Overview of Remaining Useful Life prediction techniques in Through-life Engineering Services

Through-life Engineering Services (TES) are essential in the manufacture and servicing of complex engineering products. TES improves support services by providing prognosis of run-to-failure and time-to-failure on-demand data for better decision making. The concept of Remaining Useful Life (RUL) is utilised to predict life-span of components (of a service system) with the purpose of minimising catastrophic failure events in both manufacturing and service sectors. The purpose of this paper is to identify failure mechanisms and emphasise the failure events prediction approaches that can effectively reduce uncertainties. It will demonstrate the classification of techniques used in RUL prediction for optimisation of products’ future use based on current products in-service with regards to predictability, availability and reliability. It presents a mapping of degradation mechanisms against techniques for knowledge acquisition with the objective of presenting to designers and manufacturers ways to improve the life-span of components

Wind turbine generator condition-monitoring using temperature trend analysis

Condition monitoring can greatly reduce the maintenance cost for a wind turbine. In this paper, a new condition-monitoring method based on the nonlinear state estimate technique for a wind turbine generator is proposed. The technique is used to construct the normal behavior model of the electrical generator temperature. A new and improved memory matrix construction method is adopted to achieve better coverage of the generator's normal operational space. Generator incipient failure is indicated when the residuals between model estimates and the measured generator temperature become significant. Moving window averaging is used to detect statistically significant changes of the residual mean value and standard deviation in an effective manner; when these parameters exceed predefined thresholds, an incipient failure is flagged. Examples based on data from the Supervisory Control and Data Acquisition system at a wind farm located at Zhangjiakou in northern China have been used to validate the approach and examine its sensitivity to key factors that influence the performance of the approach. It is demonstrated that the technique can identify dangerous generator over temperature before damage has occurred that results in complete shutdown of the turbine

A data-driven framework for remaining useful life estimation

Remaining useful life (RUL) estimation is one of the most common tasks in the field of prognostics and structural health management. The aim of this research is to estimate the remaining useful life of an unspecified complex system using some data-driven approaches. The approaches are suitable for problems in which a data library of complete runs of a system is available. Given a non-complete  run of the system, the RUL can be predicted  using these approaches. Three main RUL prediction algorithms, which cover centralized data processing, decentralize data processing, and  in-between, are introduced and evaluated using the data of PHM’08 Challenge Problem. The methods involve the use of some other data processing techniques including wavelets denoise and similarity search. Experiment results show that all of the approaches  are effective in performing RUL prediction

Multivariate State Estimation Technique for Remaining Useful Life Prediction of Electronic Products

This paper investigates the use of Multivariate State Estimation Techniques as input in predicting the remaining useful life prediction of electronic products. A prognostics approach combining the Multivariate State Estimation Technique with life cycle damage prediction is then presented, along with a case study. The challenges of the approach are also discussed

PROGNOSTICS OF POLYMER POSITIVE TEMPERATURE COEFFICIENT RESETTABLE FUSES

Polymer positive-temperature-coefficient (PPTC) resettable fuse has been used to circuit-protection designs in computers, automotive circuits, telecommunication devices, and medical devices. PPTC resettable fuse can trip from low resistance to high resistance under over-current conditions. The increase in the resistance decreases the current and protects the circuit. After the abnormal current is removed, and/or power is switched off, the fuse resets to low resistance stage, and can be continuously operated in the circuit. The resettable fuse degrades with the operations resulting in loss or abnormal function of the protection of circuit. This thesis is focused on the prognostics methods for resettable fuses to provide an advance warning of failure and to predict the remaining useful life. The failure precursor parameters are determined first by systematic analysis using failure modes, mechanisms, and effects analysis (FMMEA) followed by a series of experiments to verify these parameters. Then the causes of the observed failures are determined by failure analyses, including the analyses of interconnections between different parts, the microstructures of the polymer composite, the properties (such as crystallinity) of the polymer composite, and the coefficient of thermal expansion (CTE) of different parts. The revealed failure causes include the cracks and gaps between different parts, the agglomerations of the carbon black particles, the change in crystallinity of the polymer composite, and the CTE-mismatches between different parts. Cross validation (CV) sequential probability ratio test (CVSPRT) is developed to detect anomalies. CV methods are introduced into SPRT to determine the model parameters without the need of experience and reduce the false and missed alarms. A moving window training updating based dynamic model parameter optimization (MW-DMPO) n-steps-ahead prognostics method is developed to predict the failure. MW methods update the training data for prediction models by a moving window to contain the latest degradation information/data and improve the prediction accuracy. For each updating of the training data, the model parameters for data-trending model are updated dynamically. Based on the developed MW-DMPO method, a MW cross validation support vector regression (MW-CVSVR) n-steps-ahead prediction is developed to predict failures of PPTC resettable fuses in this thesis. The cross validation method is used to determine the proper SVR model parameters. The CVSPRT anomaly detection method and MW-DMPO n-steps-ahead prognostics method developed in this thesis can be extended as general methods for anomaly detection and failure prediction

On-line health monitoring of passive electronic components using digitally controlled power converter

This thesis presents System Identification based On-Line Health Monitoring to analyse the dynamic behaviour of the Switch-Mode Power Converter (SMPC), detect, and diagnose anomalies in passive electronic components. The anomaly detection in this research is determined by examining the change in passive component values due to degradation. Degradation, which is a long-term process, however, is characterised by inserting different component values in the power converter. The novel health-monitoring capability enables accurate detection of passive electronic components despite component variations and uncertainties and is valid for different topologies of the switch-mode power converter. The need for a novel on-line health-monitoring capability is driven by the need to improve unscheduled in-service, logistics, and engineering costs, including the requirement of Integrated Vehicle Health Management (IVHM) for electronic systems and components. The detection and diagnosis of degradations and failures within power converters is of great importance for aircraft electronic manufacturers, such as Thales, where component failures result in equipment downtime and large maintenance costs. The fact that existing techniques, including built-in-self test, use of dedicated sensors, physics-of-failure, and data-driven based health-monitoring, have yet to deliver extensive application in IVHM, provides the motivation for this research ... [cont.]

Gaussian process models for SCADA data based wind turbine performance/condition monitoring

Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring.Wind energy has seen remarkable growth in the past decade, and installed wind turbine capacity is increasing significantly every year around the globe. The presence of an excellent offshore wind resource and the need to reduce carbon emissions from electricity generation are driving policy to increase offshore wind generation capacity in UK waters. Logistic and transport issues make offshore maintenance costlier than onshore and availability correspondingly lower, and as a result, there is a growing interest in wind turbine condition monitoring allowing condition based, rather than corrective or scheduled, maintenance.;Offshore wind turbine manufacturers are constantly increasing the rated size the turbines, and also their hub height in order to access higher wind speeds with lower turbulence. However, such scaling up leads to significant increments in terms of materials for both tower structure and foundations, and also costs required for transportation, installation, and maintenance. Wind turbines are costly affairs that comprise several complex systems connected altogether (e.g., hub, drive shaft, gearbox, generator, yaw system, electric drive and so on).;The unexpected failure of these components can cause significant machine unavailability and/or damage to other components. This ultimately increases the operation and maintenance (O&M) cost and subsequently cost of energy (COE). Therefore, identifying faults at an early stage before catastrophic damage occurs is the primary objective associated with wind turbine condition monitoring.;Existing wind turbine condition monitoring strategies, for example, vibration signal analysis and oil debris detection, require costly sensors. The additional costs can be significant depending upon the number of wind turbines typically deployed in offshore wind farms and also, costly expertise is generally required to interpret the results. By contrast, Supervisory Control and Data Acquisition (SCADA) data analysis based condition monitoring could underpin condition based maintenance with little or no additional cost to the wind farm operator.;A Gaussian process (GP) is a stochastic, nonlinear and nonparametric model whose distribution function is the joint distribution of a collection of random variables; it is widely suitable for classification and regression problems. GP is a machine learning algorithm that uses a measure of similarity between subsequent data points (via covariance functions) to fit and or estimate the future value from a training dataset. GP models have been applied to numerous multivariate and multi-task problems including spatial and spatiotemporal contexts.;Furthermore, GP models have been applied to electricity price and residential probabilistic load forecasting, solar power forecasting. However, the application of GPs to wind turbine condition monitoring has to date been limited and not much explored.;This thesis focuses on GP based wind turbine condition monitoring that utilises data from SCADA systems exclusively. The selection of the covariance function greatly influences GP model accuracy. A comparative analysis of different covariance functions for GP models is presented with an in-depth analysis of popularly used stationary covariance functions. Based on this analysis, a suitable covariance function is selected for constructing a GP model-based fault detection algorithm for wind turbine condition monitoring.;By comparing incoming operational SCADA data, effective component condition indicators can be derived where the reference model is based on SCADA data from a healthy turbine constructed and compared against incoming data from a faulty turbine. In this thesis, a GP algorithm is constructed with suitable covariance function to detect incipient turbine operational faults or failures before they result in catastrophic damage so that preventative maintenance can be scheduled in a timely manner.;In order to judge GP model effectiveness, two other methods, based on binning, have been tested and compared with the GP based algorithm. This thesis also considers a range of critical turbine parameters and their impact on the GP fault detection algorithm.;Power is well known to be influenced by air density, and this is reflected in the IEC Standard air density correction procedure. Hence, the proper selection of an air density correction approach can improve the power curve model. This thesis addresses this, explores the different types of air density correction approach, and suggests the best way to incorporate these in the GP models to improve accuracy and reduce uncertainty.;Finally, a SCADA data based fault detection algorithm is constructed to detect failures caused by the yaw misalignment. Two fault detection algorithms based on IEC binning methods (widely used within the wind industry) are developed to assess the performance of the GP based fault detection algorithm in terms of their capability to detect in advance (and by how much) signs of failure, and also their false positive rate by making use of extensive SCADA data and turbine fault and repair logs.;GP models are robust in identifying early anomalies/failures that cause the wind turbine to underperform. This early detection is helpful in preventing machines to reach the catastrophic stage and allow enough time to undertake scheduled maintenance, which ultimately reduces the O&M, cost and maximises the power performance of wind turbines. Overall, results demonstrate the effectiveness of the GP algorithm in improving the performance of wind turbines through condition monitoring

Diagnostic and prognostic analysis tools for monitoring degradation in aged structures

This research addresses the problem of prolonging the life of aged structures of historical value that have already outlived their original designed lives many times. While a lot of research has been carried out in the field of structural monitoring, diagnostics and prognostics for high tech industries, this is not the case for historical aged structures. Currently most maintenance projects for aged structures have focused on the instrumentation and diagnostic techniques required to detect any damage with a certain degree of success. This research project involved the development of diagnostic and prognostic tools to be used for monitoring and predicting the ‘health’ of aged structures. The diagnostic and prognostic tools have been developed for the monitoring of Cutty Sark iron structures as a first application. The concept of canary and parrot sensor devices are developed where canary devices are small, accelerated devices, which will fail according to similar failure mechanisms occurring in an aged structures and parrot devices are designed to fail at the same rate as the structure, thus mimicking the structure. The model-driven prognostic tool uses a Physics-of-Failure (PoF) model to predict remaining life of a structure. It uses a corrosion model based on the decrease in corrosion rate over time to predict remaining life of an aged iron structures. The data-driven diagnostic tool developed uses Mahalanobis Distance analysis to detect anomalies in the behaviour of a structure. Bayesian Network models are then used as a fusion method, integrating remaining life predictions from the model-driven prognostic tool with information of possible anomalies from data-driven diagnostic tool to provide a probability distribution of predicted remaining life. The diagnostics and prognostic tools are validated and tested through demonstration example and experimental tests. This research primarily looks at applying diagnostic and prognostic technologies used in high-tech industries to aged iron structures. In order to achieve this, the model-driven and data-driven techniques commonly used had to be adapted taking into consideration the particular constraints of monitoring and maintaining aged structures. The fusion technique developed is a novel approach for prognostics for aged structures and provides the flexibility often needed for diagnostic and prognostic tools

Intelligent load management for on-board aircraft generator systems

This thesis describes research that has been undertaken to develop an Intelligent Load Management system that assists in overheat protection of on-board aircraft generators; both under normal operation and under fault conditions. There is an ongoing demand to optimise military aircraft performance by decreasing weight, operating costs and gas emissions, whilst increasing overall reliability. Recently, a move towards a more electric aircraft has become increasingly popular to address these demands. Flight critical systems such as: cabin pressure, flight control, surface actuation, landing gear, breaking, etc. which were conventionally controlled by pneumatic or hydraulic systems, are now included in a wider electrical network. This conversion to electrical systems increases the need for constant and uninterrupted provision of power. Generators are limited by the amount of power they can provide. An excess demand of current for a prolonged period of time can lead to overheating, which in turn, can lead catastrophic failure due to insulation degradation. The state of the art overheat protection method is using a thermo-mechanical fuse. In case of overheat the generator trips offline, with only essential systems remaining operable, in order to prevent further damage. The proposed alternative is to produce an “intelligent fuse” where models, knowledge of the mission profile, and temperature measurements are combined to predict future temperatures and manage the loading of the generators. A lab-based AC generator system was used as the main plant for this research. Based on that generator, a lumped parameter thermo-electric model was derived. It was further used as a simulation tool for faults and as a surrogate for a generator when multiple generators exist in the same system. This approach provided a high fit (<97%) in scenarios that had been advised by BAE Systems for all relevant temperatures. A large part of the predictive methods used, revolved around using linear models. Both white box and black box approaches were assessed; with autoregressive exogenous models (ARX) providing the best performance for estimating the temperatures of sensitive parts of the generator using the information of measured currents. In order to accommodate for faults during the mission, adaptive models were created. They considered variations of measured currents and measured temperatures to more accurately estimate future temperatures. These models either took the form of ARX or neural networks. Each provided their distinct advantages; with neural networks achieving more accurate prediction with high prediction horizons, whilst ARX being more robust throughout. This made ARX the preferred candidate for this application. To ensure appropriate load management, both open loop and closed loop techniques were explored. Both neural networks open loop and closed control loops provided suitable solutions, with the control loops providing the least amount of performance compromise when no load management was necessary. These ideas were extended to multi-generator systems. They were tested in simulation scenarios and in a hardware in the loop scenario, where the relative efficiency gain of having ILM in the system was subsequently assessed. ILM is also validated and tested on a BAE System aircraft generator in the Brough test facilities. Tests were run both under normal conditions and after a fault was introduced to the machine. The adaptive models provided an overall fit of at least 90% for the relevant temperatures under all conditions

A framework development to predict remaining useful life of a gas turbine mechanical component

Power-by-the-hour is a performance based offering for delivering outstanding service to operators of civil aviation aircraft. Operators need to guarantee to minimise downtime, reduce service cost and ensure value for money which requires an innovative advanced technology for predictive maintenance. Predictability, availability and reliability of the engine offers better service for operators, and the need to estimate the expected component failure prior to failure occurrence requires a proactive approach to predict the remaining useful life of components within an assembly. This research offers a framework for component remaining useful life prediction using assembly level data. The thesis presents a critical analysis on literature identifying the Weibull method, statistical technique and data-driven methodology relating to remaining useful life prediction, which are used in this research. The AS-IS practice captures relevant information based on the investigation conducted in the aerospace industry. The analysis of maintenance cycles relates to the examination of high-level events for engine availability, whereby more communications with industry showcase a through-life performance timeline visualisation. Overhaul sequence and activities are presented to gain insights of the timeline visualisation. The thesis covers the framework development and application to gas turbine single stage assembly, repair and replacement of components in single stage assembly, and multiple stage assembly. The framework is demonstrated in aerospace engines and power generation engines. The framework developed enables and supports domain experts to quickly respond to, and prepare for maintenance and on-time delivery of spare parts. The results of the framework show the probability of failure based on a pair of error values using the corresponding Scale and Shape parameters. The probability of failure is transformed into the remaining useful life depicting a typical Weibull distribution. The resulting Weibull curves developed with three scenarios of the case shows there are components renewals, therefore, the remaining useful life of the components are established. The framework is validated and verified through a case study with three scenarios and also through expert judgement