what do you consider to be the number one priority of this company

Failure Modes and Effects Analysis

D.R. Kiran , in Total Quality Management, 2017

26.8 Hazard Priority Number

Risk priority number ( RPN) is a function of the three parameters discussed above, viz, the severity of the effect of failure, the probability of occurrence, and the ease of detection for each failure fashion. RPN is calculated by multiplying these iii numbers as per the formula below,

$RPN=\mathrm{Southward}\times P\times D$

where S is the severity of the upshot of failure, P is the probability of failure, and D is the ease of detection.

RPN may not play an important role in the choice of an activeness against failure modes, just will help in indicating the threshold values for determining the areas of greatest concentration. In other words, a failure style with a loftier RPN number should be given the highest priority in the assay and corrective action. The relationship betwixt the to a higher place mentioned parameters of FEMA may be represented as in Fig. 26.three.

Fig. 26.3. The five basic steps of FMEA.

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Maintainability Tools

B.South. DHILLON , in Engineering Maintainability, 1999

Criticality Assessment

This cess ranks potential failures identified during the system analysis based on the severity of their effects and the likelihood of their occurrence. The two methods near frequently used for making a criticality assessment are hazard priority number (RPN) method and military standard method.

Risk Priority Number Method

This technique, commonly used in the automotive manufacture, bases the risk priority number for an item failure mode on three factors: probability of occurrence, the severity of the failure's effects, and probability of failure detection. The probability of occurrence is the likelihood of failure, or relative number of failures, expected during the detail's useful life. Tabular array iv.ane describes the rankings of probability of occurrence [7]. The severity of result of an item's failure is the consequences it will have for the next highest level of the system, the organisation as a whole, and/or the user. Tabular array 4.2 describes the rankings of severity of consequence [7]. The probability of failure detection is an assessment of the proposed design verification programme's ability to observe a potential problem earlier the detail involved goes into production. Tabular array four.three describes the rankings of probability of detection [7].

Table 4.1. Rankings of Probability of Occurrence and Associated Descriptions

Clarification of Ranking	Probability of Occurrence	Rank
Very high (the failure is very likely to occur	1 in 2	10
Very high	ane in 8	nine
Loftier (the failure will occur often)	1 in 20	eight
Loftier	1 in xl	7
Moderate (the failure will occur occasionally)	one in eighty	6
Moderate	1 in 400	5
Moderate	i in 1,000	4
Depression (the failure will rarely occur)	1 in 4,000	3
Low	1 in 20,000	2
Remote (the failure is unlikely to occur)	<1 in 106	1

Table 4.ii. Rankings of Severity of Failure Upshot and Associated Descriptions

Level of Severity	Rank
Very high (the failure will impact safe product functioning)	9, 10
High (there volition exist a high caste of customer dissatisfaction because of the failure)	7, 8
Moderate (the failure will generate some customer dissatisfaction)	4, 5, vi
Low (the failure volition only crusade minor customer annoyance)	2, 3
Modest (customer may not even become aware of the failure)	1

Table 4.3. Rankings of Likelihood of Detection and Associated Descriptions

Likelihood of Detection	Rank
Non-detection inevitable (potential design problems cannot be detected by the programme)	ten
Very low (program probably will non be able to discover a potential design problem)	9
Low (program is unlikely to notice a potential pattern problem)	7, viii
Moderate (program may find a potential design problem)	5, 6
High (in that location is a good take a chance that the program will discover a potential design problem)	3, 4
Very high (it is near certain that the program will detect a potential design problem)	1, ii

The gamble priority number is expressed by

(iv.1) $\text{RPN}=\left(\text{OR}\right)\left(\text{SR}\right)\left(\text{DR}\right)$

where OR is the ranking of probability of occurrence.

SR is the ranking of severity of effects.

DR is the ranking of probability of detection.

Failure modes with a loftier RPN are more critical and given a higher priority than ones with a lower RPN. When the scales used range from 1 to 10, the value of an RPN will be between one and 1,000. The scales and categories used may, of course, vary from ane system to some other.

Military Standard Method

The Department of Defense, in Procedures for Performing a Failure Mode, Effects, and Criticality Analysis [v] gear up forward a technique for ranking potential failure modes that is often used in the defense, aerospace, and nuclear power generation industries. The military standard method consists of singled-out qualitative and quantitative approaches. The qualitative arroyo, used when failure rate information are non bachelor, groups occurrence probabilities for individual item failures together into levels that establish qualitative failure probabilities.

Table four.4 presents the set up of levels and associated guidelines used in the military standard method. After the failure-way probability level is adamant, the probability level and severity classification of the failure mode are plotted on a criticality matrix, equally shown in Figure 4-1. Table 4.5 presents the failure mode severity classifications.

Table four.4. Qualitative Ranking of Failure Probabilities

Level of Probability of Occurrence	Curt Description of the Rank Level	Detailed Description of the Rank Level
V	Extremely unlikely	The probability of a failure during the item'south functional period is virtually negligible.
IV	Remote	The probability of a failure during the particular's functional period is remote.
III	Depression to moderate	The probability of a failure during the particular'south functional period is low to moderate.
2	Moderate	The probability of a failure during the item's functional flow is moderate.
I	Loftier	The probability of a failure during the item'southward functional period is high.

Figure four-ane. Criticality matrix for comparing failure modes with respect to severity.

Tabular array 4.v. Nomenclature of Failure-mode Severity

Severity Classification	Brusque Clarification of the Classification	Detailed Description of the Nomenclature
D	Minor	The failure will lead to unscheduled maintenance or repair but volition not exist serious enough to result in injury, holding damage, or system damage.
C	Marginal	The failure will lead to delay or loss of availability or mission degradation and may as well crusade small-scale injury, small property damage, or minor system damage.
B	Critical	The failure will lead to mission loss and may also crusade severe injury, major property damage, or major system damage.
A	Catastrophic	The failure may result in death or organisation loss.

The criticality matrix presented in Figure 4-one provides a machinery for comparison the probability and severity of failure modes. The criticality matrix represents the combined factors of the severity of the potential failure's furnishings and the probability that the failure will occur. This matrix can help gear up priorities for addressing potential failures and developing appropriate cosmetic measures. The area of the matrix labeled "approximate desirable design region" indicates a low probability of failures with class A and B severity furnishings and anywhere from a low to high probability of grade C and D failures that can be tolerated. Still, every possible footstep should exist taken to eliminate class A and B failure modes, or at least to reduce their probability of occurrence, by making appropriate design changes.

The quantitative approach, used when failure rate data are available, defines the failure-style criticality number, N_cf, by

(four.2) ${\text{N}}_{\text{cf}}={\lambda }_{\text{p}}\text{T}\theta \text{n}$

where λ_p is the constant failure charge per unit of the particular.

T is the item operating time.

θ is the conditional probability that the effect of the failure volition match the identified severity nomenclature. Tabular array 4.six presents quantified values for θ.

Tabular array 4.6. Failure Effect Probability Values

Failure Result Description	Value for θ (probability)
No effect	0
Possible loss	Between 0 and 0.10
Probable loss	Between 0.10 and 1.00
Bodily loss	1

n is the failure way apportionment ratio, or the probability that the particular will fail in the specific failure way under consideration. In other words, information technology is the fraction of the item failure rate that can exist apportioned to the failure way of interest. Furthermore, when all failure modes of an detail are specified, the sum or addition of the apportionments is equal to unity. Tabular array 4.7 presents examples of failure style apportionment ratios.

Table four.7. Examples of Part Failure Fashion Apportionments

Particular Description	Item Failure Style	Apportionment Value (or probability value for n)
Hydraulic valve	a)Stuck closed	0.12
	b)Stuck open	0.xi
	c)Leaking	0.77
Variable resistor	a) Open	0.53
	b) Curt	0.07
	c) Erratic output	0.forty
Relief valve	a) Prematurely open	0.77
	b) Leaking	0.23
Fixed resistor	a) Short	0.05
	b) Open	0.84
	c) Parameter change	0.eleven

The detail criticality number, C_i, is calculated for each severity grade. Information technology is the total of the disquisitional numbers associated with each of the item's failure modes that fall into the severity class nether consideration:

(4.3) ${\text{C}}_{\text{i}}=\underset{\text{i}=1}{\overset{\text{thou}}{\Sigma }}{\left({\text{N}}_{\text{cf}}\right)}_{\text{i}}=\underset{\text{i}=1}{\overset{\text{k}}{\Sigma }}{\left({\lambda }_{\text{p}}\text{T}\theta \text{north}\right)}_{\text{i}}$

where k is the number of item failure modes that autumn into the severity classification under consideration.

When an detail failure mode results in multiple severity-grade effects, each with its own occurrence probability, only the nearly critical should be used in the computation C_i [8]. Otherwise, the event may be mistakenly depression values of C_i for the less critical severity classes. Therefore, θ values should be calculated for all severity classes associated with a failure mode, including those associated with class B, C, and D failures.

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Modified failure modes and effects analysis model for critical and complex repairable systems

Garima Sharma , Rajiv Nandan Rai , in Safety and Reliability Modeling and its Applications, 2021

9.5 Example written report ¹

The instance study of ecology control and life support system (ECLSS) of orbital space station's (OSS) is selected as an case to illustrate the presented methodology. Maintenance of complex system like OSS ECLSS is a challenging task for the modern day maintenance engineers, as high skills and expertise are required to accomplish these tasks proficiently. The ECLSS of an OSS is a critical system, which includes several complex subsystems, such as atmosphere management, water management, nutrient product, waste material direction and crew condom. Moreover, the systems are interrelated with each other for proper functionability of unabridged subsystem. On failure, ECLSS subsystems are by and large exposed to imperfect repairs, which imply that the repair actions bring the subsystems to a land that is in between the new land and the state prior to failure.

The outset step is to place important decision criteria relevant to the current maintenance of OSS ECLSS and that could be 1) skill, ii) environment, 3) procedure, and 4) resources. The skills are required to undertake a number of processes including inspection, servicing, troubleshooting, removal, installation, rigging, testing, and repairing during maintenance of OSS ECLSS. The ecology weather condition inside OSS is adverse due to microgravity conditions. Since the crew is unable to acquit weight on their feet, in the long term there are many health problems associated with it. Basic and muscles weaken, and other changes likewise have place within the trunk. This adversely affects the working weather in executing the maintenance job of OSS ECLSS. Adherence to the process helps ensure that the crew is properly trained and each workplace has the necessary equipment and other resources to perform the job. Approved written procedures are required to be followed for performance of all maintenance and repair activities by the coiffure. Maintenance resources are needed to facilitate the successful completion of the maintenance task. The resource is the crew's near important requirement to get the given work done. Mostly, requirement of resources is dictated by the features of the environmental factors, and deportment of the crew for OSS ECLSS maintenance.

Now, because these four criteria, fuzzy-AHP weights are estimated with the aid of fuzzy extent analysis as explained in Section 9.3.1 and the obtained weights are every bit appended below:

(9.28) $\left[\begin{array}{c}{\mathrm{Due\; west}}_{FM\left(Skill\right)}\\ W_{FM\left(\mathrm{Due\; east}nvironm\mathrm{eastward}\mathrm{north}t\right)}\\ {\mathrm{Due\; west}}_{FM\left(Proc\mathrm{due\; east}dur\mathrm{eastward}\right)}\\ {\mathrm{Westward}}_{FM\left(Resourc\mathrm{eastward}\mathrm{due\; south}\right)}\end{array}\right]=\left[\begin{array}{c}0.17\\ 0.38\\ 0.06\\ 0.38\end{array}\right]$

TheRPN_System [Eq. (nine.25)] in example of ECLSS tin be written as:

$RP{N}_{\mathrm{Due\; east}CL\mathrm{Southward}S\left(\mathrm{Thousand}\right)}=RP{\mathrm{Due\; north}}_{\mathrm{South}\mathrm{chiliad}ill}+RP{N}_{\mathrm{East}nvirongent}+RP{N}_{Procedure}+RP{N}_{Resourc\mathrm{due\; east}s}$

Where:

RPN _ECLSS(G) : take chances priority number of ECLSS due to maintenance

RPN_Skill : risk priority number of ECLSS due to skill

RPN_Surroundings  : risk priority number of ECLSS due to surround

RPN_Process : gamble priority number of ECLSS due to procedure

RPN_Resources : risk priority number of ECLSS due to process

Thus from Eq. (nine.26),

$\begin{array}{cc}\hfill RP{N}_{\mathrm{Southward}\mathrm{1000}ill}& =q_{F\mathrm{Thousand}\left(\mathrm{Due\; south}\mathrm{thou}i\mathrm{50}l\right)}\times {\mathrm{Westward}}_{F\mathrm{Grand}\left(\mathrm{Due\; south}\mathrm{grand}i\mathrm{fifty}\mathrm{fifty}\right)}\times S_{F\mathrm{One\; thousand}\left(Skill\right)}\times D_{FM\left(Skill\right)}\hfill \\ \hfill RP{\mathrm{Northward}}_{\mathrm{East}nvironment}& =q_{FM\left(Environme\mathrm{northward}t\right)}\times W_{FM\left(Enviro\mathrm{north}\mathrm{one\; thousand}ent\right)}\hfill \\ & \times {\mathrm{Southward}}_{F\mathrm{Yard}\left(Environm\mathrm{east}nt\right)}\times D_{FM\left(E\mathrm{northward}5iron\mathrm{grand}\mathrm{due\; east}\mathrm{due\; north}t\right)}\hfill \\ \hfill RP{N}_{Procedur\mathrm{eastward}}& =q_{FM\left(Procedur\mathrm{due\; east}\right)}\times W_{FM\left(Proc\mathrm{east}dure\right)}\times S_{FM\left(Procedure\right)}\times D_{FM\left(Proc\mathrm{east}dur\mathrm{eastward}\right)}\hfill \\ \hfill RP{\mathrm{North}}_{R\mathrm{east}\mathrm{due\; south}ources}& =q_{FK\left(Re\mathrm{south}ourc\mathrm{east}s\right)}\times W_{F\mathrm{Chiliad}\left(Re\mathrm{south}ources\right)}\times {\mathrm{Southward}}_{FG\left(R\mathrm{due\; east}sourc\mathrm{due\; east}\mathrm{southward}\right)}\times D_{FM\left(Re\mathrm{due\; south}ources\right)}\hfill \end{array}$

Where

q _FM(Skill) ,q _{FM(Environment)}, q _{FM(Procedure)} and q _FM(Resource) : repair effectiveness indices due to skill, environment, procedure, and resources.

West _FM(Skill) , W _{FM(Environment)},W _{FM(Procedure)} and Due west _{FM(Resources)} : importance weights of all the 4 criteria estimated through fuzzy- AHP.

Southward _FM(Skill) ,Due south _{FM(Environment)},S _{FM(Procedure)}andSouth _{FM(Resources)} : severity level due to skill, surroundings, procedure, and resource.

D _FM(Skill) ,D _{FM(Environment)},D _{FM(Procedure)}, andD _{FM(Resources)}: detection Level of failures due to skill, environment, procedure, and resource.

Thus the concluding algorithm for the RPN of ECLSS due to maintenance is:

$\begin{array}{cc}\hfill RP{\mathrm{Northward}}_{EC\mathrm{Fifty}\mathrm{Southward}\mathrm{South}\left(\mathrm{Chiliad}\right)}& =\{\left[q_{F\mathrm{Grand}\left(S\mathrm{thou}ill\right)}\times {\mathrm{Due\; west}}_{FM\left(\mathrm{South}ki\mathrm{50}l\right)}\times {\mathrm{South}}_{F\mathrm{Yard}\left(S\mathrm{thou}i\mathrm{fifty}l\right)}\times D_{FG\left(Skil\mathrm{fifty}\right)}\right]\hfill \\ & +[q_{FM\left(Environment\right)}\times {\mathrm{Westward}}_{F\mathrm{Thousand}\left(E\mathrm{north}viro\mathrm{north}me\mathrm{north}t\right)}\times S_{FM\left(Enviro\mathrm{north}m\mathrm{eastward}nt\right)}\hfill \\ & \times D_{FK\left(E\mathrm{northward}\mathrm{five}iro\mathrm{due\; north}ment\right)}]\hfill \\ & +\left[q_{FM\left(Procedur\mathrm{east}\right)}\times {\mathrm{Westward}}_{FM\left(Procedure\right)}\times S_{FM\left(Procedure\right)}\times D_{FM\left(Proc\mathrm{due\; east}dure\right)}\right]\hfill \\ & +\left[q_{F\mathrm{1000}\left(Resource\mathrm{due\; south}\right)}\times W_{F\mathrm{Chiliad}\left(R\mathrm{due\; east}sources\right)}\times {\mathrm{Due\; south}}_{FM\left(Re\mathrm{south}ources\right)}\times D_{FM\left(Resourc\mathrm{eastward}\mathrm{south}\right)}\right]\}\hfill \end{array}$

The values of weights for the four criterions equally shown in Eq. (9.28) are also scaled on a numeric scale of i − 10 on similar lines every bit that of scaling of the probability of occurrence (O) (Ebeling, 2004; Rai and Bolia, 2015). The scale decided for the weights for the RPN interpretation is as follows:

For 0  ≤ West ≤ 0.ii, the values assigned are from (one − 6) and

For 0.2  ≤ W  ≤ 0.iii, the values assigned are from (7 − 10)

Based on Eq. (9.28) and the calibration explained higher up following values are assigned to the weights of all four criteria:

$\left[\begin{array}{c}{\mathrm{West}}_{FM\left(\mathrm{Southward}ki\mathrm{fifty}l\right)}\\ W_{FM\left(Environkent\right)}\\ W_{FK\left(Proc\mathrm{eastward}dur\mathrm{due\; east}\right)}\\ {\mathrm{Due\; west}}_{FM\left(Resources\right)}\end{array}\right]=\left[\begin{array}{c}6\\ 10\\ 2\\ \mathrm{x}\end{array}\right]$

The severity values designated to the four criteria respectively equally explained in sub-department ix.4.1 are as follows: (T ways transpose of matrix)

The detection values assigned to all the iv criteria as explained in sub-section 9.4.ii are equally follows:

${\left[D_{FM\left(S\mathrm{thou}il\mathrm{fifty}\right)},D_{FK\left(Enviro\mathrm{northward}me\mathrm{due\; north}t\right)},D_{FM\left(Proc\mathrm{east}dure\right)},D_{FM\left(R\mathrm{due\; east}\mathrm{due\; south}ource\mathrm{due\; south}\right)}\right]}^T={\left[5,4,\mathrm{vi},2\right]}^T$

The values of RPN equally a function of the corresponding q obtained for all the criterions are as appended beneath:

$\begin{array}{cc}\hfill RP{\mathrm{North}}_{\mathrm{Southward}gill}& =6\times 5\times 5\times q_{FG\left(Ski\mathrm{fifty}\mathrm{fifty}\right)}=150q_{FM\left(Skill\right)}\hfill \\ \hfill RP{\mathrm{Due\; north}}_{Enviro\mathrm{north}m\mathrm{due\; east}\mathrm{due\; north}till}& =10\times 8\times 4\times q_{F\mathrm{One\; thousand}\left(Enviro\mathrm{due\; north}ment\right)}=320q_{F\mathrm{1000}\left(Enviro\mathrm{northward}me\mathrm{northward}t\right)}\hfill \\ \hfill RP{\mathrm{Northward}}_{Procedure}& =2\times 6\times 6\times q_{F\mathrm{Yard}\left(Proc\mathrm{east}dure\right)}=72q_{F\mathrm{Grand}\left(\mathrm{Due\; south}ki\mathrm{fifty}l\right)}\hfill \\ \hfill RP{N}_{Resources}& =10\times \mathrm{x}\times \mathrm{two}\times q_{F\mathrm{Thousand}\left(R\mathrm{due\; east}\mathrm{southward}ources\right)}=200q_{F\mathrm{Chiliad}\left(R\mathrm{east}sources\right)}\hfill \end{array}$

Equally explained in Section 9.two, the values of q varies from 0 to one. Hence the sensitivity graph of RPN_i is plotted for different values of q and are placed at Fig. 9.2.

Figure ix.two. Graphs between REI (q) and RPN.

The final value of RPN _ECLSS(M) tin exist obtained using Eq. (9.25). It can be seen from Fig. ix.2 that as the values of q increases from 0 to ane, the RPN as well increases. Thus if the RPN has to exist kept low the value of REI (q) should be equally depression every bit possible.

9.five.1 Remedial measures

It is observed from Fig. 9.2 that to attain a low value of RPN, the value of REI (q) for the four selected criteria (Sharma and Rai, 2019) i.e., skill, environment, procedure and resource need to exist kept as low every bit possible for OSS ECLSS maintenance . The skill of the coiffure needs to be enhanced. The attributes of the cocky-management, advice and interpersonal skills, trouble-solving, ability to consistently work safely and rigorously and adhering to OSS regulations are required to be inculcated in the coiffure members. For possessing the required skill to carry out a specified maintenance chore the crew should be trained accordingly. Training is an extremely useful tool that can help the crew to be in a position where the maintenance and repair task of OSS ECLSS tin be washed correctly, effectively, and meticulously.

The crew members inside OSS should be fully aware of the right standard operating procedures and follow servicing packages properly while carrying out maintenance tasks for the ECLSS. Post-obit the right procedure is the standard arroyo to identify the noesis, skills, and attitudes necessary to perform each task in a given job. Adherence to the procedure helps ensure that each crew member is properly trained and each workplace has the necessary equipment and other resources to perform the job. Unworkable or ambiguous procedures are one of the well-nigh common reasons for procedural violations.

The resources available should be adequate to cater for both preventive and cosmetic maintenance tasks. The ability of a crew fellow member inside OSS to complete a ECLSS maintenance activity may be greatly afflicted by the not-availability of resource. The performance of an activity may be further affected if the bachelor resources are of low quality or inadequate for an activity. Therefore, forward planning to locate, acquire, and store resource is essential to complete a job more effectively, correctly and efficiently. It is likewise essential to properly maintain the available resources. Moreover, necessary arrangements are required to be made to learn resources peculiarly the spare parts in time to accomplish a loftier availability of ECLSS equipment (both the storage and recycling).

In view of the foregoing it is reiterated that, if the RPN is to exist kept low so that the risk associated with the maintenance of OSS ECLSS is kept at blank minimum, and so the REI (q) associated with skill, environment, procedure, and resources are to be kept as shut to goose egg equally possible.

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Guided Discussion Hazard Analysis

Swapan Basu , in Plant Hazard Analysis and Safety Instrumentation Systems, 2017

2.2.3 Risk Priority Number (RPN)

In addition to the other run a risk assessment tools discussed in Chapter I, an organization may choose to develop risk ranking tables based on RPN to assist the controlling process. The RPN approach is an alternative to the risk matrix, also found in FMEA/FMECA. In FMEA/FMECA, the analyzing team assigns each failure mode numeric values that quantify likelihood of occurrence, likelihood of detection, and severity of impact. So, each failure style has a numeric score to quantify (1) likelihood of failure occurrence, (2) likelihood of failure undetected, and (iii) severity of harm or damage the failure mode may cause. All ranks are given on a scale from i to 10 (or 1–5). The specific rating descriptions and criteria for the ranking of occurrence (O), severity (S), and detection (D) are divers past the organization, FMECA standard, and/or the analysis team to fit the products or processes that are beingness analyzed. Fig. 4/two.2.iii-1 shows typical S,O and D in calibration of 10, for RPN calculation facility. Here, one thing worth noting is that in the case of occurrence likelihood, more often than not, component failures in E/E/PE, etc. are expressed in terms of in one case in number of years, whereas in other cases it is expressed in terms of number of items failed per (say) 1000 items.

Effigy Four/2.2.iii-1. Ranking of severity, occurrence, and detection for RPN.

When using this risk assessment technique, it is of import to remember that RPN ratings are relative to a particular analysis. Therefore RPN in one analysis is comparable to RPNs in the same assay but it may not be comparable to RPNs in another analysis. And then, it is not possible to share these numbers with other applications.

•

The RPN   = South  × O  × D. Higher RPN is worse than lower RPN, that is, higher RPN numbers signify more risks. Here, 1 needs to note that RPN (simple product of 3 parameters) is not a perfect representation of risks considering these number assignments are subjective and not continuous. Another interesting factor worth noting is that RPN (in its simplest product class, i.due east., RPN   = S  × O  × D) cannot assume whatever number, for example, the RPN value cannot exist 13 because this is a prime number >x (since the scale is ten, and 13 can have but factors thirteen and 1). Some other interesting fact is that with same RPN value, risks are not same, for instance, (Due south)(O)(D)   =   2   ×   6   ×   x   = 120 and 8   ×   three   ×   5   =   120; in ane example severity is at the lowest end, whereas in other case it is at the higher end of the calibration. Naturally, in the second case, severity is more pregnant than in the commencement, though both have aforementioned RPN.

•

An organization may consider issues with high severity and/or loftier occurrence ratings to correspond a higher risk than issues with high detection ratings. Therefore basing decisions solely on the RPN (considered in isolation) may result in inefficiency and/or increased risk. In view of this, for amend assessments, instead of using a uncomplicated product form, many companies use other calculation methods suitable for the application, that is, in some cases information technology is quite possible to use a weighted sum and then that specific weightings could exist more transparent and the consequence is more accurate and may be free from the limitations discussed previously. A particular analysis team may cull to supplement or replace the basic RPN methodology with other related techniques, such as revised RPNs, the occurrence/severity matrix, ranking lists, adventure ranking tables, and/or higher-level RPNs. All of these techniques rely heavily on engineering science judgment and must exist customized to fit the production or process that is being analyzed and the particular needs/priorities of the organization.

•

FMEA worksheets will typically place whether corrective action is required based on a combination of severity, occurrence, detection, and/or RPN values. Afterward RPN assessment, recommended actions are suggested. After implementation of the recommended action, the unabridged issue is reassessed to get an indication the effectiveness of the corrective activeness. Naturally, with a revised set of severity, occurrence and detection ratings a new RPN is calculated. From these two values information technology is possible to become % reduction in RPN:

%reduction in RPN   =   100   ∗   {RPN_initial  −   RPN_revised}/RPN_initial

Let initial South, O, and D values be 7, 8, and six, so RPN   =   336 and revised S, O, and D values be 7, v, and four, then RPN   =   140.

Therefore %reduction in RPN   =   100∗(336   −   140)/336   =   58.three%. From hither it can exist concluded that RPN is a method to assess the relative take a chance for a particular analysis and is a helpful tool. Also there can be several revised methods or techniques to calculate this and apply it for the analysis best suited. Another important term, explained in Fig. IV/two.ii.3-2 is "error proofing." Readers are advised to take a notation of this as the same volition be referred to in subsequent chapters.

Effigy IV/2.2.iii-2. Error proofing.

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Design quality management

Robin Kent , in Quality Direction in Plastics Processing, 2016

Hazard Priority Number (RPN)

For each potential cause, the private factors are rated and multiplied together (RPN = Due south × P × C) to produce the RPN.

The design FMEA forms the ground of the draft control plan for the product (see Section 10.three).

Action can then be taken and recorded on the significant RPNs to:

•

Reduce the severity.

•

Reduce the probability.

•

Better the blueprint or temporarily improve the controls.

The RPN is then reassessed after completing the actions.

•

Tip - Action should be mandatory for whatever severity rating of nine or to a higher place.

•

Tip - A design FMEA should be reviewed and revised with time (information technology is a 'alive document') to reflect new equipment, processes and procedures. This will permit the command plan to be reviewed with the experience gained.

The FMEA is a 'alive' document and needs to exist maintained and kept upwards-to-date.

Severity of event (Due south)	Ranking	Probability of event (P)	Ranking	Electric current controls (C)	Ranking
Hazardous: without warning	10	Very High: issue is inevitable	10	Absolute dubiety of detection	10
Hazardous with alarm	9		9	Very remote take a chance of detection	nine
Very Loftier	viii	High: Repeated events	8	Remote	8
Loftier	vii		7	Very low	7
Moderate	6	Moderate: Occasional events	half-dozen	Depression	half-dozen
Depression	5		five	Moderate	5
Very depression	4		four	Moderately loftier	4
Pocket-sized	three	Low: Relatively few events	three	High	3
Very small-scale	2		2	Very loftier	two
None	one	Remote: Consequence is unlikely	1	Almost certain to detect	1

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Experimental Friction Behavior of Elastomers on Glass

Andrea Stoll , Martin Strangfeld , in Automotive Buzz, Squeak and Rattle, 2012

Wear with intermediary ways

If the specimen contaminated with dust is exposed (Effigy x.22) to long-term stress, the stick-sideslip probability (RPN) increases for all lacquers from about twoscore cycles, except L2. After cleaning using a brush the original stick-sideslip-free condition tin about be regained. The friction force increases with increasing number of cycles and remains at a considerably higher level compared to the initial value afterwards cleaning (Effigy 10.23). The dust-induced, optically visible wear concerns all specimens, except L2, and thus accompanies the stick-slip-behavior. Intensification of the test to 1,000 cycles likewise results in article of clothing of the specimen L2. There is no self-cleaning with long-term stress.

Figure x.22. Preparation of the elastomer specimen with dust.

Figure ten.23. Evolution of the risk priority number (RPN) in relation to the number of cycles of specimens prepared with dust.

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Advanced gas turbine nugget and performance management

T. Álvarez Tejedor , ... P. Pilidis , in Modern Gas Turbine Systems, 2013

12.three.5 Level v: prognostics

The primary function of the prognostics level is to project the current health and performance state of equipment into the time to come, taking into account estimates of hereafter usage profiles. The prognostics level may report health and functioning status at a hereafter time, or may estimate the remaining useful life of an asset given its projected usage profile. Assessments of futurity health or remaining useful life may too have an associated diagnosis of the projected mistake condition.

Assessments of time to come wellness or remaining life may also accept an associated prognosis of the projected fault condition. A calculation of the future RPN may also be performed. This module includes the component/system'due south future health grade and future failure events with associated likelihood probability. The output from this level includes:

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Estimates of future wellness grade.

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Predictions of faults and failures.

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Estimates of remaining life.

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Recommendations.

So prognostics allow usa to predict the onset of hot gas path component failure to friction match its used, or to enhance maintenance support. Prognostic capabilities aggrandize support options and allow for cost-effective planning and management. Component remaining-life-cess and constitute modelling are required for prognostic purposes:

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Life consumption tracking module of hot gas path components:

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Assessment model for coating degradation.

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Assessment model for creep fatigue impairment.

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Assessment model for thermo-mechanical fatigue.

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Hot gas path components lifing prognostics.

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'What if' analysis for functioning and wellness of hot gas path components.

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Risk assessment management for a new medical device

Seeram Ramakrishna , ... Wee Eong Teo , in Medical Devices, 2015

five.3 Risk analysis techniques

There are several normally used risk analysis techniques each with its strengths and weaknesses. Examples of risk assay include preliminary chance analysis (PHA), fault tree analysis (FTA), failure way and effect analysis (FMEA), and risk and operability analysis (HAZOP). Two techniques will exist discussed here to illustrate run a risk analysis based on a top-downwards arrangement approach and a lesser-up arroyo.

v.3.1 Failure way and effect analysis

FMEA is a bottom-up risk analysis technique and information technology is ane of the about popular methods because of its relative simplicity. As the proper name suggests, it involves identifying possible failure modes; the consequence of failure followed by analyzing the cause of the failure.

The FMEA form typically comprises of columns with the post-obit basic sections,

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Potential hazard (failure mode)

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Potential impairment of failure (effects)

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Severity

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Cause of failure

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Probability/occurrence

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Chance level or run a risk priority number (RPN)

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Hazard cosmetic measures

A failure is an event in which the medical device and its components did not function as intended or may have resulted in a hazardous event. Some examples of failure modes are operation failure, materials failure, mechanical failure, electrical failure, and failure of indications. It is important to include and anticipate all possible failure modes such that corresponding furnishings and cause can be predicted for preventive measures to be taken.

Effects of a failure will help to determine the cost or severity of the failure. It is mutual to find a failure effect that is caused by one or more failure modes. Examples of failure event are motorcar stalling, no power, delayed response, and output error. Knowing the effect will help to assign the severity of the outcome. Generally, the lowest level is "negligible" significant no impairment or damage and "catastrophic" is the highest level meaning expiry or serious injuries. The number of intermediate levels is determined by the manufacturer or the organization although it is common to observe a total of three to five levels.

Knowing the failure mode will also assist to determine the potential cause of it. Causes of failures may be attributed to machines or humans. Cause of failure due to a machine may be inadequate strength, insufficient ability, inappropriate insulation, and software code error. Crusade of failure due to a human is usually associated with insufficient training or lack of instructions on the automobile.

It is common at the start of a projection that the failure probability is a prediction based on limited available information. Prediction may come from verification testing, databases of similar items, or estimates from experts. The failure probability may be adjusted later when more data is available from production or customer feedback. Probability of failure typically ranges from improbable to frequent with a total of four to five levels.

Hazard level and RPN are generated from the combination of severity level and failure probability. Although the intention is the same, there is a slight deviation in using hazard level and RPN in the determination of risk acceptability. Risk level works past placing the assigned severity and probability level in a matrix as shown below (Tabular array 5.four).

Table 5.iv. Run a risk acceptability evaluation based on hazard level through a severity and probability matrix

		Level	Severity level
			Negligible	Small-scale	Serious	Catastrophic
			1	two	three	iv
Probability	Frequent	4	Undesirable	Undesirable	Unacceptable	Unacceptable
	Occasional	3	Acceptable	Undesirable	Unacceptable	Unacceptable
	Remote	2	Adequate	Acceptable	Undesirable	Undesirable
	Improbable	1	Adequate	Acceptable	Acceptable	Acceptable

Acceptability of the risk for each combination of probability and severity level is based on the matrix which is defined past the organization. Some organizations include an "Undesirable" category in the matrix to indicate risk corrective measures is required if the combination falls into that category. The RPN works by a numerical multiplication of the probability and severity level. Acceptability is determined by the assignment of the RPN according to each defined category (Table 5.5).

Table v.v. Acceptability determined past the assignment of the RPN

Category	RPN range
Unacceptable	≥   nine
Undesirable	5   ≤   RPN   <   9
Adequate	<   5

Risk level and RPN each have its benefits and weaknesses. Take chances level allows the organization to determine acceptability based on their preferred weighting of the severity and the probability. RPN is a numerical number which gives equal weight to severity and probability unless the equation or assignment of the value is modified to reflect preference. However, RPN tin can be used to include other considerations to the run a risk analysis such as the inclusion of detectability of failure value to the analysis.

Additional categories may be incorporated to the basic FMEA to capture more details to suit the organization's need. It is common to meet a process step, product role, or component listed in the first cavalcade and identifying the potential hazard in the 2d column. Effects of the failure may also be farther refined to effects at the local level and at the arrangement level. For example, a faulty resister in an electrical printed circuit board may cause a bulb to fuse at the local level. At the system level, the result is that there is no power signal light. In the risk analysis for a medical device, an additional column for probability after risk command measures is added to determine the balance risk level. It is of import to note that whatever run a risk control measures will only reduce the probability of failure but non its severity. Other variants of the FMEA include the addition of a column for likelihood of detection. To verify that risk control measures have been implemented and are constructive, an implementation column and effectiveness column may be added to include certificate references. Documents pertaining to implementation are work instructions, quality control procedures, component requirement specifications, engineering change documents, operating manuals, and others. Effectiveness documents may include inspection documents, drawings, postproduction monitoring, vendor evaluation forms, component compliance certificates, and others.

When considering using the FMEA every bit the run a risk analysis tool, it is important to know its weakness. Although the FMEA works well with medical devices with relatively simple systems and few components where failure of an unabridged organization is traceable to the elements, information technology becomes very tedious for complicated machines with multiple functions and modular systems. For instance, a dental archwire comprises of a unmarried material which relies on its mechanical properties to event teeth move based on its profile. However, a complicated organisation such as a robotic surgery arm contains multiple systems such as electronic components, mechanical components, software, and others. But listing the components in a FMEA will see many pages. Complications and misunderstandings may arise when multiple redundancies are built into the organization and the consignment of a failure style and its cause is difficult to ascertain as the relationship and inter-dependency between the components may non exist well divers in the FMEA. For such larger complicated systems, a FTA may exist more appropriate.

five.3.2 Fault tree analysis

The FTA is a systematic top-downwards method which starts from an assumption of a system failure followed by identification of the modes of organisation or component behavior that has contributed to this failure. These modes of system or component are non confined to hardware or software merely include other factors such as human factors or interaction. FTA is particularly useful when quantitative data on probability is available although qualitative analysis can too be performed. In either case, an FTA can pinpoint mutual factors or the factors that are the highest contributor of organization failure. This is non every bit readily identifiable using other risk analysis techniques such equally FMEA. Its visual representation of the causes of the failure allows easy identification of a single fault event (a single failure that triggers a complete arrangement failure). Where quantitative data is bachelor, the probability of failures can be anticipated through mathematical calculations.

The FTA is comprised of a acme event and a serial of symbols, events, and logic gates for the structure of the tree. Some of the symbols commonly used in an FTA are shown in Table 5.6. Refer to IEC 61025 [154] for more symbols used in an FTA.

Table v.6. Common symbols used in FTA

Symbol	Name	Clarification
	Basic event	Failure mode of a component or an individual failure cause
	Undeveloped event	A potential failure mode or failure cause. However, relevant information is unavailable at the moment
	OR gate	Output event occur if whatsoever of the input events occur
	AND gate	Output consequence occur only if all the input events occur
	NOT gate	Output event occur simply if all the input events DOES NOT occur

For complicated systems, the FTA diagram may go very large when the system failure is at a very high level. For example, a top consequence such every bit "system no response" in an electrical device may be due to numerous causes. In the absenteeism of software to track the FTA, information technology is more practical to consider intermediate undesirable events such as "input power cutting" or "transformer failure." This as well allows different functional teams to work on various aspects of the FTA earlier combining at a later phase. Figure 5.3 is an analogy of how an FTA diagram looks for an warning-related harm to patient in a medical system [155].

Figure five.iii. Illustration of FTA for alarm in a medical system [155].

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Applied Risk Analysis - As a Tool for Minimizing Plastic Product Failures

Subodh Medhekar , ... Robert Caligiuri , in Plastics Failure Analysis and Prevention, 2001

A SIMPLIFIED FMEA APPROACH

The objective of the FMEA is to create a living certificate that becomes a basis for making strategic engineering decisions. In a similar fashion to others, we characterize the relative risk contribution of potential failure scenarios associated with the procedure or product in terms of a risk priority number (RPN). This RPN is obtained as a product of three indices representing, respectively, the severity of the failure consequences, it's likelihood of occurrence, and it'due south detectability.

The process we take developed to simplify the FMEA process employs a three-phase arroyo. In the first phase we develop a common and consistent framework for the analysis. Nosotros get together an FMEA team and utilize a combination of brainstorming sessions (which is the traditional FMEA method) and focused evaluations of the functionality of each component under both normal and aberrant conditions. Nosotros then apply the deductive approach and develop a handful of "generic" failure modes, each with an associated relative severity index representing its potential safety or business organisation touch, and numerous potential failure scenarios involving these modes.

Depending on the nature of the information in this phase, nosotros also develop a relative scale for the likelihood of occurrence and detectability indices, from which values are objectively assigned to private scenarios. These indices are constructed to be broad brush, rather than detailed, and are unremarkably qualitative rather than quantitative. For example, instead of trying to precisely evaluate specific frequencies of occurrence, nosotros volition simply construct a scale based on "occasionally", "more than than a few times", "observed once or twice in a product's lifetime", and "never observed". In nearly all cases, we find that this simplification provides adequate resolution yet shortens the assay time substantially. A typical case of simplified occurrence indices is displayed north Tabular array i.

Tabular array 1. Sample simplified frequency of occurrence alphabetize

Evidence Nearly the Failure Scenario	Occurrence Index
Documented "frequent" occurrence in this or like application.	10
Known to take occurred "a few times" with documented evidence.	eight
Known to have occurred once with documented or reported evidence in this or similar application.	6
Anecdotal evidence of previous occurrence of this or related failure scenario.	4
No previous history, merely greater potential to occur.	2
No previous history, merely potential to occur.	1

In the 2d phase, we compile information relevant to each individual failure scenario in "evidence sheets". This can range from qualitative or anecdotal information to formally documented, quantitative data. Typical data sources are field service data from previous generation or like products, supplier quality control data, process control and product quality assurance data, descriptions of previous failure experiences, and published failure data.

The information collected and tabulated in this phase has applicability beyond the FMEA, and forms the basis of the "living" document. It is critically important that the document remain "living", that is, it must be continually updated. An FMEA which is performed and never acted upon or updated can represent a potential liability to a visitor in the event of subsequent litigation apropos the component being analyzed.

In the third stage, we calculate the RPNs and graphically analyze their distribution. This provides guidance every bit to the risk contributors that may require action. We also develop appropriate action strategies in this phase and evaluate their potential for risk mitigation.

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Contamination Control

Jacques C.J. van der Donck , in Developments in Surface Contamination and Cleaning: Particle Deposition, Control and Removal, 2010

2.two Footstep 2: Setting Priorities for the Contaminating Steps

In the first step, events are identified that volition cause an out-of-specification situation for the process or production. Since contagion control has a larger telescopic than only an isolated event, more than one contaminating footstep tin can be plant. For the sake of efficiency the largest risks should exist considered offset. This requires a arrangement for setting priorities.

A good method, which is widely used in the semiconductor industry for yield optimization, is FMEA [22] . In this method, the process or equipment is divided into subprocesses and for each pace or module a failure mode is defined. Finally, a risk priority number (RPN) is calculated for each failure fashion. The RPN is calculated from the scores for 3 different parameters described beneath: Severity, Occurrence, and Detection. When the failure modes are ranked from high RPN to low RPN, the failure mode with the highest risk will appear at the meridian of the list.

•

Severity is related to the impact of a failure mode on the functionality of the production or process. The score on Severity increases when the influence of a contaminant on a disquisitional functional property increases. The scores depend on the application and failure mode. In the literature [22] the definitions of the scores for Severity differ slightly. They are all related either to customer dissatisfaction or to damage to equipment or people. Scoring is very different for experimental equipment that is all the same under development than for improvement of mature processes. For experimental equipment, Severity is oft ranked betwixt "no affect" and "extensive damage to equipment".

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Occurrence is related to frequency of the failure mode. The score for Occurrence increases when the failure manner takes place more frequently. Scoring tables vary slightly from source to source. For the semiconductor manufacture, the Sematech ratings [22] can exist used. Score 1 is defined every bit "An unlikely probability of occurrence during operating time interval. Single failure fashion (FM) probability < 0.001." Score 10 is defined as "A loftier probability of occurrence during the operation interval FM > 0.ii."

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Detection is related to the detectability of the failure mode. If the occurrence of a failure mode can be detected instantaneously, the score for Detection volition be low. If no detection method is bachelor for the failure mode, then the score volition increase to 10.

The RPN is then calculated from the following expression:

$\text{RPN}=\text{Severity}\times \text{Occurrence}\times \text{Detection}$

The accuracy of the RPN is strongly dependent on the accuracy of the input data. For equipment in performance, the experimental data on Severity, Occurrence, and Detection tin can be gathered. If the equipment is still in the design stage scores tin can but be estimated. For Severity and Detection, the start estimates can be quite accurate, since they can be derived from the process design and requirements. If Step one has been carried out, this information is bachelor. Finding a score for Occurrence is more difficult. If no experimental data are available, but estimates can exist fabricated that are mainly based on feel.

FMEA is a engineering-driven activity. The simply way costs are rated is by the score for Severity as "client dissatisfaction" [22]. An economic evaluation of the costs caused past the failure is not given. An culling method for prioritizing, which includes the costs of a failure manner, is a Total Value of Ownership analysis [23]. Here, the processes are analyzed in terms of added value of each process pace and the costs related to it. With this method, costs related to the failure mode and the economical value for a solution tin be made apparent.

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