Advanced Embedded Passives Program

Safety critical mechanical systems and structures, such as engines, aircraft frames, and bridges, have benefited from advanced sensor systems developed specifically for in-situ fault diagnosis (often called health and usage monitoring, or condition monitoring). A considerable body of knowledge on health monitoring of mechanical systems, using various combinations of sensors that monitor loading conditions in the application environment already exists.

However, most complex systems today contain a significant portion of electronics content. In addition, studies have shown that most failures are often traced back to the electronics, not the mechanical components, of a system because electronics typically fail earlier. Therefore, the development of new health monitoring approaches that are applicable to electronics is in need. Furthermore, it is desirable to monitor the health of electronic systems and develop damage models that assess and predict the remaining life of such systems (often called prognostics) to enable advanced warning of failures and life-cycle management planning.

Prognostics and health management (PHM) is a framework of methodologies that permit the reliability of a system to be evaluated in its actual life-cycle conditions, to determine the advent of failure, and mitigate the system risks. PHM methodologies are based on several key elements, as shown in the figure below. First, prognostic sensors provide the capability to either monitor failure precursors or collect a history of environmental stresses. The data collected by prognostic sensors, in many cases, must be compressed for archival and analysis purposes while preserving the salient information contained within the data. Second, assessment methods must be employed to convert the sensor data into accumulated damage based on the relevant failure mechanisms. Third, remaining life of systems must be determined from the accumulated damage to enable a prediction of when the system will likely fail. The final key aspect of PHM methodologies is to derive value from remaining life estimations. Value can take the form of advanced warning of failures; increased availability through an extension of maintenance cycles and/or timely repair actions; lower life-cycle costs of equipment from reductions in inspection costs, downtime, inventory, and no-fault-founds; or improved system qualification, design, and logistical support of fielded and future systems.

Return on Investment Modeling (Maintenance Planning)

All PHM approaches are essentially the extrapolation of trends based on recent observations to estimate Remaining Useful Life (RUL). The value obtained from PHM can take the form of advanced warning of failures; increased availability through an extension of maintenance cycles and/or timely repair actions; lower life-cycle costs of equipment from reductions in inspection costs, downtime, inventory, and no-fault-founds; or the improvement of system qualification, design, and logistical support of fielded and future systems. Proposals to adopt PHM approaches are often articulated in the form of a business case; an economic justification is the cornerstone of a persuasive case. Return On Investment (ROI) is a useful means of gauging the economic merits of adopting PHM.

The determination of the ROI allows managers to include quantitative and readily interpretable results in their decision-making. ROI analysis may be used to select between different types of PHM, to optimize the use of a particular PHM approach, or to determine whether to adopt PHM versus more traditional maintenance approaches. Furthermore, ‘point estimates’ of the value of PHM based on a set of fixed inputs when in reality, many of the critical inputs are uncertain, may not accurately represent a business case. Accommodating the uncertainties in the PHM ROI calculation is at the heart of developing realistic business cases that address prognostic requirements.

Our analysis is based on a discrete-event simulation that follows a population of sockets through their lifetime from first LRU installation to retirement of the socket. “Discrete-event simulator” refers to the simulation of a timeline, where specific events are added to the timeline and the resulting event order and timing can be used to analyze throughput, life cycle cost, availability, etc. “Socket” refers to one instance of an installation location for an LRU. “Population” means that the simulator is stochastic (governed by the laws of probability) so that a statistically significant number of non-identical fielded systems can be assessed and the results are distributions rather than single values.

The methodology and tools developed by CALCE enable the calculation of ROI, life cycle cost, and availability as probability distributions for systems of one or more concurrently managed LRUs that employ mixtures of management approaches. The features of the model include:

•Various maintenance interval and PHM approaches are distinguished by how sampled time-to-failure values are used to model PHM forecasting distributions
- Unscheduled maintenance
- Fixed interval scheduled maintenance
- Precursor to failure (LRU dependent)
- Data driven or life consumption monitoring (LRU independent)
•False positives modeled
•Operational profiles
•Multiple sockets can be modeled at same time
•Time-to-failure is segregated into a set of failure mechanisms for which a distribution can be determined and a set of random failure mechanisms (failure mechanisms that may be predictable but are not detected by the PHM approach used)
Spares inventory (initial spares, replenishment policy, lead time)
Cost of money

Publications Related to this Research Area

P. Sandborn, “A Decision Support Model for Determining the Applicability of Prognostics Health Management (PHM) Approaches to Electronic Systems,” Proc. Reliability and Maintainability Symposium (RAMS), Arlington, VA, Jan. 2005. -- note, this paper has been superseded by the following paper: link

CALCE Prognostics group conducts research and development of prognostics and health management applications for electronic products and systems, as well as systems-of-systems. The research focuses on computational algorithms, advanced sensors and data collection techniques, condition-based maintenance, prognostics and health management for the application of in-situ diagnostics and prognostics. The group is using physics based models along with empirical models for prognostics. CALCE is researching the use of a hybrid approach, which combines physics of failure and data driven methods for accurate prognostics and diagnostics.

The prognostic group collaborates with industry and research partners to develop advanced sensors for diagnostics and prognostics applications. Applications such as tamper proof low-cost autonomous sensors that incorporate wireless communication, high onboard memory capacity and can be attached to any product with minimal interference to the functioning of that product are being developed. CALCE Prognostics enables real time prognostics and health management of electronic products in their application environment.

Prognostics and Health Management (PHM) for Electronic Systems

Introduction to Electronic Systems PHM

Return on Investment Modeling (Maintenance Planning)

Publications Related to this Research Area