Blowin' in the wind....risk based inspection of offshore turbines
TWI Bulletin, March - April 2009
Offshore wind farm managers face increasing pressure to minimise life cycle costsbut maintain reliability and safety.
Chris Ablitt is the manager of the Asset Integrity section at TWI and is responsible for management of ageing asset activities within TWI, taking a leading role in promoting and developing AIM services throughout the UK and international offices. Chris is responsible for both Fitness for Service (FFS) and Risk Based Inspection (RBI)assessment projects for pressure systems in the process industries and energy sector. Taking a leading role in the planning, execution and optimisation of inspection, assessment, monitoring and repair strategies for ageing and newplant.
Ujjwal Bharadwaj is a Research Engineer based at TWI in Cambridge. He is pursuing a doctorate inrisk based assessment of structures and equipment. His research interests within the remit of risk management include the application of optimization, probabilistic assessment, simulation and other decision making techniques to solveproblems in industry. After completing his basic degree in Electrical and Electronics engineering, he served in industry in the maintenance sector. He then studied Risk Management at the London School of Economics at the Master's levelbefore taking up his engineering doctorate.
TWI has been involved in Risk Based Inspection (RBI) for the last 10 years, providing consultancy, training and software to its Industrial Members throughout the world. As Chris Ablitt and Ujjwal Bharadwaj explain,in addition to covering process plant, pipelines, tanks and power plant, TWI has recently expanded the methodology to cover wind turbine farms. This article covers an introduction to RBI and how it can be applied to wind farms.
In recent years, increased competition across all industries has driven the need for cost effective approaches to asset inspection and maintenance planning. Several industries operating high integrity structures and equipment areplanning in-service inspection on the basis of the information gained from an analysis of the risk of failure. On this basis, optimised inspection frequencies and methods relating to the likelihood of failure and scale of consequencecan be generated for each individual item of equipment, thus focusing resources on high risk items.
In summary, RBI is a structured approach to optimise inspection and maintenance planning.
In the context of safety as well as economics, risk is a combination of the probability of occurrence of a hazardous or detrimental event and the magnitude of the consequences of the event. Risk is defined by three components:
- The event.
- The probability of the event occurring.
- The undesirable consequences.
Risk based inspection uses an analysis of the risk of failure for the development of a management plan. The risk analysis identifies all credible types and causes of structural failure and assesses the rate of degradation inrelation to future fitness-for-service (FFS). This information is used to formulate what, where, when and how to inspect. Feedback from inspection back into the risk analysis and future planning is an essential part of the perpetualRBI process.
The management of inspection and maintenance expenditure can be particularly acute for plant in the final part of its life cycle. The classic bathtub curve, shown in Figure 1, (failure rate of a component or a system versustime) reveals three distinct phases in the lifetime of a system.
Fig.1. Failure rate of plant components versus age During the initial stage, the Infant Mortality stage, there are 'teething' problems causing the failure rate to be high. The rate then falls as these problems - design, manufacturing defects, etc are identified and solved.In the second stage, which is the Useful Life stage, standard maintenance practices keep the failure rate almost constant. However, intrinsic risk from events such as national disasters, arson and sabotage mean risk is nevereliminated. During the third and final stage of the plant life - the Ageing stage, the failure rate rises mainly due to damage accumulated by ageing. At some point during this stage, high failure rates require operators to considerreplacing the plant. In practice, there are usually a number of such pieces of equipment reaching this stage at once and a limited budget is made available to decision makers. TWI has developed a risk-based methodology to identify theoptimum time of replacement or repair of equipment, given multiple equipment and limited budgetary support. The background to the methodology and the process is described in the following sections.
Risk
Risk has numerous definitions depending upon its use. Risk is a combination of the probability of an event and its consequence. It is a deviation from the normal or expected. Numerically, it is a product of probability of an eventoccurring and the consequence of the event.
The risk-based approach to maintenance
A risk-based approach considers failure in both its dimensions, taking cognisance of the two elements that constitute risk - the probability (or likelihood) of failure and the consequence of that failure. Figure 2 shows thetwo dimensional risk profiles of the components in an offshore wind farm henceforth referred to as the plant.
Fig.2. Idealised risk plot of several components within a plant The probabilities and the consequences of failure of ten components have been determined and presented as points on a Risk Plot. An iso-risk line is also plotted representing a constant risk level as defined by the operatoraccording to their perception of what is an acceptable threshold level of risk. The iso-risk line separates acceptable risk components from the unacceptable risk components, enabling plant managers to focus maintenance resources on therelatively more risky components.
TWI has developed a unique time dimension to the determination of risk. The TWI methodology is able to calculate a risk-based safe operating period based on an analysis of the rate of change of the likelihood of failure. Theiso-risk line is therefore replaced with a target run-length.
Risk analysis methods
Risk analysis is the systematic use of information to identify sources of risk, and to estimate the risk of failure. The information used in risk analysis includes historical data, theoretical analysis, informed opinions andstakeholder concerns.
Risk analysis methods are generally categorised as qualitative or quantitative. There may be an intermediate category (semi-quantitative) depending upon how quantitative the risk analysis is. The American Petroleum Institute'sRecommended Practice 580 on risk-based inspection describes a 'continuum of approaches' ranging from the qualitative to quantitative, see Figure 3. The figure depicts the level of detail in risk analysis corresponding to apurely qualitative approach on one end of the spectrum, to the purely quantitative one on the other, with intermediate approaches in between.
Fig.3. Continuum of risk analysis methods Qualitative analysis
This method uses engineering judgement and experience as the basis for risk analysis. The results of the analysis largely depend on the expertise of the user. The primary advantage of qualitative risk analysis is that it enablesassessment in the absence of detailed numerical data. It is also the first pragmatic step to conduct a quantitative risk analysis by screening out components of less concern. Moreover, the results can serve as a reality check on theoutcome of quantitative analysis. However, it is not a very detailed analysis and provides only a broad categorisation of risk. Failure Modes, Effects and Criticality Analysis (FMECA), Hazard and Operability Studies (HAZOPS), and theRisk Matrix approach are examples of qualitative risk analysis. In the Risk Matrix approach, the likelihoods and consequences of failure are qualitatively described in broad ranges (eg high, medium or low). Figure 4shows the risk profiles of selected components of a wind farm. The risk profiles are for demonstration only: in practice, the profiling is done by involving plant experts.
Fig.4. Qualitative risk analysis using a risk matrix Quantitative analysis
Qualitative risk assessments become less discerning when the system complexity increases, so quantitative analysis is usually required for the risk discrimination of a system of components. Quantitative Analysis assigns numericalvalues to the probability (eg 10-5 failure events per year) and the consequences of failure (eg inventory released over 1,100m2). Qualitative analysis techniques such asFMECA and HAZOPS can become quantitative when the values of failure consequence and failure probability are numerically estimated. The numeric values can be determined from a variety of references such as generic failure databases,elicited expert opinion, or calculated by specific engineering and statistical analysis. There are statistical methods for combining data from various sources or updating data with additional information.
In the current discussion, it is assumed that the structure of a wind turbine tower is of critical importance, as highlighted by the qualitative analysis in the previous section.
For the quantitative risk analysis method for wind farms, a failure frequency versus time curve, for the Ageing period of life is developed by engineering analysis of the structure for the active or potentially active in-servicedamage mechanisms, eg corrosion of the turbine tower. The consequence of failure is in financial terms. For complex systems, event tree analysis is usually undertaken to determine the effect the particular component has on thesystem, to thereby resolve the individual cost of consequence of the component's failure.
Risk based optimisation
The next step is the calculation of the optimum action schedule or date, of the run-repair-replace action. This calculation weighs the financial benefits of maintenance action against the risk (as expressed in costs) of not takingthe action. The ultimate aim is to maximise the net present value of the investment (ie the maintenance action) by adjusting the date of the action.
Conclusions
Risk-based maintenance optimisation requires a detailed analysis using quantitative techniques. The proposed methodology uses engineering analysis by developing a basic probabilistic damage mechanism model to obtain failure rates.The resulting failure rates over time, are used to calculate expected present values of cash flows before and after selected maintenance actions (eg equipment replacement).
It has been shown that the optimum year of replacement can be calculated when the net present value (NPV) of the maintenance action is maximised. If there is a budgetary constraint that does not allow for a series of actions in asystem of structures to be undertaken in a given strategic planning period, multi-component optimisation can be easily undertaken using the approach.
Future work will focus on the derivation of failure rates using expert elicitation, as well as the combination of failure probabilities using Bayesian methods to update prior probability distributions with other data sources, eg generic failure databases with expert knowledge. The optimisation method currently used in the method will also be developed by exploring the practicalities of using off-the-shelf genetic algorithm solvers. Finally, furtherwork will be undertaken to incorporate a wider range of in-service equipment damage mechanisms, as well as previous in-service inspection data, to optimise future inspection plans (ie coverage and schedule).
The methodology has been captured and automated in its semi-qualitative form in a piece of TWI software called RISKWISE for Wind. Please contact chris.ablitt@twi.co.uk for moreinformation.
