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Prognostic and Health Management (PHM) has been widely applied to hardware systems in the electronics and non-electronics domains but has not been explored for software. While software does not decay over time, it can degrade over release cycles. Software health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental. Relevant research areas such as software defect prediction, software reliability prediction, predictive maintenance of software, software degradation, and software performance prediction, exist, but all of these represent diagnostic models built upon historical data – none of which can predict an RUL for software. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, this paper addresses how PHM can be used to make decisions for software systems such as version update/upgrade, module changes, system re-engineering, rejuvenation, maintenance scheduling, budgeting, and total abandonment. This paper presents a method to prognostically and continuously predict the RUL of a software system based on usage parameters (e.g., the numbers and categories of releases) and performance parameters (e.g., response time). The model developed has been validated by comparing actual data, with the results that were generated by predictive models. Statistical validation (regression validation, and k-fold cross validation) has also been carried out. A case study, based on publicly available data for the Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to software systems and RUL can be calculated to make system management decisions.

Throughout the world, various policies and regulations exist that govern the buying and selling of energy. In the United States, government mandates have been developed in the form of State-specific Renewable Portfolio Standards (RPSs) that require specific percentages of renewable energy consumption. RPS laws are managed through Power Purchase Agreements (PPAs) and bundled Renewable Energy Credits (RECs), other parts of the world use similar instruments. Both PPAs and RECs typically use a Levelized Cost of Energy (LCOE) calculation to determine the price of energy and may contain limitations on how much energy can be purchased. Conventional LCOE calculations do not account for asymmetry in cash flows that arise from PPA and REC imposed penalties, which can lead to significant errors. The objective of this paper is to use a modified LCOE to demonstrate the impacts of energy purchase limits and the combined effect of multiple energy generators entering into an energy purchase agreement with one buyer. The Maryland Offshore Renewable Energy Credit (OREC) is used as an analysis vehicle, which demonstrates that the effective LCOE can decrease by as much as 36% or increase by as much as 15% when RPS and REC energy purchase limits introduce cash flow asymmetry.

Prognostics and Health Management (PHM) is an engineering discipline focused on predicting the point at which systems or components will no longer perform as intended. The prediction is often articulated as a Remaining Useful Life (RUL). RUL is an important decision-making tool for contingency mitigation, i.e., the prediction of an RUL (and its associated confidence) enables decisions to be made about how and when to maintain the system. PHM is generally applied to hardware systems in the electronics and non-electronics application domains. The application of PHM (and RUL) concepts has not been explored for application to software. Today, software (SW) health management is confined to diagnostic assessments that identify problems, whereas prognostic assessment potentially indicates when in the future a problem will become detrimental to the operation of the system. Relevant areas such as SW defect prediction, SW reliability prediction, predictive maintenance of SW, SW degradation, and SW performance prediction, exist, but all represent static models, built upon historical data — none of which can calculate an RUL. This paper addresses the application of PHM concepts to software systems for fault predictions and RUL estimation. Specifically, we wish to address how PHM can be used to make decisions for SW systems such as version update, module changes, rejuvenation, maintenance scheduling and abandonment. This paper presents a method to prognostically and continuously predict the RUL of a SW system based on usage parameters (e.g., numbers and categories of releases) and multiple performance parameters (e.g., response time). The model is validated based on actual data (on performance parameters), generated by the test beds versus predicted data, generated by a predictive model. Statistical validation (regression validation) has been carried out as well. The test beds replicate and validate faults, collected from a real application, in a controlled and standard test (staging) environment. A case study based on publicly available data on faults and enhancement requests for the open-source Bugzilla application is presented. This case study demonstrates that PHM concepts can be applied to SW systems and RUL can be calculated to make decisions on software version update or upgrade, module changes, rejuvenation, maintenance schedule and total abandonment.

“Sustainment” (as commonly defined by industry and government) is comprised of maintenance, support, and upgrade practices that maintain or improve the performance of a system and maximize the availability of goods and services while minimizing their cost and footprint or, more simply, the capacity of a system to endure. System sustainment is a multitrillion-dollar enterprise, in government (infrastructure and defense) and industry (transportation, industrial controls, data centers, and others). Systems associated with human safety, the delivery of critical services, important humanitarian, and military missions and global economic stability are often compromised by the failure to develop, resource, and implement effective long-term sustainment strategies. System sustainment is, unfortunately, an area that has traditionally been dominated by transactional processes with little strategic planning, policy, or methodological support. This chapter discusses the definition of sustainment and the relationship of sustainment to system resilience, the economics of sustainment (i.e., making business cases to strategically sustain systems), policies that impact the ability to sustain systems, and the emergence of outcome-based contracting for system sustainment.

Pure-tin platings, which have become prevalent in electronics that uses lead-free solders, result in the spontaneous growth of conductive tin whiskers. If the tin whiskers bridge the gap between conductors, they can cause short circuit failures in systems. In this chapter we use cost-based FMEA to determine the projected cost of failure consequence for corrosion mitigation for the assembly of electronic systems. A case study of the lead-free implementation of a power supply demonstrates the return on investment of the control plan for the same product under various risk scenarios.

The Cost of Energy is a major concern for the electric power industry. Customers are sensitive to the cost of renewable energy, which is typically more expensive than conventional energy generation due to the variability and uncertainty associated with their sources. Power Purchase Agreements (PPAs) are developed to balance the energy price and associated risks with poewr gerneation and transmission. The energy delivery limits imposed by current PPAs impact the Levelized Cost of Energy (LCOE) in ways that are not accomodated by existing LCOE models. In this work, a new cost model is developed to evaluate the LCOE from a wind power source under a PPA contract. The application of the model to real wind farms demonstrates that the actual LCOE depends on the defined minimum/maximum energy purchase limitations within a PPA contract. The developed cost model can be used as a basis for setting appropriate PPA terms, such as a price schedule and performance metrics. Hence, it can help the Seller to negotiate penalties and energy price within their PPAs.

Prognostics and Health Management (PHM) technologies have been introduced into wind turbines to forecast the Remaining Useful Life (RUL). PHM with RUL predictions enables predictive maintenance for wind turbines prior to failure, thus avoiding corrective maintenance that may be expensive and cause long downtimes. For a wind farm managed using a power purchase agreement (PPA), a simulation-based European real options analysis model is used to schedule predictive maintenance by maximizing the predictive maintenance option value. For multiple wind turbines indicating RULs concurrently, the predictive maintenance value for each turbine depends on the operational state of all the other turbines, the amount of energy delivered, and the energy delivery target, prices and penalization mechanism for under-delivery defined in the PPA. A case study is presented in which the optimum predictive maintenance opportunity is determined for a wind farm managed using a PPA. To the authors' knowledge, this is the first wind farm maintenance model including a PPA, and the case study demonstrates that the optimum predictive maintenance opportunity for a PPA-managed farm is different from the same farm managed using an “as-delivered” contract, and also differs from the optimum predictive maintenance opportunities for the individual turbines with RULs managed in isolation.

Renewable energy from wind and solar is considered to be the main alternative to fossil fuels. The costs of renewable energy technologies are high and without tax credits they are not currently competitive with fossil fuels in many markets. Improvements in the performance or reduction in operational costs will have significant impacts on the price of renewable energy and ultimately impact their competitiveness. New technologies targeted at improving the efficiency of the current systems or reducing their life-cycle costs will help; however, these technologies are expensive and detailed cost tradeoff and return on investment (ROI) analysis are required to make business cases for them. In this paper, we formulate an ROI model and describe its implementation in a stochastic discrete-event simulator to calculate financial tradeoffs and enable business cases for technology insertion into wind farms. The new ROI model includes changes in revenue and operations costs (including changes in reliability due to the technology insertion) and introduces the concept of identical timeline conditions to guarantee a meaningful ROI calculation. A case study for using light detection and ranging (LIDAR) to increase the efficiency and improve the reliability of wind turbines in a wind farm is provided.

Outcome-based contracts that pay for effectiveness and penalize performance shortcomings have been introduced to incentivize cost reduction efforts on the contractor side of product service systems (PSSs). Outcome-based contracting concepts are being used for PSS acquisitions in healthcare, energy, military systems and infrastructure. These contracts allow customers to pay only for the specific outcomes achieved (e.g., availability) rather than the workmanship and materials delivered. Given the rise in interest in outcome-based contracts, it is incumbent upon the through-life engineering services (TES) community to determine how to design systems (including designing the sustainment of systems) to operate under these contract mechanisms, and to ultimately coordinate the system design with the design of the contract terms. Furthermore, sustainment decisions made under outcome-based contracts must target the optimum action for the population of systems managed under the contract, rather than the optimum action for an individual system. Today, outcome-based contract design is always performed separate from the engineering and TES design processes, and provided as a requirement to the design process, an approach that creates significant risks for all parties. For systems managed under outcome-based contracts, contract failure may mean significant money is spent by the customer (potentially the public) for either no outcome or inadequate outcome, or result in the contractor being driven out of business, which can lead to disaster for both parties.

Sustainment and sustainability are concepts that have pervaded recent engineering culture. Although the popular media often associates sustainability with environmental and socio-ecological constructs, it is a widely used and understood concept with application to many technology, system, and business areas that extend beyond a socio-ecological context. This paper discusses the usage of the term sustainment, proposes a general definition of sustainment that conflates ecological, social, political, economic and technological interests, and provides recommendations that broaden the perspective.

Railway assets suffer wear and tear during operation. Prognostics can be used to assess the current health of a system and predict its remaining life, based on features that capture the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area; however, it has become an important part of condition-based maintenance of systems. As there are many prognostic techniques, usage must be tuned to particular applications. Broadly stated, prognostic methods are either data driven, or rule or model based. Each approach has advantages and disadvantages, depending on the hierarchical level of the analysed item; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more-complete information can be gathered, leading to more-accurate recognition of the impending fault state. However, the amount of information collected from disparate data sources is increasing exponentially and has different natures and granularity; therefore, there is a real need for context engines to establish meaningful data links for further exploration. This approach is especially relevant in railway systems where the maintainer and operator know some of the failure mechanisms, but the sheer complexity of the infrastructure and rolling stock precludes the development of a complete model-based approach. Hybrid models are extremely useful for accurately estimating the remaining useful life (RUL) of railway systems. This paper addresses the process of data aggregation into a contextual awareness hybrid model to obtain RUL values within logical confidence intervals so that the life cycle of railway assets can be managed and optimized.

Understanding the total life-cycle cost is an essential part of all sustainment contracts. Sustainment constitutes 70% or more of the total life-cycle cost of safety-, mission-, and infrastructure-critical systems. For many types of systems, availability is the most critical factor in determining the total life-cycle cost of the system. To address this, availability-based contracts have been introduced in the governmental and non-governmental acquisitions space (e.g., energy, defense, transportation, and healthcare). As a result, in the new service-oriented environment, formulating contracts in cost effective ways (for both the customer and the contractor) is of great importance for pricing, negotiations, and transparency. Meanwhile, the development, implementation, and impact of contract and availability requirements within contracts is not well understood. This article reviews quantitative studies that address the critical elements for designing availability-based contracts. Assessments of current metho...

Yaw error lowers the efficiency and reliability of wind turbines resulting in higher maintenance costs. LIDAR devices can correct the yaw error; however, they are expensive, which creates a trade-off between their costs and benefits. In this study, a stochastic discrete-event simulation model is developed that models the operation of a wind farm. We optimize the net present value (NPV) changes associated with using LIDAR devices in a wind farm to determine the optimum number of LIDAR devices and their associated turbine stay time as a function of number of turbines in the wind farm for specific turbine sizes.

Prognostics and health management (PHM) provides an opportunity for lowering sustainment costs, improving maintenance decision‐making, and providing product usage feedback for the product design and validation process. This chapter discusses the determination of the implementation costs, potential cost avoidance, and the resulting return on investment offered by electronics PHM. An important attribute of most business cases is the development of an economic justification. Return on investment (ROI) is a useful means of gauging the economic merits of adopting PHM. Financial costs are part of the engineering economics of technology acquisitions. Implementation costs are the costs associated with the realization of PHM in a system, that is, the achievement of the technologies and support necessary to integrate and incorporate PHM into new or existing systems. The costs of implementing PHM can be categorized as recurring, nonrecurring, or infrastructural depending on the frequency and role of the corresponding activities.

Wind energy and especially offshore wind energy faces an uphill battle in the United States to become a mainstream source of energy generation due to its high price relative to fossil fuels. The wind industry is looking for methods to reduce the costs of energy production by improving the efficiency of wind turbines and reducing their operation and maintenance costs. Correction of yaw error is one way to lower the price of wind energy. Yaw error is the angle between the turbine’s central axis in horizontal plane and the wind flow direction. LIDAR devices are used to correct yaw error, however they are expensive. Therefore, there is a need to develop a return on investment model (ROI) to calculate the cost trade-offs of using such systems. This work reviews how yaw error affects the performance and maintenance costs of wind turbines, discuss the development of an ROI model and provide a case study with two scenarios where LIDAR is used to correct the yaw error of an onshore and an offshore wind farm.

With renewable energy and wind energy in particular becoming mainstream means of energy production, the reliability aspect of wind turbines and their sub-assemblies has become a topic of interest for owners and manufacturers of wind turbines. Operation and Maintenance (O&M) costs account for more than 25% of total costs of onshore wind projects and these costs are even higher for offshore installations. Effective management of O&M costs depends on accurate failure prediction for turbine sub-assemblies. There are numerous models that predict failure times and O&M costs of wind farms. All these models have inputs in the form of reliability parameters. These parameters are usually generated by researchers using field failure data. There are several databases that report the failure data of operating wind turbines and researches use these failure data to generate the reliability parameters through various methods of statistical analysis. However, in order to perform the statistical analysis or use the results of the analysis, one must understand the underlying assumptions of the database along with information about the wind turbine population in the database such as their power rating, age, etc. In this work, we analyze the relevant assumptions and discuss what information is required from a database in order to improve the statistical analysis on wind turbines’ failure data.

Technology-centric products often contain parts, software, and materials that have procurement lives that end before the product they are in reaches the end of its life cycle. Life-cycle mismatches between parts and products, which is referred to as obsolescence, can result in large life-cycle costs for mission, safety, and infrastructure critical products, such as aircraft, medical, and military systems. Diminishing Manufacturing Sources and Materials Shortages is a type of obsolescence that describes the loss of the ability to purchase (or procure) a part (or its associated technology) from its original manufacturer. A key enabler for performing pro-active and strategic management of the life cycle of mission, safety, and infrastructure critical products is the ability to forecast when technologies and parts will become unavailable for purchase, that is, obsolete. This article reviews methods that are used to forecast obsolescence, focusing on long-term forecasting used to predict the obsolescence dates for technologies and electronic parts.

The loss of critical human skills that are either nonreplenishable or take very long periods of time to reconstitute impacts the support of legacy systems ranging from infrastructure, military, and aerospace to information technology. Many legacy systems must be supported for long periods of time because they are prohibitively expensive to replace. Loss of critical human skills is a problem for legacy system support organizations as they try to understand and mitigate the effects of an aging workforce with highly specialized low-demand skill sets. Existing literature focuses on workers that have skills that are obsolete and therefore need to be retrained to remain employable; alternatively, this paper addresses the system support impacts due to the lack of workers with the required skill set. This paper develops a model for forecasting the loss of critical human skills and the impact of that loss on the future cost of system support. The model can be used to support business cases for system replacement. A detailed case study of a legacy control system from the chemical manufacturing industry is provided and managerial insights associated with the support of the system drawn.