health using automated failure detection algorithms can improve turbine reliability. AE testing is based condition monitoring system uses data already collected at the wind turbine controller. It is an effective way to monitor wind turbines for early warning of failures and performance issues. CMFI methods are classified into modelbased and signal-based methods. They can be implemented with or without the use of artificial intelligence. The object of this thesis is to design a model-based CMFI scheme for WTs, which can be used under normal operation conditions.

A return on investment analysis of applying health monitoring to LED lighting systems

LED lighting systems have become desirable because of their environmental and energy-saving advantages. The lack of information regarding LED reliability is a barrier to the further expansion of LED use, especially in large-scale applications such as street lighting and traffic lights, and safety-related applications such as automotive headlights. Prognostics and health management (PHM) techniques can be utilized to provide LED reliability information to remove this barrier. However, the return on investment (ROI) for LED lighting systems has been of concern. To reduce life cycle cost, a PHM maintenance approach with system health monitoring (SHM) is considered as a means of providing early warning of failure, reducing unscheduled maintenance events, and extending the time interval of maintenance cycles. This paper presents the ROI from a PHM maintenance approach with SHM in LED lighting systems compared with the unscheduled maintenance approach based on different exponential and normal failure distributions. Three different exponential distributions with 10%, 20%, and 30% failure rates were used to investigate how ROI changes with different failure rates. For each failure rate, the mean times to failure (MTTFs) were 41,000h, 20,500h, and 13,667h, respectively. Three normal failure distributions with the same MTTFs as those of the exponential distributions were utilized to compare the results with the exponential distributions. ROI results showed that the PHM maintenance approach with SHM is required for cost savings in the exponential failure distributions. In case of the normal distributions, the PHM maintenance approach with SHM shows ROI benefits when MTTFs are less than 30,000h. The PHM maintenance approach with SHM needs to be considered in industrial applications based on the reliability of LED lighting systems to maximize the ROI benefit when the total life cycle cost of the system employing the unscheduled maintenance is greater than the total life cycle cost of the system employing the PHM maintenance approach with SHM.

Research of Acoustic Emission Testing Method with Application to Monitored for Wind Turbines

Acoustic Emission method of nondestructive check is based on exertion wave radiation and their registration during fast local material structure reorganization. It is used as a means of analysis of materials, constructions, productions control and diagnosis during operating time. In the article, it is applied to structural health monitoring of Wind Turbines. Acoustic emission testing is based condition monitoring system uses data already collected at the wind turbine controller. It is an effective way to monitor wind turbines for early warning of failures and performance issues. We used a number of measurements to develop anomaly detection algorithms and investigated classification techniques using clustering algorithms and principal components analysis for capturing fault signatures. When registering signal amplitude it is required to consider its frequency distribution connecting each amplitude rate with the corresponding vibration rate. The correlation and signal to noise ratio were evaluated by the algorithm based on frequency spectrum. Classical method of frequency distortion influence exclusion consists of FRF calculation with subsequent adjustment of received signals spectral characteristics.

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

<p>Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.</p>

Structural health monitoring for wind turbine foundations

The construction of onshore wind turbines has been increasing rapidly as the UK attempts to meet its renewable energy targets. As the UK’s future energy depends increasingly on wind farms, safety and security are critical to the success of this renewable energy source. Structural integrity of the tower and its components is a critical element of this security of supply. With the stochastic nature of the load regime, a bespoke structural health monitoring system is required to monitor the integrity of the concrete foundations supporting the tower. This paper presents an assessment of ‘embedded can’-style foundation failure modes in large onshore wind turbines and proposes a novel condition-based monitoring solution to aid in early warning of failure.

Online Anomaly Detection for Hard Disk Drives Based on Mahalanobis Distance

A hard disk drive (HDD) failure may cause serious data loss and catastrophic consequences. Online health monitoring provides information about the degradation trend of the HDD, and hence the early warning of failures, which gives us a chance to save the data. This paper developed an approach for HDD anomaly detection using Mahalanobis distance (MD). Critical parameters were selected using failure modes, mechanisms, and effects analysis (FMMEA), and the minimum redundancy maximum relevance (mRMR) method. A self-monitoring, analysis, and reporting technology (SMART) data set is used to evaluate the performance of the developed approach. The result shows that about 67% of the anomalies of failed drives can be detected with zero false alarm rate, and most of them can provide users with at least 20 hours during which to backup the data.

Cost Optimization for Canary-Equipped Electronic Systems in Terms of Inventory Control and Maintenance Decisions

For electronic systems equipped with early warning devices known as canaries, the early warning of failure can provide the opportunity for better spare-parts ordering, timely replacement of parts, and a resulting reduction in system life cycle costs. This paper presents both theoretical and simulation-based models for spare-part ordering and system replacement decisions for systems equipped with canaries. We first develop a theoretical model for the expected total cost per unit time in terms of the order interval, and maximum allowable stock level. This model is then compared with a simulation algorithm to validate the model. On the basis of the simulation algorithm, at each ordering point, we remove the constraint of a maximum allowable stock level, and order a quantity proportional to the predicted replacement quantity based on the expected number of canary warnings over the next order period. We then further expand the model to consider the risk (after a canary warning) with respect to the expected cost for delaying the replacement until the next order arrival point if a spare is not available, and then decide whether to replace the system immediately or wait. Finally, we consider the case where a preventive maintenance plan is in place. The comparison using numerical examples among all developed models and algorithms shows that the expected total operating cost in terms of inventory and maintenance is lowered significantly when the canary is used, and particularly when the risk analysis is implemented.

Anomaly Detection of Polymer Resettable Circuit Protection Devices

As circuit protection devices, failure or abnormal behavior of polymer positive-temperature-coefficient resettable devices can cause damage to circuits. It is necessary to detect anomalies in the resettable circuit protection devices to provide early warning of failure and avoid damage to a circuit. In this paper, a novel anomaly detection method, the cross-validation-based sequential probability ratio test, is developed and applied to the failure precursor parameters of the resettable circuit protection devices to conduct anomaly detection. The cross-validation-based sequential probability ratio test integrates the advantages of both the sequential probability ratio test for in situ anomaly detection and the cross-validation technique for model parameter selection to reduce the probability of false and missed alarms in anomaly detection. The cross-validation-based sequential probability ratio test solves the model parameter selection difficulty of the traditional sequential probability ratio test and improves its performance in anomaly detection.

Research on Reliability of Offshore Wind Turbine

This paper is concerned with the enhancement of the Capacity Factor for wind turbines, especially offshore. In particular the work describes analysis of data on a wind turbine drive train to enhance the reliability of components, reduce maintenance time and provide early warning of failures from the understanding of mechanical dynamics.

On track to a remote conditioned future

The city of Liverpool is served by its own underground system, operated by Serco Ned under the name of Merseyrail. Network Rail owns and maintains the operational infrastructure comprising track, signalling, power distribution and plant assets. The particular environment at the centre of the system (underground and inaccessible) was an ideal proving ground to install a remote condition monitoring system. Early warning of failure and the switching of various pieces of plant by remote control have allowed significant improvements to safety and operational performance of the rail network.

The benefits of system insight

As more and more industries become interested in the equipment life cycle and whole process costs, pump monitoring is emerging as an important consideration. Whether it be to monitor for early warning of failure, for optimization of routine maintenance schedules or as part of an integrated system for process optimization, Paul Cross argues that the more you know about the operation of your pump, the better you can run it.

Early Failure Warning Systems For Railway Signalling Mechanical Equipment: 1 - Train Stops

The scope and motivation of the paper relates to prediction and early detection, by means of active condition monitoring, of possible right-side failures in railway signalling mechanical equipment which would otherwise cause delay. Train-stops have been considered in the first instance. Modern condition monitoring techniques provide early warning signals. They can complement and direct the maintenance work of permanent way engineers in order to make efficient use of resources, save costs associated with too frequent maintenance routines and increase the quality of service by reducing delays. Train-stops are firstly described and their operation explained. Current maintenance routines are briefly outlined and the most common fault modes and processes which cause them are discussed. The paper follows with a survey of various fault prediction and condition monitoring techniques in other industries and analyses their possible usage in this particular application. Results from the application of two such techniques on a laboratory test rig are presented.