DOC PREVIEW
Using Real Options to Manage Condition-Based Maintenance Enabled by PHM

This preview shows page 1-2 out of 7 pages.

Save
View full document
View full document
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience
View full document
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience
Premium Document
Do you want full access? Go Premium and unlock all 7 pages.
Access to all documents
Download any document
Ad free experience

Unformatted text preview:

Using Real Options to Manage Condition-Based Maintenance Enabled by PHM Gilbert Haddad, Peter Sandborn, and Michael Pecht Center for Advanced Life Cycle Engineering Department of Mechanical Engineering University of Maryland College Park, MD, USA [email protected] Abstract—This work proposes a new economic approach that can form a cost-benefit-risk basis for optimum decision making for systems with prognostic capabilities, and a method to assess the value of PHM for its user after a prognostic indication. PHM potentially enables performance based logistics, condition-based maintenance, and reduced life cycle cost. When an anomaly is detected in a system, and the remaining useful life is estimated, the user has to make a decision about how to operate or manage the system given a set of constraints or requirements (e.g., to maximize availability). This paper proposes a new economic basis for evaluating the flexibility enabled by prognostic and health management systems. The proposed framework is based on Real Options theory for valuating the options arising through the use of PHM. In the context of PHM an option represents the purchase of an opportunity to take a particular action in the future. The underlying assets are not tradable securities (as they would be in financial options), but rather, they are cost avoidance opportunities or mission values. We provide two potential applications to illustrate the new model for electronic systems in a commercial aircraft used by a commercial airline, and wind farms. Keywords-component; decision support system; maintenance optimization, real options; economic analysis; post-prognostic indication; PHM; CBM; avilability I. INTRODUCTION Prognostics and health management (PHM) is discipline consisting of technologies and methods to assess the reliability of a product in its actual life cycle conditions to determine the advent of failure and mitigate system risks [1][2]. It is a technology that allows complex systems to shift from traditional maintenance (scheduled or unscheduled) to condition-based maintenance (CBM). PHM is an enabler of performance-based contracts and potentially reduces life-cycle cost. When an anomaly is detected in a PHM-enabled system, and the remaining useful life (RUL) of the system is estimated, the decision maker is then faced with multiple choices called options, which can be exercised to manage the health of the system. An ‘option’ is a right, but not an obligation to take a particular action in the future [3]. Existing work on health management for systems with prognostic capabilities addresses the enterprise level (a fleet of systems) and the system level (individual system instances). In broad terms, the former focuses on the use of PHM to perform logistics planning, availability optimization and on building business cases to justify the implementation of PHM across an enterprise, and the latter focuses on fault accommodation and isolation to ensure mission success, and failure avoidance. This paper provides a new economic basis to manage the flexibility (e.g., when to perform maintenance after a prognostic indication) enabled by PHM systems using Real Options (RO) theory. It addresses a gap in health management for systems with PHM by addressing the economic aspect after a prognostic indication. We also attempt to link high-level requirements (such as an availability requirement from the customer) and low-level requirements such the performance of the prognostic algorithm [4]. Systems incorporate PHM for a number of reasons that include: failure avoidance, life cycle cost reduction, warranty verification, future system design improvements, and availability improvement. One very common PHM driver is availability (which is reflected into safety and life cycle cost). For example, the value of safety and infrastructure critical systems such as avionics systems and wind farms is associated with their availability. Availability is the ability of a service or a system to be functional when it is requested for use or operation [5]. Commercial airlines go out of business if their planes are not available to fly; 911 systems are useless if they are not available when people call them; and wind farms cannot be depended on for energy generation if they are always down waiting for maintenance. Availability of a system is a function of its reliability and how efficiently it can be maintained. There are different approaches to maintenance, but fundamentally, depending on if a system has failed, when we think it will fail, how it has failed, etc., there are decisions that need to be made about how to and when to maintain it. A simple motivating example would be an aircraft flying between two locations. A prognostic indication is obtained at a certain time during the flight. The decision-maker has a set of options amongst which they can choose. The term options will be used in the remainder of the paper to denote a choice or action the decision maker can take after a prognostic indication. Fig. 1 shows a general diagram for options arising after prognostic indication. Not all systems have all the options shown in Fig. 1 available to them. The authors would like to thank the more than 100 companies and organizations that support research activities at the Center for Advanced Life Cycle Engineering at the University of Maryland annually.Figure 1- Options arising post-prognostic indication If a value, or valuation, of each one of the options can be established, the appropriate health management decisions can be performed at the system-level. Furthermore, the approach can be extended to optimize health management decisions at the enterprise-level. Consider a wind farm example; assuming that a farm has 10 turbines with prognostic capabilities each having a different remaining useful life (RUL). RUL is treated as a deterministic number in this example for the purpose of illustration. Fig. 2 shows the RUL for each of the turbines of such as farm. Figure 2- Turbines with different prognostic


Using Real Options to Manage Condition-Based Maintenance Enabled by PHM

Download Using Real Options to Manage Condition-Based Maintenance Enabled by PHM
Our administrator received your request to download this document. We will send you the file to your email shortly.
Loading Unlocking...
Login

Join to view Using Real Options to Manage Condition-Based Maintenance Enabled by PHM and access 3M+ class-specific study document.

or
We will never post anything without your permission.
Don't have an account?
Sign Up

Join to view Using Real Options to Manage Condition-Based Maintenance Enabled by PHM 2 2 and access 3M+ class-specific study document.

or

By creating an account you agree to our Privacy Policy and Terms Of Use

Already a member?