Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (March 2020)
1

Implementation of Knowledge-based RAM Analysis for
Improvement of Plant Availability and Economic
Efficiency

SHUNSAKU MATSUMOTO*1 NAOKO FUJIWARA*1
YASUHARU CHUMAN*2

One of the methodologies to implement product/service development strategies that would
efficiently boost product reliability, availability, maintainability and economic performance iscalled RAM (Reliability, Availability and Maintainability) analysis. We have been working on
finding solutions to improve the availability and economic performance of the various different
products that MHI Group produces, utilizing RAM analysis in collaboration with experts in thedesign, service and R&D departments. While we conduct RAM analysis on actual products, towiden the application range thereof, we have been working on the development of new
methodologies including a proprietary risk assessment framework. This paper introduces an
overview of RAM analysis, our new risk assessment framework and some examples of RAM
analysis applications.

|1. Introduction
In order to efficiently boost product reliability, availability, maintainability and economic
performance, it is necessary to identify the availability of the overall system and its high-risk
components, as well as to provide required solutions for these factors based on the design andservice in decreasing order of risk factors, in light of the assessment of the current situation incontrast with the target availability. RAM analysis is a methodology to implement product/service
development strategies.
We have been working on finding solutions to improve the availability of the various
different products that MHI Group produces, utilizing RAM analysis in collaboration with experts
in the design, service and R&D departments.
However, the generally-used RAM analysis, which only uses statistical failure rate data, isincapable of predicting the failure rate or availability based on the actual operation with respect to
new products with no such data yet or those with little operating time. This paper introduces an
overview of RAM analysis, which would maximize product availability and economic performance
while minimizing technical risks, our newly-developed risk assessment framework for providing
solutions to problems and some examples of RAM analysis applications.

|2. RAM analysis
RAM analysis is a system availability simulation based on drawings of equipment subject toassessment, i.e., piping and instrumentation diagrams, etc., utilizing the reliability block diagram
which is constructed taking into account the redundancy in various portions such as standby
systems of the system configuration components and partial-load operation, the specific failure
rates of individual components (blocks), the recovery time after breakdown and the probability
model and probability parameter of the breakdown maintenance cost. Figure 1 presents an example
of input and output in RAM analysis.
RAM analysis assesses, during a given period of time, the probability distribution of system
reliability, availability and maintainability of the product, as well as the risks that the system
configuration components carry, including the impact on overall availability, preventive
maintenance cost and breakdown maintenance cost, in terms of all components at the same time.
*1

Strength Research Department, Research & Innovation Center*2

Director, Strength Research Department, Research & Innovation CenterMitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
2This approach enables the finding of solutions for individual components in decreasing order ofrisk level, aiming to achieve the target product availability, across organizational boundaries suchas divisions, departments and domains, which would lead to the optimum allocation ofmanagement resources for design and services.
RAM analysis requires a large amount of data input, amongst which the failure rate is mostimportant and its prediction accuracy has a significant impact on product availability prediction andthe identification of high-risk components. Failure prediction models are roughly classified as (1)the failure data-driven model, (2) the failure physics model and (3) the newly-developed failure rate
correction model based on expert knowledge. The characteristics of RAM analysis utilizing thesemodels are explained below.

Figure 1

2.1

Example of input and output in RAM analysis

RAM analysis utilizing failure data-driven model

The components, such as valves and pumps, are used with similar specifications in many
products. Therefore, the statistical processing of failure rate using failure data would be relatively
easy. In terms of general components, operating data and failure data have been accumulated
throughout the world, part of which is publicly available as failure rate databases. Many public
failure rate data bases provide statistical failure rates of components in numerical tables, which
offer the advantage that RAM analysis can be conducted in a relatively short amount of time aslong as a reliability block diagram of the system subject to assessment is created. Accordingly,
RAM analysis in general refers to the method using this model.
The public failure rate data is the statistically-processed actual number of operating hours
and breakdowns, which would provide the average failure occurrence of the component. On theother hand, actual components have different conditions such as the design safety factor,
manufacturing quality, maintenance/operation conditions and usage environment, depending on theindustrial field, manufacturer, user country and operator, resulting in different failure rates. When
applying RAM analysis to actual products, if a public failure rate database is utilized, the predicted
system availability and high-risk components are very likely to be different from how designers
and experts actually feel or the failure tendency of the actual machine. Furthermore, public failure
rate databases are based on the condition that downtime intervals of the actual machines follow the
exponential distribution (random failures) where the assessment of chronological changes in the
failure rate is unavailable.
Meanwhile, manufacturers and operators have for many years accumulated operational andmaintenance data on the product subject to assessment and its chronological failure/survival record,
which would enable the embodiment of a real-life failure assessment and corresponding solutions
tailored for the product, including the analysis of various failure patterns such as initial failures,
random failures and deterioration caused by aging (wear-out failures).

Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)3

2.2

RAM analysis utilizing failure physics modelA statistical failure rate is not available for components with little operating time or few
instances of failure, such as large-scale structures or customized components. In such cases, theprobability distribution of the service life and failure probability are calculated based on the failure
physics model of the component and the uncertainty propagation using the input random variables.
This assessment utilizes, in addition to knowledge about the product design, various different
methods and data such as load/environmental conditions for different types of products, analysis
procedures for individual production methods, structure/element testing expertise and data,
online/offline measuring techniques of actual machines and response/damage prediction methods
utilizing actual measurements. Figure 2 is a diagram of RAM analysis utilizing the failure physics
model. The limit state assessment of individual components comprehensively evaluates the random
uncertainty and epistemic uncertainty affecting the load, response and strength of the component,
where the chronological failure rate (service life distribution) is obtained.
The probability distribution of the component’s service life calculated in this assessment will
be used as input data in RAM analysis, just like the failure rate. Especially with respect to wear-out
failures, quantifying the service life distribution and failure probability of the component would
enable designs based on reliability and risks (=PoF (Probability of Failure)× CoF (Consequence ofFailure)) such as RBDO (Reliability Based Design Optimization), as well as support for
decision-making on services such as RBM (Risk Based Maintenance).

Figure 2 Schematic Diagram of failure physics model in
RAM analysis

2.3

Knowledge-based RAM analysis

2.3.1 Development overview
New products with new designs/structures have no statistical failure data. They have greater
uncertainty in the failure mechanism and damage rate compared with mature products. Under suchcircumstances, not only is quantitative data an important source of information, but so are
qualitative risk assessments by designers and experts. However, knowledge on failures experienced
by the designers and experts in the past and their causal analyses tends to largely depend on theirmemories and subjective views of individuals, and is very likely to vary greatly from person toperson.
Accordingly, we expanded existing trouble-preventing frameworks such as FMEA (Failure
Mode and Effect Analysis) and DRBFM (Design Review Based on Failure Mode) and constructed
a new FMEA framework for RAM analysis which would assess the product risk in different failure
patterns such as initial failures, random failures and wear-out failures. Figure 3 depicts part of a
FMEA sheet for RAM analysis.

Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
4This framework first breaks down the configuration components into parts and major failuremodes (STEP 1). Then the presence or absence of the novelty and specialization which would
increase the failure risk is examined in terms of the individual failure modes from the perspectiveof structure, material, environment, operating method, etc. (STEP 2). Next, in terms of individual
parts and failure modes, 3 failure patterns, i.e., initial failures, random failures and wear-out
failures, are assessed (STEP 3) prior to assessing the impact on the system in the case of failures
such as MTTR (Mean Time To Repair) after a failure and the system output capacity during
downtime (STEP 4).
Furthermore, the most significant characteristic of this method is that designers and experts
assess, with respect to components with high novelty, the level of influential factors corresponding
to initial failures, random failures and wear-out failures, according to which a probability parameter(hereinafter simply called “values in the risk matrix”) is given in a risk matrix, where the failure
rate is re-assessed (STEP 5). The details of this assessment will be described in “Failure rate
prediction in risk matrix” below.
Subsequently, the spare parts that would become necessary in the case of a failure and theirinventories, as well as information such as delivery dates, are extracted (STEP 6). Lastly, in light of
the latest design updates and outcomes of design/R&D, the residual risks in the individual failuremodes are assessed (STEP 7).
This process, in which the knowledge of designers and experts is quantified and reflected in
RAM analysis while securing transparency and consistency using the FMEA sheet for RAM
analysis, is called knowledge-based RAM analysis. Especially in terms of the availability
prediction provided by this knowledge-based RAM analysis and the Pareto chart illustrating
high-risk components for new products, it has been confirmed that they are close to the actual
situation of real system and what designers and experts feel, compared with general RAM analysis.

Figure 3

Risk assessment on FMEA sheet for RAM analysis

2.3.2 Failure rate prediction in risk matrix(a) Initial failures
Initial failures are likely to happen due to the combination of insufficient experience andexcessive loads including faulty designs (incorrect choice of materials, mistaken prediction of
responses, etc.), manufacturing failures (material defects, improper thermal treatments, etc.)
and flaws in operational procedures. Therefore, as shown in Figure 4, the maturity and severity(i.e., loads, temperature, environment, etc.) of the product are classified into 3 levels where theinitial failure occurrence is assessed.
The product maturity is classified into (L1) mass-produced with a proven track record,(L2) not mass-produced but with a proven track record and (L3) not mass-produced without atrack record. The severity is classified, in terms of load as an example, into (L1) low load, (L2)medium load and (L3) high load. Values in the risk matrix are shape parameters in accordancewith the Weibull distribution (<1.0), where the greater the assessment level is, the smaller the
shape parameter becomes, which means that the initial failure rate is assessed high.
(b) Random failures
Random failures are likely to happen due to random stresses on the system or its harsh

Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
5handling. As shown in Figure 5, the frequency of abnormal loads and their magnitudes
assumed by designers and experts are assessed according to 3 levels to assess the random
failure rate.
The frequency of abnormal loads is classified into (L1) once/20 years, (L2) once/year and(L3) once/month, whereas the extent is (L1) no impact, (L2) system fails after a few times and(L3) system fails after once occurrence. Values in the risk matrix are the weight factors of thefrequency of occurrence against the statistical failure rate of a similar mature product, where thehigher the assessment level is, the greater the weight factor becomes, which means the random
failure rate is assessed high.

Figure 4 Initial failure risk assessment with risk matrix (The values in
the chart are ficticious.)

Figure 5 Random failure risk assessment with risk matrix (The values
in the chart are ficticious.)(c)

Wear-out failures
Damage in wear-out failures (fatigue, creep, mechanical wear, corrosion, etc.)
accumulates gradually compared with initial failures and random failures. Therefore, based on
the predicted damage progress rate, some measures of preventive maintenance such as
time-based maintenance or condition-based maintenance are undertaken to prevent failures.
Accordingly, the assessment of wear-out failures is, as shown in Figure 6, classified into
3 levels, (L1) damage progresses as predicted, (L2) damage progresses faster than predicted due
to specific causes and (L3) damage progresses faster than predicted without any known cause,
where the gap between the predicted damage progress rate and the actual speed, as well as thecorresponding factor analysis are combined with certainty factors.
Wear-out failures are assessed only when there is examination/measurement data
available from a demonstration facility which is very similar to the product. Values in the risk
matrix are variation factors of the damage progress rate where the higher the assessment levelis, the greater the variation factor becomes, which indicates high uncertainty in the damage
progress rate.

Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
6

Figure 6 Wear-out failure risk assessment with risk matrix (The values
in the chart are ficticious.)

2.3.3 Parameter setting in the risk matrix
The values in the risk matrix are set in accordance with either (1) performance-weighted
assessment method or (2) system reliability calibration method(1). In performance-weighted
assessment method, the numerical assessments in the risk matrix conducted by individual experts
are assessed by assigning a weight to each of them according to the individual failure rate
prediction accuracy. On the other hand, system reliability calibration method, as shown in
Figure 7, the values so that the central tendency (mean, median, mode, etc.) in the system
reliability predictive distribution calculated by RAM analysis would be consistent with therepresentative reliability value. Both methods are designed to form a reasonable consensus between
the parties just like the failure rate prediction accuracy in individual components.

Figure 7 Calibration in risk matrix utilizing availability data ofactual machine|3. Measures for improved availability and optimum service
investment
Many large products/mechanical systems include a number of various different components,
being handled by a broad range of design/service departments. Therefore, the decision-making forthe optimization of the overall system requires a great deal of coordination between people, causing
an increase in man-hours and longer review periods. Accordingly, some examples ofdecision-making for designs/services utilizing RAM analysis are provided below.

3.1

Visualization of the balance between preventive maintenance cost andbreakdown maintenance cost

The statistically-representative value of the breakdown maintenance cost for system
configuration components calculated by RAM analysis (expected values, percentile, etc.) and thepreventive maintenance cost obtained by collecting and analyzing field data and maintenance plandata (costs for inspection, repair and replacement of the component) are plotted on the horizontal
axis and vertical axis, respectively, where the balance between the breakdown maintenance costand preventive maintenance cost of each component is relativized and visualized. Figure 8 gives anMitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
7

example of the visualization of the balance between the expected preventive maintenance cost andbreakdown maintenance cost.
Accordingly, configuration components are classified into 4 major quadrants, (1)components with an excess maintenance tendency where the preventive maintenance cost is
relatively greater than the breakdown maintenance cost, (2) components with anunder-maintenance tendency where the breakdown maintenance cost is relatively greater than thepreventive maintenance cost, (3) components for which comprehensive improvement in design,operating process and service needs to be considered where both the breakdown maintenance costand preventive maintenance cost are relatively high and (4) components with relatively low
maintenance costs, based on which we can move on to examining practical measures in each
aspect.

Figure 8 Visualization of balance between preventive maintenance cost andbreakdown maintenance cost

3.2

Strategic technical development/maintenance menu for improved availability
and reduced maintenance cost

Discussions on the improved availability and reduced breakdown maintenance cost utilize
the Pareto chart demonstrating the impact on the availability of individual components, breakdown
maintenance cost, etc., and examine possible measures to achieve the target availability andbreakdown maintenance cost in descending order of the component risk level until the targets areachieved, while taking into account the budget constraints and effects of the measures.
Upon examining practical measures for high-risk components, in addition to the current
values in RAM analysis (statistical failure rate, failure probability and average recovery time),
impacts on the system calculated by RAM analysis (expected annual downtime of the system due
to failures of components subject to assessment, etc.), proposed necessary practical measures, aswell as the details of actual efforts currently made by the design/service/R&D departments, areindicated (Table 1), which would facilitate further technical discussions and reviews on the
proposed measures on a substantive level between experts across different departments such as
design, R&D and service.
Table 1 High-risk components and updated measures extracted by RAM analysis
Risk
Rank

Component

1

Superheater
tube

2

Soot blower

3

Fire brick::

Main failure mode
Reduction of thickness
Leakage
Corrosion
Creep
Leakages
Corrosion
Demand failure
FailExpansion
:

MTBF
[hrs]
AAAAA

MTTR
[hrs]
aaa

Risk
evaluation[hrs/Yr]Countermeasures
Research & Development
progress

X

New materials
Preparing spare parts
New design based on detailed
FEM and strength assessment

BBBBB

bbb

Y

CCCCC

ccc

Z

:

:

:

New design based on detailed
FEM and strength assessment:

Mitsubishi Heavy Industries Technical Review Vol. 57 No. 1 (June 2020)
8|4. Conclusion
We have been working on RAM analysis and finding solutions to improve the availability ofthe various different products that MHI Group produces, in collaboration with experts in thedesign, service and R&D departments.
RAM analysis assesses the product availability and risks of individual components across all
the equipment in one-shot simulation. The integration of this risk assessment technology for entire
products and technologies and knowledge concerning individual components in this manner isdesigned to improve the product availability and to minimize the technical risks for many products.
Furthermore, in terms of challenges that companies face such as improvement in theavailability of new products and a reduction in technical risks, we have expanded the application
range of methodologies and introduced ways to perform them by developing our new risk
assessment framework that expands on conventional RAM analysis.
In the future, throughout the service life of a product, we will reflect in RAM analysis thetechnologies and knowledge obtained from the measurement data of actual machines and technicaldevelopment, continually review availability and high-risk components and solve problems for ourcustomers, proposing updated measures and new solutions.

References(1)

Shunsaku Matsumoto et al, Technology Risk management and Solution for Plant Availability using RAM
Analysis, The Ninth Japan Conference on Structural Safety and Reliability Symposium JCOSSAR 2019，(2019) p. 366-371