Prognostics and Health Management’s Potential
Benefits to Warranty
Yan Ning1, Peter Rundle2, Michael Pecht1
1
CALCE: Center for Advanced Life Cycle Engineering, University of Maryland
College Park, MD, 20742
2
Rundle Law Corporation, Irvine, CA, 92606
pecht@calce.umd.edu
Abstract
Prognostics and Health Management (PHM) is a methodology that permits the assessment of product
reliability and the prediction of remaining useful life (RUL) under life-cycle conditions through
continuous monitoring. This paper addressed a unique use of PHM for business warranties. PHM is able
to help understand what went wrong quickly and exactly in a product. PHM will assist companies to
make optimized decisions about warranty service. PHM can shift the reactive warranty paradigm to a
proactive one. PHM will also reduce no-fault-found returns, verify customer-induced failures, and avoid
recalls and safety issues. With PHM incorporated into products, warranty policies will be reformed and
new warranty strategies will be created. To demonstrate possible advantages and disadvantages of PHMbased warranty, this paper discusses how PHM can be implemented on product warranty with a case
study.
1. Background and Motivation
Warranty has gained increasing awareness and interest from manufacturers, consumers, governments, and
legislations due to demand for high quality products. A warranty is an assurance that the manufacturer of
a product guarantees the quality and reliability of a product in terms of correcting any legitimate problems
[1]. When a product does not perform as expected, the manufacturer or seller must provide a remedy such
as repairing or replacing the item to meet the warranty.
Warranties are categorized into two basic types − implied and express [2]. Implied warranties are
unspoken and unwritten promises based on merchantability or fitness, arising from the nature of the
transaction. An express warranty is explicitly offered by a supplier to a customer during a sales
transaction, either orally or in writing. According to the Magnusson-Moss Warranty Act, written
warranties are further recognized as “full” or “limited” [2]. A “full” warranty provides a full refund, a free
replacement, or a free repair within a reasonable amount of time. All warranties that offer anything less
than the “full warranty” must be designated as “limited.”
Warranty is an important element of post-sale service strategy, which is helpful for suppliers when selling
a product. Customers view a warranty as a measure of the product’s performance and want to know that
the supplier will provide a solution for problems arising from the product after purchase. Longer
warranties create a tool that marketers can use to convey the seller’s confidence in the product.
A warranty will result in costs to the supplier beyond those associated with the design, manufacturing,
and sale of the product. According to Warranty Week Magazine, automotive manufacturers and their
suppliers spent almost $13 billion on warranty claims in the U.S. in 2006; the total cost of warranty
claims for computer and related high technology U.S.-based companies is about $8 billion in 2007. In
2009, top 50 U.S.-based warranty providers paid over $14 billion on warranty claims, in which warranty
costs were around $353 million for Microsoft, $232 million for Seagate Technology, and $42 million for
Palm Inc. [3].
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Problems need to resolve in warranty include how to understand what went wrong quickly and provide
service accordingly; how to deal with no-fault-found (NFF) returns and reduce their occurrence; how to
verify customer-induced failures; and how to avoid recalls and safety issues. By solving these problems,
warranty cost will decline.
Warranty problems and costly expenditures of warranty are encouraging manufacturers to improve field
reliability and operational availability. Traditional reliability prediction methods such as Mil HDBK217D [4], 217-PLUS, Telcordia [5], PRISM [6], and FIDES [7], rely on the collection of failure data and
generally assume that the products or components have constant failure rates. Today, these methods are
considered inaccurate for forecasting actual field failures and provide misleading predictions that can
result in poor logistics decisions for warranty and maintenance service.
For repairable products, another way to reduce warranty cost is to employ effective maintenance practice
to decrease warranty service cost. In the literature, researchers address maintenance policies and develop
maintenance models under diverse warranties [8-15]. Effective maintenance actions can reduce system
downtime and service costs, while improper maintenance schedules may cause damage to spread to the
entire system, lead to long downtime, and result in unnecessary inspections and component changes.
Interest has been growing in monitoring the ongoing reliability of products in order to provide advance
warning for failures and assist in maintenance. PHM is a technique that helps maintenance engineers
detect initial degradation of a product and estimate its remaining useful life. PHM has the potential to
reduce warranty claims and costs, improve warranty service, and saving consumers’ time and money.
Further, the implementation of PHM on a product will affect how sellers and buyers view warranty and
how warrantors approach warranty.
The paper is organized into five sections. Section 2 introduces the concept of PHM and the three types of
PHM approaches. In Section 3 the authors address the significant effects of PHM on warranties. A case
study is provided in Section 4 to demonstrate the advantages and disadvantages of integrating PHM into
product warranty. Section 5 concludes this paper.
2. Overview of PHM
PHM is an approach that seeks to estimate system reliability in-situ, predict a product’s remaining useful
life (RUL) for expected future use, and reduce inspections and maintenance efforts through real-time
monitoring and incipient fault detecting [16]. PHM utilizes sensing techniques, data mining, and
interpretation of environmental, operational, and performance-related parameters to indicate a system’s
health status. PHM has been implemented using model-based, data-driven, and fusion approaches.
Model-based approaches integrate physical understanding of a product into estimation of RUL.
Prognostics can be achieved via either system or physics-of-failure (PoF) modeling. In system modeling,
mathematical functions or mappings are used to represent the system [17]. PoF [18] monitors the lifecycle loads of a product and utilizes the knowledge of failure mode, mechanism, and effect analysis
(FMMEA) [19] to perform reliability and RUL modeling. PoF models use appropriate stress and damage
analysis methods to evaluate the susceptibility to failure in terms of the time-to-failure or likelihood of
failure for a given geometry, material construction, and environmental and operational condition [20].
RUL is then predicted based on the damage accumulation information [21]. However, failure mechanisms
for components are not always available. Also, PoF approaches tend to be computationally prohibitive at
system level.
Data-driven approaches are based on machine-learning techniques and statistic pattern recognition to
analyze current and historical operational data for predicting RUL of a product [22]. Data-driven
approaches can capture the complicated relationships and trends without the need for specific physical
models. Threshold limits on statistical features, such as mean and standard deviation, are used to identify
the severity of a fault. Time series analysis provides progression of fault or damage over time, which is
used to determine the next probable occurrence of failure and estimate prognostics distance [22].
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However, data-driven approaches cannot distinguish different failure mechanisms, and therefore, it is
difficult to perform root-cause analysis; and they cannot estimate RUL without historical knowledge of
operational parameters.
Fusion approaches [22,23] are developed to leverage the power from both model-based and data-driven
approaches while eliminating some of their drawbacks. The fusion approaches involve both FMMEA
analysis to identify the failure mechanisms and the monitoring of critical parameters using data-driven
methods. Continuous health monitoring provides information about the environmental and operational
loads and the performance of the product to detect any anomaly in the product. Model-based and datadriven approaches are combined to define the failure threshold to predict the RUL.
3. How PHM Can Benefit Warranty
PHM has substantial impact on warranty. It can improve warranty service by understanding what went
wrong quickly and accurately, assisting companies to make optimized warranty decisions, and enabling
proactive warranty paradigm. It will reduce no-fault-found returns, verify customer-induced failures, and
avoid recalls and safety issues under warranty. A more profound effect of PHM is that it has the potential
to reshape manufacturers’ warranty strategies. Eventually, PHM is potential to reduce warranty and life
cycle costs and bring more profits to manufacturers and suppliers. Figure 1 explicitly outlines how PHM
can change warranty.
What went wrong
Improve
warranty service
Optimal warranty
decision-making
Proactive warranty
paradigm
Reduce NFF
returns
PHM
Verify customer
abuse
Avoid recalls and
safety issues
Reduce
warranty
cost &
life cycle
cost
Lifetime warranty
strategy
Availability-based
warranty strategy
Create warranty
strategies
Figure 1 The potential effects of PHM on warranty
3.1 Improve warranty service
The implementation of PHM into products will improve warranty service through real-time monitoring,
initial fault detection, advance warning of failures, and RUL prediction. With PHM, system reliability can
be estimated in-situ and the problematic components or structures can be located and isolated from the
system based on the monitoring data and usage profile. Warranty engineers will understand what went
wrong in the system quickly and accurately, and repair or replace the returned item accordingly. Thus,
PHM will reduce diagnostics and inspection effort for returned products and decrease warranty cost.
PHM data can assist companies to make optimized decisions for warranty service. They can select
optimal actions on warranty returns, either by replacing the item with a new one or repairing it by means
of perfect (overhaul), imperfect, or minimal repair [24,25]. Different warranty service results in different
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costs and hazard rates of future failure. Minimal repair costs less, while replacement and major repair
reduces the chance of subsequent failures. The manufacturer or supplier can choose an optimal action
based on the estimation of system health condition and RUL from PHM data, attaining an active health
management of the system and reducing overall warranty cost.
Currently, most warranty services are performed after the occurrence of failure—replacing or repairing a
product when it is out of service. PHM can change the means that warranty is approached. It is able to
shift warranty philosophy from a reactive way to a proactive paradigm, seeking to reduce system failures,
unplanned maintenance events, and unnecessary inspections to reduce warranty cost.
The proactive warranty paradigm can be achieved through effective maintenance practices. Conditionbased maintenance (CBM) uses real-time data to prioritize maintenance resources and attempts to avoid
unnecessary maintenance tasks by taking actions only when there is evidence of abnormal behavior [26].
With increasing requirement in predicting degradations and initiatory faults, a prognostics layer is added
to CBM. PHM can be regarded as an enabler of condition-based maintenance through continuous
monitoring and RUL prediction.
With real-time monitoring, if an inappropriate load is detected from a PHM system, it can be corrected to
make the system work under reasonable and optimal loads. This will improve field reliability and
availability and reduce over consumption. “Prognostics” is the process of predicting a future state of
reliability based on current usage profile and historic conditions. The distance between advance warning
and predicted failure allows proper actions to be taken to eliminate failure occurrence. Warranty
engineers can conduct proactive maintenance tasks to prevent system deterioration and field failures.
Thus, warranty returns will be reduced.
Additionally, PHM can facilitate proactive logistical support for warranty service. If the prognostics
solution is designed in a way that it shows where and how the product is degrading or failing, the
manufacturer and supplier can acquire the necessary equipment and supplies before the product comes in
for servicing. The replacing products, spare parts, equipment and tools, and labor, if scheduled before
hand, are cheaper than if they were to be expedited. At the same time, PHM can help avoid a high
inventory level of resources.
3.2 Reduce NFF returns
No-fault-found (NFF) returns afflict many industries−automotive, consumer electronics, mobile telephone
and others. It is estimated that 50%-60% NFF failure rate is seen in product returns [20]. The cost
involved in NFF returns makes up a large percentage of warranty cost. A great many devices that are
reported as NFF during the first troubleshooting session are often returned with the same NFF symptoms
or a permanent mode of failure. In some conditions, the mechanics replace everything that may cause the
problem to avoid sequential failures and to save test time. However, this often leads to perfectly good
units being removed, which is costly. Since intermittences and other cannot-duplicate failures occur
randomly and in a small time window, conventional test equipment and methods are limited when applied
to trouble-shooting for such problems.
PHM offers a potential solution to mitigate NFF risks. The continuous in-situ monitoring and collection
of real-time data from PHM provides an overall usage trend, where an anomaly can be identified, critical
parameters related to a specific fault model can be isolated, and root cause can be traced back. Pattern
recognition and statistical techniques employed in detecting changes in system behavior have shown that
it is possible, through data-driven PHM approaches, to notice sudden changes in system parameters and
analyze intermittent faults [17]. Thus, PHM can potentially reduce the warranty cost resulting from NFF
faults.
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3.3 Verify customer-induced failures
Warranty verification and consumer abuse detection is an important issue in warranty. Most warranty
policies explicitly exclude failures that are inflicted by consumers, whether intentional or not. However, it
is not easy to determine whether the failure is induced by a consumer. Thus, this part of warranty service
is often costly for manufacturers. By tracking the usage profile data during the life cycle of a product, the
company can identify the cause of failure and verify the field usage condition for returned products.
Therefore, PHM can help identify customer-induced failures that should be excluded from warranty
coverage and hence reduce the cost from this type of returns.
3.4 Avoid recalls and safety issues
If a product failure involves human injuries and fatalities, the manufacturer will not only undertake direct
warranty cost but also assume expensive accident costs, court fees, and losses to its reputation and sales.
For example, Toyota Motor Corporation (TMC) announced a recall on about 2.3 million vehicles with
sticky accelerator pedals in the U.S. on January 21, 2010, and widened the net to include Europe and
China in the following week [27]. The sticky accelerator pedal led to 52 fatalities, according to National
Highway Traffic Safety Administration [28]. According to Tatsuo Yoshida, an auto analyst in Tokyo, the
total cost of the recall was likely to go up to about $900 million, and lost sales would cost Toyota another
$155 million per week [29]. Also, at least 89 class-action lawsuits had been filed by March 2010 claiming
that the massive safety recalls are causing the value of other Toyota owner’s vehicles to plummet. The
lawsuits could cost the Japanese automaker $3 billion or more [28].
As PHM can estimate RUL and predict future failures, it is potential to prevent catastrophes and assures
safety. With the PHM prediction of initial degradation and advance warning of failures, actions can be
taken before major failures and catastrophes really occur. PHM data can also be used to provide clues to
reveal the size of a problem. If predicted return rates are higher than originally expected, the company
may shift the warranty service to an earlier level of RUL and investigate problems in its product design.
In additional, if deviation from a health baseline is identified and screened by a PHM system during
manufacturing phases, the manufacturer could timely pin-point the problems earlier on and correct them
before producing more. This will avoid recalls after putting the product on the market.
3.5 Create warranty strategies
Warranty strategy is an important product character for marketing because warranty is often viewed by
customers as a signal of product quality and reliability. PHM incorporated into a product can create new
warranty strategies that will provide opportunities for companies.
Nowadays, most warrantors do not bear the cost of replacement or repair of parts after the warranty
period. However, customers expect maintenance-free warranty throughout the product’s useful life, for
example, at least 100,000 miles for a vehicle (a vehicle is typically offered a five-years/50,000 miles
(5/50) warranty). A product with a relatively longer warranty period might be inferred as more reliable
than one with a shorter period. The adoption of PHM into products will make manufacturers confident
about prolonging their warranty period due to improved warranty service and reduced warranty cost.
Companies may further offer a lifetime warranty based on PHM system to assure customers and improve
competitiveness. Rahman and Chattopadhyay [30] address lifetime warranty policies and models for
estimating costs for different lifetime definitions such as technical life (physical life), technological life
(Obsolescence), commercial life (economic life/functional life), and Ownership life. With PHM
integrated into products, there will be opportunities for manufacturers to promote lifetime warranties.
“Power by the hour” is a standard maintenance model for aircraft engines and avionics systems. The
airlines do not purchase the engine itself under such contracts, but the purchase guarantees the flight
hours that the engine can perform. The engine company undertakes all the maintenance of the product
with a fixed maintenance cost. A manufacturer can afford this type of service only when it has a low-cost
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and efficient maintenance servicing system. PHM can help realize this, solving the customers’ problems,
reducing the manufacturer’s cost, and optimizing the procedure. With a PHM system built into a product
the manufacturer could schedule necessary condition-based maintenance to make sure the product works
properly and to reduce maintenance cost during the warranty period. PHM can enable a new warranty
practice to guarantee the usage of a product rather than the product itself.
Because PHM can reduce system failures and downtime, it may further create an availability-based
warranty. Availability is defined as “the instantaneous probability that a system or component will be
available to perform its intended mission or function when called upon to do so at any point in time.”
Operational availability (Ao) is calculated by Equation 1 [31]:
‫ܣ‬଴ =
Mean Time Between Maintenance (MTBM)
Mean Downtime + MTBM
Equation 1
PHM is the right technique to increase MTBM and reduce downtime via condition-based maintenance.
This will increase the availability of the system, ‫ܣ‬଴. The availability-based warranty strategy stimulated
by PHM might be a new trend and opportunity.
3.6 Warranty cost reduction
Warranty cost is a big part of the life cycle costs, and manufacturers are motivated to reduce it to make
more profit. Warranty cost is determined by several factors, such as warranty policy, usage environment
and intensity, reliability of the product, and warranty service. As addressed above, warranty cost can be
reduced with the implementation of PHM through improved warranty service, reduced NFF returns and
safety issues, verified usage conditions, and strategic warranty practices.
On the other side, the implementation of PHM itself will result in additional costs in the form of hardware
and software. Only when the cost avoidance from PHM exceeds the investment in it, can adopting PHM
cut down product life cycle costs. Sandborn and Wilkinson [32] address if and how a specific
application’s life cycle cost can actually be reduced by using PHM. An analysis of the cost-contributing
activities to implement PHM are required; and cost avoidance factors should be integrated with risk
analysis to make business decisions about the implementation of PHM-based warranty.
4. Case Study: Apple’s liquid contact indicator causing problems
A research study from SquareTrade, Inc. found that 31% of iPhones failed in the first 22 months, of
which two-thirds were caused by user abuse or accidental damage [33]. Water damage accounted for over
25% of those [33]. When iPhones are damaged by liquid containing water, users often return their phones
to Apple retailers to seek repair or replacement under warranty. Apple must verify the cause of failure, as
its warranty does not cover user-induced damage.
Apple has explained that the personnel receiving the problematic device are often unqualified or untrained
to determine whether it has failed because of manufacturing defects or consumer abuse [34]. To identify
if a device has failed due to liquid contact, Apple has installed Liquid Contact Indicators (LCIs) to its
iPhones and other products. When water comes into contact with the edge of this small sticker, it changes
color permanently from white to red. Apple expects that the LCI will enable its personnel to determine
quickly whether the device suffers from water damage. It refuses to honor warranties on products with
triggered LCIs, presuming that the customer has caused the problem, and desires that the LCI—3M™
Water Contact Indicator [35]— prevents costly repairs and replacements due to customer abuse.
Nearly all smartphones and electronic devices are installed with internal liquid indicators, but Apple has
also installed external LCIs in the headphone jack and the bottom of the dock-connector housing of the
phone [36]. The problem is that the external LCIs may falsely indicate user-induced failure. If incorrect
indication occurs, both Apple and the customer will suffer. Currently, Apple is facing two class-action
lawsuits in the United States for its using the external sensors to deny warranty service [37]. The suits
6
arguing that Apple is “using the faulty technology to reach questionable conclusions” [35, 38]. The
plaintiffs allege the LCI can be triggered by humidity or temperature, and the warranty is “rendered
illusory” without verifying whether there is other evidence of water damage [37].
One of the problems of the LCI arises when the indicator triggers under high-humidity. A 3M test shows
one drop of water can turn half the tape red in one minute, and the indicator turns pink when exposed
continuously for seven days to a 55°C and 95% relative humidity environment [39]. However, according
to Apple, the indicator is triggered only when exposed to liquid. Another 3M test finds the indicator
changes color when exposes to condensing steam [40]. Leaving an air-conditioned building in locations
like Singapore and Hong Kong will often create condensation on the iPhone containing LCIs.
Condensation can also take place in many parts of the world during winter when one enters a warm
environment from the street, as many who wear glasses can testify. Additionally, the LCI may be
triggered during activities like workouts. There are some reports that the indicator was set off due to
sweaty palms when users took their iPhone to the gym to listen to music or use the calorie-counting
fitness application [41, 42]. If Apple lets a tiny amount of sweat ruins a warranty, it is almost impossible
to keep the warranty safe and perhaps unlikely to make the consumers satisfied.
Realizing that there are potential problems with the LCIs, Apple officially revised its internal support
policy on iPod water damage in November 2010 [43]. The changes require employees to look for signs of
damage beyond an activated LCI and would benefit those who may have had the LCI triggered
inappropriately. The policy makes no mention of the iPhone, but it's possible that Apple has modified its
stance for its smartphones as well.
Detecting customer-induced product failure is desirable for Apple and other companies to exclude costly
out-of-warranty requests. However, if the customer-abuse-detection system is applied improperly, it will
cause problems and cost the warrantor more for service under inconclusive data, lawsuits, and damaged
reputation.
Integrating PHM into warranties is a more reliable and effective customer abuse detection method.
Compared to the binary “yes-no” data about abuse events from Apple’s LCIs, a PHM system can provide
the overall usage trend in product life cycle to allow root-cause analysis for identifying if the failure is
under warranty. A new business practice of PHM-based warranty can be adopted to measure life cycle
loads, record an overall usage profile and time stamps of anomalous events, decrease warranty costs and
prevent lawsuit, and warn consumers of damaging conditions and protect the product. If a PHM-based
warranty is implemented effectively, companies can improve their warranty service, increase customer
satisfaction, and make more profit.
5. Conclusions
This paper addresses a unique perspective of PHM on product warranty. PHM will benefit the bottom line
of companies in the following ways: improving warranty service, reducing NFF returns, verifying
customer-induced failures, avoiding recalls and safety issues, and driving new warranty strategies.
PHM will reform warranty service and rewrite warranty policies. PHM can help understand what went
wrong with a product and provide proper service quickly; can aid manufacturers to make optimal
decisions to achieve active management of system health and reduce overall warranty cost; and can
change the current reactive warranty service to a proactive paradigm to decrease customers’ losses and
save time and money for manufacturers. By reducing NFF returns, verifying customer-induced failures,
avoiding recalls and safety issues, PHM can also reduce warranty cost and increase customer satisfaction.
Moreover, PHM has the potential to create new warranty strategies for companies to increase their
competition.
PHM is eventually expected to reduce warranty cost and bring manufacturers and suppliers more profit.
When implementing PHM-based warranty, investment and cost avoidance factors should be integrated
with uncertainty and risk to achieve an optimized business decision.
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6. References
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
Pecht, M., Establishing a Relationship Between Warranty and Reliability. IEEE Transactions on
Electronics Packaging Manufacturing, 2006. 29(3): p. 184-190.
Federal Trade Commission, A Businessperson's Guide to Federal Warranty Law. 1975 [cited
2010 Nov. 2]; Available from: http://business.ftc.gov/documents/bus01-businesspersons-guidefederal-warranty-law.
WarrantyWeek. Top 100 Warranty Providers of 2009. 2010 [cited 2010 July 1]; Available from:
http://www.warrantyweek.com/archive/ww20100401.html.
U.S. Department of Defense (DOD), MIL-HDBK 217, in Military Handbook for Reliability
Prediction of Electronic Equipment. 1965: Washington, DC.
Telcordia Technologies, Reliability Prediction Procedure for Electronic Equipment, in Special
Report SR-332, I. 1, Editor. 2001, Telcordia Customer Service: Piscataway, NJ.
Denson, W., A Tutorial: PRISM. RAC Journal, 1999: p. 1-6.
Guide, F., FIDES Guide Issue A, Reliability Methodology for Electronic Systems. 2004.
Chun, Y.H., Optimal Number of Periodic Preventive Maintenance Operations under Warranty.
Reliability Engineering and System Safety, 1992. 37(3): p. 223-225.
Jack, N. and J.S. Dagpunar, An Optimal Imperfect Maintenance Policy over a Warranty Period.
Microelecotronics and Reliability, 1994. 34(3): p. 529-534.
Yeh, R.H. and H. Lo, Optimal Preventive-maintenance Warranty Policy for Repairable Products.
European Journal of Operational Research, 2001. 134(1): p. 59-69.
Camci, F., System Maintenance Scheduling with Prognostics Information Using Genetic
Algorithm. IEEE Transaction on Reliability, 2009. 56(3): p. 539-552.
Jack, N., D.N.P. Murthy, and B.P. Iskandar, Comments on "Maintenance Policies with Twodimensional Warranty". Reliability Engineering and System Safety, 2003. 82(1): p. 105-109.
Murthy, D.N.P. and W. R.J., Modelling Two-dimensional Failure Free Warranties, in The Fifith
Symposium on Applied Stochastic Models and Data Analysis. 1991: Granada, Spain.
Murthy, D.N.P., I. B.P., and W. R.J., Two-dimensional Failure Free Warranties: Twodimensional Point Process Models. Operation Reliability, 1995. 43(2): p. 356-366.
Jung, K.M., M. Park, and D.H. Park, System Maintenance Cost Dependent on Life Cycle under
Renewing Warranty Policy. Reliability Engineering and System Safety, 2010. 95(7): p. 816-821.
Kalgren, P.W., et al. Defining PHM, A Lexical Evolution of Maintenance and Logistics, in IEEE
AUTOTESTCON 2006 Conference Record. 2006: Anaheim, CA.
Pecht, M.G., A Prognostics and Health Management Roadmap for Information and ElectronicsRich Systems. IEICE Fundamentals Review, 2010. 3(4): p. 25-32.
Pecht, M. and A. Dasgupta, Physics-of-Failure: An Approach to Reliability Product Development.
Journal of the Institute of Environmental Sciences, 1995. 38: p. 30-34.
Ganesan, G., et al., Identification and Utilization of Failure Mechanisms to Enhance FMEA and
FMECA, in Proceedings of the IEEE Workshop on Accelerated Stress Testing and Reliability
(ASTR). 2005: Austin, TX.
Pecht, M.G., Prognostics and Health Management of Electronics. 2008, Hoboken, NJ: John
Wiley & Sons, Inc.
Mishra, S., et al. Remaining Life Prediction of Electronic Products Using Life Consumption
Monitoring Approach. in Proceedings of the European Microelecrronics Packaging and
Interconnection Symposium. 2002: Cracow, Poland.
Kumar, S., et al., A Hybrid Prognostic Methodology for Electronics Systems, in IEEE World
Congress on Computational Intelligence. 2008: Hong Kong.
Cheng, S. and M. Pecht, A Fusion Prognostics Method for Remaining Useful Life Prediction of
Electronic Production, in The fifth Annual IEEE Conference on Automation Science and
Engineering. 2009: Bangalore, India. p. 102-107.
8
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
Rahman, A. and G. Chattopadhyay, Lifetime Warranty Policies: Complexities in Modelling and
Potential for Industrial Application, in The Fifth Asian Pacific Industrial Engineering and
Management Systems Conference. 2004: Gold Coast, Australia.
Chattopadhyay, G. and A. Rahman, Modelling Environmental and human Factors in
Maintenance of High Volume Infrastructure Components, in The Third Asian Pacific Conference
on System Integrity and Maintenance. 2002: Hilton, Cairns, Australia.
Jardine, A.K.S., D. Lin, and D. Banjevic, A Review on Machinery Diagnostics and Prognostics
Implementing Condition-Based Maintenance. Mechanical Systems and Signal Processing, 2006.
20(7): p. 1483-1510.
Allen, K. and J. Sturcke. Timeline: Toyota's recall woes. 2010 [cited 2010 June 27]; Available
from: http://www.guardian.co.uk/business/2010/jan/29/timeline-toyota-recall-accelerator-pedal.
Anderson, C. and G. Bluestein. Toyota Recall Class-Action Lawsuits Could Cost The Automaker
$3
Billion.
2010
[cited
2010
June
27];
Available
from:
http://www.huffingtonpost.com/2010/03/09/toyota-recall-classaction_n_491282.html.
CBS/AP. Toyota Recall Costing the Automaker Dearly. 2010 [cited 2010 June 27]; Available
from: http://www.cbsnews.com/stories/2010/01/29/business/main6153710.shtml.
Chattopadhyay, G. and A. Rahman, Development of Lifetime Warranty Policies and Models for
Estimating Costs. Reliability Engineering & System Safety, 2006. 93(4): p. 522-529.
Headquarters, Department of the Army, TM 5-698-2, in Technical Manual: Reliability-Centered
Maintenance (RCM) for Command, Control, Communications, Computer, Intelligence,
Surveillance, and Reconnainssance (C41SR) Facilities. 2006: Washington, DC.
Sandborn, P.A. and C. Wilkinson, A Maintenance Planning and Business Case Development
Model for the Application of Prognostics and Health Management (PHM) to Electronic Systems.
Microelectronics Reliability, 2007. 47(12): p. 1889-1901.
Austin Sands and Vince Tseng, S., Inc. (2009) One-Third of iPhones Fail Over 2 Years, Mostly
From Accidents. Volume,
Johnson, et al., Consumer Abuse Detection System and Method. United States Patent Application
20100312920, 2009.
101610, in Charlene Gallion. 2010, United States District Court, Northern District of California.
AppleSupport. iPhone and iPod: Liquid damage is not covered by warranty. 2010 [cited 2010
June 2]; Available from: http://support.apple.com/kb/HT3302.
Liu, I.J., Is Hong Kong so humid it could wreck your iPhone? 2010(Aug 22).
Service, C.N. iPhone Warranty Voided on Pretext, Class Says. 2010 [cited 2010 June 2];
Available from: http://www.courthousenews.com/2010/04/16/26463.htm.
3M (2004) 3M Water Contact Indicator Tape 5557.
3M (2003) 3M Water Contact Indicator Tape 5557 Condensing Steam Test.
Martin, D. Sweaty Workouts Killing iPhones? 2009 [cited 2010 June 16]; Available from:
http://reviews.cnet.com/8301-19512_7-10214153-233.html.
Klurfeld, M. Apple iPhone Abuse Detection Sensors: Who Is Abusing Whom? 2009 [cited 2010
June 16]; Available from: http://techgeist.net/2009/09/apple-iphone-abuse-detection-sensorsabusing-2/.
Lane, S. Apple adopts more thorough iPod water damage review policy. 2010. [cited 2010
November
15];
Available
from:
http://www.appleinsider.com/articles/10/11/09/apple_adopts_more_thorough_ipod_water_damag
e_review_policy.html
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