White Paper

Predictive and Prescriptive
Maintenance in theContext of Automotive
Functional Safety

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Table of Contents
Terms and Definitions ................................................................................................ 3
1. Introduction ............................................................................................................... 4
2. FR Calculation Based on TTF Prediction ....................................................................... 5
3. FR Reduction by Predictive Maintenance..................................................................... 9
4. Failure Rate and Prescriptive Maintenance................................................................ 11
5. Conclusions and Outlook ........................................................................................... 14

AcknowledgmentproteanTecs would like to thank Antonio Priore for his review and feedback.

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Terms and Definitions
AcronymDefinitions

Comment

ASIL

Automotive Safety Integrity Level

see ISO 26262:2018-1

DCDiagnostic Coverage

see ISO 26262:2018-1

EFR

Early Failure Region

see section 2

FIT

Failure In Timea unit that represents failure rates andhow many failures occur every 109
hours

FR

Failure Ratethe frequency with which anengineered system or component fails
expressed in failures per unit of time

HTOL

High-Temperature Operating Lifea reliability test

IFR

Intrinsic Failure Region

see section 2

LFM

Latent Fault Metricsee ISO 26262:2018-1

MTBF

Mean Time Before Failure

the average time between inherent
failures of a mechanical or electronicsystem, during normal system
operation

PMHF

Probability Metric for (random)
Hardware Failuressee ISO 26262:2018-1

RUL

Remaining Useful Lifethe length of time a machine is likely
to operate before it requires repair or
replacement

SPFM

Single-Point Fault Metricsee ISO 26262:2018-1

TTF

Time To Failureamount of time an asset operates
before it fails, equivalent to RUL

WFR

Wear-out Failure Region

see section 2© proteanTecs 2022. All rights reserved.

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1 Introduction
Reliability and safety are crucial features of automotive platforms, and semiconductor chips usedin these architectures should comply with the applicable requirements mandated by ISO 26262
series of standards. The functional safety requirements are derived from the safety goals to reach“the absence of unreasonable risk due to hazards caused by malfunctioning behavior ofelectrical/electronic systems.” In the standard, the malfunctions are classified as systematic
failures1 and random hardware failures. In this white paper, we consider the second type, which
represents the failure appearing during the lifetime of the system due to random defects innate
to the manufacturing or caused by operational conditions and usage. In addition, we describe theapplication of the time-to-failure (TTF) predictions in the context of estimation of the failure rate
(FR), the subsequent improvement of the reliability of an electronic device, and the benefits ofpredictive and prescriptive maintenance methods.
The net benefits of TTF predictions, embodied as software applications for predictive andprescriptive maintenance, are presented in the context of electronic device reliability metrics:
failure rate reduction, diagnostic coverage improvement, and longer operational lifetime. These
three key performance indicators (KPIs), whose limits are defined in functional safety standards
for mission-critical applications, are shown to be enhanced by the proposed methodology.
Upon TTF prediction of incoming permanent field defects, the predictive and prescriptive
maintenance applications provide the actional insight to keep the fail-safe state of the system,
which is fundamental for achieving the highest functional safety levels.
This white paper covers the following: Base failure rate calculation with TTF: For a given number of device samples, we consider
not only the field failures in the cumulative failure fraction but also the predicted failures,to calculate the parameters of a Weibull instantaneous failure rate function, i.e., thefailure rate of the intrinsic and wear-out regions of the reliability bathtub curve.
 Reducing the failure rate by using predictive maintenance: Assuming we use the TTF
prediction for predictive maintenance, we can remove a device from the field before its
failure occurs. The cumulative failures are reduced and the number of device samples as
well, with the impact of reducing the overall failure rate.
 Extending lifetime by prescriptive maintenance: As the TTF prediction forecasts an
expected failure within the useful lifetime of the device, the prescription ofcounteractions can postpone the loss of operations. The benefits of prescriptive

1 These failures are deterministically induced by incorrect or flawed processes during development, manufacturing,or maintenance.

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maintenance are shown in terms of a reduction of the failure rate in the operational
lifetime, or conversely postponing the beginning of the wear-out region.

2 FR Calculation Based on TTF Prediction
The reliability curve of an electronic device assumes the so-called bathtub shape with steep sides
and a flat center. It includes three parts2: (1) early failure rate (EFR) region, the first part is adecreasing failure rate; (2) intrinsic failure rate (IFR) region, the second part is an almost constantand much lower failure rate; and (3) wear-out failure rate (WFR) region, the third part is anincreasing failure rate.
The first region EFR is driven by gross defects, which are normally screened out by testing before
shipment. Later in the field during the useful life we assume the failures are due to either (a)latent defects escaping the burn-in tests, (b) random defects or soft errors, such as transient
faults caused by sub-atomic particle strike, and (c) material wear-out defects or failures causedby aging.
In this paper, we are not going to consider further the failures screened at production but focus
on the field failures after it. Nonetheless, it is worth mentioning that a sensitive outlier detection
mechanism is expected to reduce the number of unscreened latent defects which escape theproduction3. Furthermore, we are going to discuss the permanent errors and defects, not thetransient ones.
By knowing the cumulative fraction of defective devices, it is possible to plot the number of
failures vs. time in a Weibull chart and extract the characteristic Weibull parameters for shape() and scale (t63). The Weibull failure rate or hazard function:𝛽 𝑡 𝛽−1
𝜆(𝑡) =( )𝑡63 𝑡63
In practice, we must consider the three regions as independent contributors and calculate theWeibull parameters using an individual liner fit in a log-log scale.
In Figure 1, there is an example of the Weibull chart for a hypothetical set of data described inTable 1.

2 Reference chapter 7 of J. W. McPherson “Reliability Physics and Engineering”, 2 nd edition, Springer.
3 See reliability tests described in “Quality and Reliability Manual” ISSI, chapter 3. For example, PAT Part Average

Testing https://www.issi.com/WW/pdf/q_r_manual.pdf.

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5 of 15TABLE 1
Observed failure data for 5000 devices in total.
Observed FailuresTime1(year)
N° of1
failures

2

3

4

5

6

7

8

9

10

2

2

2

2

2

2

5

15

25

Weibull chart
1

10

-4.5
-5.0

wearouty = 9.9023ln(x) - 27.422

intrinsic

ln(-ln(1-F))

-5.5
-6.0

Log. (wearout)Log. (intrinsic)
y = 1.3024ln(x) - 8.4118

-6.5
-7.0
-7.5

-8.0
-8.5

time (years)

Figure 1: Weibull chart of the IFR and WFR regions.

We can extract from the chart the shape factor  as the slope of the logarithmic fit, and the scale
factor t63 as the intercept with the y-axis.
Let us consider the following two methods to calculate the reliability of a device: (a) collecting
the empirical field data from the operational life of the device, or (b) accelerating the time-tofailure process increasing the operating conditions.
Assuming we can predict by when a device under observation is going to fail by monitoring theperformances (also known as TTF). At any moment of the device’s lifetime, we can forecast the
cumulative failure fraction distribution. In other words, the concept is to estimate the Weibull
parameters by combining the observed and the predicted device failures.

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As an example, let us assume that we are observing the same 5000 devices as described in Table
2. However, instead of waiting 10 years to collect the field data, we can use the TTF prediction at
an earlier moment, for example after four years. The failures after the fourth year are forecasted
by knowing the remaining time before the failure of the device. In other words, TTF determinesin which column of the table we count the predicted failure.
As in any field-data analysis, how to split the data between the intrinsic and wear-out regions is
an arbitrary choice, based only on the observation of a rapid increment of the number of failures.
In the table example, we arbitrarily decided that the wear-out region starts when the predicted
failures are double the average of the precedent years.
TABLE 2
Observed and predicted failure data for 5000 devices in total.
After the fourth year, the data is predicted.
Observed and Predicted Failures
Time (year)1
2
N° of observed
failuresN° of predicted
intrinsic failures
N° of predicted
wear-out failures

1

2

3

4

2

2

5

6

7

2

2

2

8

9

10

4

16

23

We assume uniform data across time for the latent and random defects in the constant failure
rate region, therefore the observed and predicted numbers shall be consistent and continuous,even if the observable degradation in performance could be very quick and without prior notice,therefore difficult to be predicted with accuracy.
On the contrary, the wear-out effects are much slower and easier to be observed with the proper
coverage of data. In this case, the predictions are typically more accurate, and several techniques
are available, including model-based, analytical-based, or knowledge-based4 techniques.
If we consider a reliability test such as high-temperature operating life (HTOL), it will determine
the expected failure rate during the operating conditions. The JEDEC 22-A108 standard defines
the minimum amount of devices that must be considered and the acceptance criteria (maxnumber of accepted failures)5. The survivor devices in a reliability test may be on the verge offailure, but it would not be visible in the traditional pass/fail metrics. Using parametric

4 For a survey of the techniques, "Overview of Remaining Useful Life Prediction Technologies.” C. Okoh et al. /Procedia CIRP 16 (2014) 158 – 163.
5 See https://www.jedec.org/sites/default/files/docs/22A108D.pdf© proteanTecs 2022. All rights reserved.

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measurements of the performances and conditions of the device, it will be possible to estimate
the incoming TTF and increase the confidence of the reliability assessment.
In this section, we have covered: The reliability curve and the best-known methods to estimate the characteristic
parameters of this curve, i.e. failure rate calculation.
 Failure rate estimation of a device by considering the predicted failures TTF to anticipate
the field data of an uncomplete data set, such as after a few years of a new device in themarket and much before the device's end of life. The early availability of such data canplay a key role in the quantitative functional safety analysis (e.g. FMEDA) of new systems,
which use new technologies for the first time and are not extensively proven in the fieldyet.

Figure 2: Chart of parametric data of device health vs. lifetime in hours (taken from "Overview of
Remaining Useful Life Prediction Technologies.” C. Okoh et al.). The degradation over time of the healthindex can be used to estimate the remaining useful life (shown as RUL in the picture) before a failure.
RUL and TTF are equivalent in our case.

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3 FR Reduction by Predictive Maintenance
The ISO 26262 series of standards defines four different Automotive Safety Integrity Levels (ASIL),
as shown in Table 3. Depending on the ASIL, the hardware architectural metrics should be
calculated and fulfilled, including single-point fault metric (SPFM), latent fault metric (LFM), andprobabilistic metric for hardware failure (PMHF), which defines the failure rate target of thesystem, or of the hardware element of the system.
TABLE 3
ISO 26262 Automotive Safety Integrity Levels (ASIL)
ASIL
A
B
C
D

SPFM
Not relevant
≥ 90%
≥ 97%
≥ 99%

LFM
Not relevant
≥ 60%
≥ 80%
≥ 90%

PMHF
< 1000 FIT< 100 FIT
< 100 FIT
< 10 FIT

Under the term “predictive maintenance”, we consider all the techniques which are designed toforecast the root cause of an incoming in-service device failure and when it will happen. The main
promise of predictive maintenance is to allow convenient scheduling of maintenance services or
preventive actions and to counteract potentially safety related device failures and operational
downtime. In this regard, a predicted failure never develops into an actual failure, which can beconsidered in the field data of the reliability studies. We propose to remove such devices fromthe field data, reducing both the number of observed devices and actual failures.
In Table 4, we present a numerical example. We assume that the predictive maintenance iscapable to detect k=50% of the imminent faults, which can be both intrinsic/latent or wear-out
failures. We also assume that there is a false-positive rate of h=10%, which can also spoil thequality of the results. Namely, if F is the real number of devices that are going to fail in the futureof a sample size of N devices, the total number of predictions P is equal to:
Predicted failures, P = (k*F + h*(N-F))
Table 4 shows the hypothetical results of an application characterized by 50% detection and
h=10%. At every yearly prediction, the predicted devices were removed from the field whether
the prediction is correct or not, reducing the number of observed failures.

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9 of 15TABLE 4
Observed failures with and without predictive maintenance.
Observed Failures and Predictive MaintenanceTime (year)12
3
N° of observed
failures without
predictive
maintenanceN° of observed
failures withpredictive
maintenance

4

5

6

7

8

9

10

1

2

2

2

2

2

2

5

15

25

1

0

2

1

1

2

1

2

8

13

In the example proposed, we observe that the shape parameters are not dramatically changed
as we expected, but the scale parameter of the IFR region of the case with predictive
maintenance is twice bigger in comparison to the case without prediction.
As a result, if we set the reliability requirement with a limit of 100 FIT, our example shows thatthe useful lifetime of the device is extended from almost 7 years to 8 years. Alternatively, theresidual failure rate in the useful lifetime area is significantly improved by 20-50% FIT, which is aremarkable reliability improvement. See Table 5 for the Weibull parameters and Figure 3 for the
reliability curves.
Table 5
Weibull parameters with and without predictive maintenance.
Parameters
Intrinsic FR region
Wear-out FR regionbt63 (years)bt63 (years)© proteanTecs 2022. All rights reserved.

Without predictive
maintenance
1,302
638
9,902
15,9

With predictive
maintenance
1,196
1585
11,00816,1

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Failure Rate
300

w/o predictive
maintenance
200

FIT

with predictive
maintenace
100

0
0

1

2

3

4

5

6

7

8

9

10

Years

Figure 3: Reliability chart shows the combination of the three contributors, early, intrinsic, and wear-out
regions for both cases, with and without predictive maintenance.

In this section, we have covered: The ability to reduce the in-service failures from the field data plays the same benefit asa preventive action, positively improving the robustness, reliability, and intrinsic failurerate.

4 Failure Rate and Prescriptive Maintenance
Prescriptive maintenance is a concept that collects and analyzes data to produce specificrecommendations to reduce operational risks.
At the core of both predictive and prescriptive maintenance, there is the use of sensors to collectdata about the conditions and performances of a device, to monitor their behavior and evolution
in time, and the ability to forecast their degradation to anticipate the occurrence of a fault.
If predictive maintenance aims to prevent unexpected failures by repairing or removing thedevice promptly, the prescriptive concept recommends actions to change the future outcome byadapting the operational conditions of the device.

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Some of the random hardware faults occurring in the intrinsic failure region are difficult to bepredicted, if at all6. Therefore, it is even more difficult to avoid their outcome by changing theoperational conditions. On the other hand, the aging of the device is typically a time-dependent
effect and can be modeled as a continuous degradation of observable KPIs. By parametricmeasurements of characteristic health or performance indicators, it is possible to observe theimpact of wear-out and predict their trend over time.
The operational state of a device determines the device stress, therefore the aging speed.
Let us consider what we can do to reduce the operational workload of a device in a given momentof its life, thus reducing the overall stress.
It may not be possible to influence the external contributors, such as the ambient temperature,
which may be fully independent and outside of our control. However, we may influence the otheroperational conditions impacting the operational state of the device, such as voltage andfrequency, and the software application in execution.
For example, we can change the software application utilization of hardware by restricting thenumber of software processes in execution or disabling non-essential jobs, or, for a computerarchitecture cable of dynamic voltage scaling, we can constrain the overvolting or minimum
frequency. There are two main consequences—risk of failure reduction and performance drop.
Such a strategy is similar to the so-called “limp-home mode” used in any modern electronic
controlled combustion vehicle, when, in case of failures, the throttle is set to fast enough to get
the transmission but not so fast that driving may be dangerous. The system’s safe state ismaintained at the cost of a partial reduction of the system availability.
In Figure 4, we are considering the same example as discussed in the previous section on FR
Reduction by Predictive Maintenance In this case, the wear-out faults are delayed because we
assume a reduction of the operational stress as soon the degradation is below a criticalthreshold to choose arbitrarily. If the reliability requirement is defined as FR<100 FIT, theresulting useful lifetime is extended from 8 to 9 years.

6 i.e. soft error caused by secondary particles from cosmic rays.

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Failure Rate
300

w/o predictive
maintenancewith predictive
maintenace

200

FIT

with prescriptive
maintenance
100

0
0

1

2

3

4

5

6

7

8

9

10

Years

Figure 4: The operational lifetime is defined by three main contributors: (1) The latent defects left
unscreened after manufacturing, such as burn-in tests. (2) Random external events that can critically
impact the device and cause an internal failure, such as alpha particle changing memory bits orelectromagnetic interference (EMI) influences. (3) Early degradation of devices, which depends on how far
is the average lifetime from the end of the operational lifetime and how wide is the standard deviation of
the probability distribution. The duration of the operational lifetime in green depends on how long the
failure rate of the system remains below the safety target, for example, FR<100 FIT. We can notice how
there is a notable extension of the useful lifetime with predictive and prescriptive maintenance as the FR
remains lower 100 FIT for a much longer period.

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5 Conclusions and Outlook
Predictive and prescriptive maintenance are extremely promising approaches for preventing
system breakdowns and unnecessary maintenance, nevertheless not exclusively in these regards.
In this white paper, we presented the benefits of such techniques in the context of functionalsafety, under the premise that the ability to forecast hardware failure is not only a reliability or
quality KPI, but it plays a crucial role in keeping the system in its safe state.
In the latest edition of ISO 26262:2018-1, a safety mechanism is defined as a “technical solution
implemented by E/E functions or elements, or by other technologies, to detect and mitigate or
tolerate faults, or control or avoid failures, in order to maintain intended functionality, or achieve
or maintain a safe state.” In our opinion, the keywords here are failure avoidance and maintaining
a safe state. Yet, in the current edition of the standard for Functional Safety for Road Vehicles,
the intention of the authors seems to focus on reactive safety by detection of a fault, rather thanproactive safety by anticipating its occurrence.
Let us consider the case of a hardware device, that according to the required metrics of thestandard, is classified as ASIL-B out-of-context. As described in Table 3, the failure rate is under
100 FIT (PMHF), and it has a 90% coverage by the safety mechanisms to prevent risk from singlepoint faults in the hardware architecture (SPFM). What if, by the combination of predictive andprescriptive mechanisms, we would be able to reduce the actual failure rate below 10 FIT? By thecurrent definitions, we would not be able to classify the system element at a higher ASIL, because
the prognostic coverage (also known as the proportion of the hardware element failure rate thatis forecasted and anticipated by the implemented predictive or prescriptive maintenance7) does
not contribute to the SPFM or PMHF metric.
The increased use of predictive and prescriptive maintenance methods could lead the industrial
community to consider one of the following two proposals, or similar:
1. Define separate hardware architectural metrics dedicated to predictive andprescriptive maintenance coverage and account for them in the final PMHF
calculation. For example, this could be considered in the development of the next
version of ISO 26262.
2. Consider predictive and prescriptive maintenance in the calculation of the intrinsic
failure rate ahead of SPFM, LFM and PMHF calculations. This proposal could be
adopted today and could benefit those projects where, given higher complexity
and area, minimum targets of SPFM and LFM still does not allow to meet the
PMHF requirement for the system.

7 A careful reader would notice that the definition of the prognostic coverage is modeled upon the diagnostic

coverage definition 3.33 of the ISO 26262-1 standard.

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Visibility from within electronics is an important trend in automotive, as in other domains,
where IoT, connectivity, and the high number of sensors and data are becoming available andconvenient. Continuous, in-mission mode health and performance degradation monitoring are
the key elements for offering proactive safety.
As in the past, it is expected to be a virtuous circle. As the technology is available, the limits are
updated to push the industry into the adoption of the most advanced technologies and to
sharpen the reliability requirements of standards and legislation.

References
•
•
•
•
•
•

A. Evelyn Landman, Alex Burlak, C. Nir Sever and D. Marc Hutner, “Applying Universal
Chip Telemetry to Detect Latent Defects and Aging in Advanced Electronics”, 2022 IEEE
International Reliability Physics Symposium (IRPS)
J. W. McPherson, Reliability Physics and Engineering Time to Failure Modeling, 2nd
Edition, Springer, 2013
J. W. McPherson, Degradation Modeling, 2017
E. Landman et al., "Degradation Monitoring from a Vision to Reality," 2019 IEEE
International Reliability Physics Symposium (IRPS), 2019, pp. 1 4, doi: 10.1109 /2019.8720527
Shidhartha Das, IEEE Solid-State Circuits Conference 2022
Okoh et al., “Overview of Remaining Useful Life Prediction Techniques in Through-life
Engineering Services”, December 2014, Procedia CIRP 16:158–163© proteanTecs 2022. All rights reserved.

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