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Industry 4.0 is the fourth industrial revolution where machine operations are linked through data communications and together, they form a production ecosystem. In Industry 4.0 settings, these operations are monitored, recorded and analyzed. This can be performed at Edge, Fog or Cloud levels. In the industrial big data era, with ever maturing sensor technologies, data capture, communication and storage technologies, utilizing machine data for operational insights provides companies with competitive advantages. Benefits can include reduced operational and maintenance costs, a decrease in unscheduled downtime and greater assurance of on-time delivery of products. In this work, we will cover the milestones of implementing an industry 4.0 condition-based maintenance (CBM) strategy for machine tools and their surrounding systems. In addition, we will discuss the methodology for sensor selection, data collection, transmission and storage, return on investment for CBM and building CBM models for detection. Finally, will delve into challenges of implementing this methodology in industrial settings from both technology and logistics aspects.

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.

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.

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.

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.

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.

Yaw error is the angle between a turbine’s rotor central axis and the wind flow. The presence of yaw error results in lower power production from turbines. Yaw error also puts extra loads on turbine components, which in turn, lowers their reliability. In this study we develop a stochastic model to calculate the average capacity factor of a 50 turbine offshore wind farm and investigate the effects of minimizing the yaw error on the capacity factor. In this paper, we define the capacity factor in terms of energy production, which is consistent with the common practice of wind farms (rather than the power production capacity factor definition that is used in textbooks and research articles). The benefit of using the energy production is that it incorporates both the power production improvements and downtime decreases. For minimizing the yaw error, a nacelle mounted LIDAR is used. While the LIDAR is on a turbine, it collects wind speed and direction data for a period of time, which is used to calculate a correction bias for the yaw controller of the turbine, then it will be moved to another turbine in the farm to perform the same task. The results of our investigation shows that although the improvements of the capacity factor are less than the theoretical values, the extra income from the efficiency improvements is larger than the cost of the LIDAR.

Printed circuit boards (PCBs) are made of several dielectric layers stacked on top of each other. These layers could be standard PCB core board or high density interconnect (HDI) layers. Microvias allow signal and power transmission between the HDI layers of the PCBs. The presence of voiding in filled microvias has raised concerns in industry about how they affect the degradation of microvias during the life cycle of the product. Voids can vary widely in shape and size and have been observed in both stacked and single-level microvias. This paper examines whether the presence of voids alone is responsible for the degradation of microvias, or if parameters such as void size and void shape have an influence as well. Both voided and nonvoided microvias were tested together using liquid-to-liquid thermal shock as the accelerated testing method to understand their different behaviors under thermomechanical stresses.

The rise of energy prices, concerns over climate change and geopolitical issues have brought special attention to renewable sources of energy and wind energy in particular. Based on NREL projections, the United States has more than 32,000 TWh of onshore and 17,000 TWh of offshore potential for wind power generation, which is far beyond its 11,000 TWh of current annual electricity consumption. However, there are a number of efficiency challenges that must be overcome in order to turn this potential into actual production. One area that can potentially improve the energy production of wind turbines is the correction of yaw error. Yaw error (also referred to as yaw angle or yaw misalignment) is the angle between the turbine’s rotor and the wind direction. A yaw error reduces turbine’s power production at wind speeds below the rated speed. Besides impacting the power producing ability of a turbine, yaw error also affects the reliability of critical subsystems in wind turbines. Variation in yaw error (at any wind speed and not only below the rated speed) affects the loads on the components and the subsequent mechanical stresses. These mechanical stresses change the damage accumulation for components and sub-assemblies, which ultimately affects their reliability. About 17 to 28% of wind project costs attribute to O&M costs, which are directly affected by the reliability. In this study, we investigate the effects of yaw error on the reliability of blades by performing load and stress analysis for various yaw errors. We then use the results of these analyses to adjust the Weibull parameters used for predicting the failure time of blades. Finally, we will use a stochastic cost model to show how correcting the yaw error can avoid maintenance costs in wind farms.

O&M costs can make up to 30% of the lifetime CoE of an offshore wind farm [1]. As a means of reducing this cost operators and O&M providers need a greater understanding of what is driving that O&M cost. Failure rates of wind turbines and their components are a key driver of O&M costs. Past papers have modelled O&M costs assuming a fixed average failure rate for wind turbine subsystems [2]. This work aims to determine if it is accurate to assume a fixed failure rate or if a failure rate distribution through time can be provided to allow for more accurate O&M cost modelling and in turn CoE modelling. This paper shows the results of an analysis of offshore wind turbine annual failure rates over an 8 year period. The analysis is based on around 350 modern multi MW offshore turbines located in 5-10 offshore wind farms throughout Europe. The literature review for this paper indicated that a constant average failure rate should only be used if the shape parameter of the failure distribution is around 1. However results from the failure rate analysis in this paper have shown that in many cases a constant failure rate is not correct for O&M Modelling.

In this paper, a simulation-based real options analysis (ROA) approach is applied to valuate the predictive maintenance options created by PHM for multiple turbines in offshore wind farms managed under outcome-based contracts known as power purchase agreements (PPAs). When a remaining useful life (RUL) is predicted for a subsystem in a single turbine, a predictive maintenance option is triggered. If predictive maintenance is implemented before the subsystem or turbine fails, the option is exercised; if the predictive maintenance is not implemented and the subsystem or turbine runs to failure, the option expires and the option value is zero. The time-history cost avoidance and cumulative revenue paths are simulated considering the uncertainties in wind and the RUL predictions. By valuating a series of European real options based on all possible predictive maintenance opportunities, the maintenance opportunity with the maximum value can be obtained. In a wind farm, there may be multiple turbines concurrently indicating RULs. To model multiple turbines managed via an outcome-based contract (PPA), the cumulative revenue and cost avoidance for each turbine depends on the operational state of the other turbines in the farm, the amount of energy that has been delivered and will be delivered by the whole farm. A case study is presented that determines the optimum predictive maintenance opportunity for a farm under a PPA, the optimum predictive maintenance opportunity for the same farm managed via an as-delivered contract, and the optimum predictive maintenance opportunities for individual turbines managed independently.

Intermittent failures and no fault found (NFF) phenomena are a concern in electronic systems because of their unpredictable nature and irregular occurrence. They can impose significant costs for companies, damage the reputation of a company, or be catastrophic in systems such as nuclear plants or avionics. Intermittent failures in systems can be attributed to hardware failures or software failures. In order to diagnose and mitigate the intermittent failures in systems, the nature and the root cause of these failures have to be understood. In this paper we have reviewed the current literature concerning intermittent failures and have a comprehensive study on how these failures happen, how to detect them and how to mitigate them.