Mechanical Reliability Prediction: A Different Approach

Mechanical Reliability Prediction:A Different Appro ach

Abstract

Market trend

Exploring alternative approaches

Introduction to Case Study: Hydraulic Accumulator (HYDAC) Solution

1. Approach to select an appropriate method:

2. PoF, SSI and NSWC methods explained

a. A sample prediction for explaining the PoF approach to predict Barrel (Cylinder) failure rate:

b. Failure rate calculation using SSI Theory:

c. Sample prediction using NSWC for Dynamic Seal

3. Summary of analysis

Best Practices in the Industry

Common issues with alternative methods

ConclusionConclusion

Reference

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Table of Contents

© 2014, HCL Technologies. Reproduction Prohibited. This document is protected under Copyright by the Author, all rights reserved.

Mechanical Reliability Prediction: A Different Approach | 3

Abstract

Market trends

Reliability Prediction is a practice of predicting the failure rate of a component or subsystem during the early design phase of the product development cycle. This is carried out in an attempt to ensure product reliability. Primarily, reliabilityprediction is a design-supportive study intended for the following functionalities

In the aerospace industry, the failure rates predicted by this study are used for safety assessment in compliance to FAR 25.1309 and ARP 4761. The predicted failure rates are used in FMEA which in turn, provides input to FTA.

The current industry practice uses a standard database approach called NPRD to find failure rates of mechanical components/systems. In recent times, aircraft manufacturers are increasingly rejecting NPRD predictions, as the failure rates predicted using this database is arguable in terms of design closeness. Also, in most cases, they were found to vary with actual failure rates observed in the field.

A possible solution for this problem could be devised using a combination of approaches, such as A possible solution for this problem could be devised using a combination of approaches, such as

Although prediction with the above methods are accurate, these methods are not yet implemented due to a lack of understanding, increased complexity, requirements of large amounts of design data, and the need for extensive time and effort. In this whitepaper, we have built a case study around Accumulator. This helps explain an effective solution for mechanical predictions using a blend of the SSI theory, PoF approach, and NSWC methods.

In the current scenario, the NPRD method is extensively used for mechanical product reliability prediction as this method is relatively easy to predict the part failure rate. NPRD has remained the preferred method for years. However, the current market trend is moving towards exploring other appropriate methods for mechanical predictions. Aircraft makers feel the need to explore alternative methods due to the following reasons:

Design feasibility evaluation

Comparing design alternatives

Identifying potential failure areas

Tracking reliability improvements

Stress Strength Interference (SSI) theory

Physics of Failure (PoF) approach

Naval Surface Warfare Centre (NSWC) methods

Need for more accurate, precise and reliable failure data which considers actual usage conditions in its prediction models

Predictions should be specific for each manufacturer. Also, the quality of components and manufacturing processes should be accounted for

NPRD assumes a constant failure rate, which is not true in all cases

In NPRD, failure rates are not application sensitive and have limited accuracy

It is doubtful that sizeable design improvements will result from the NPRD prediction process



Exploring alternative approaches

Hydraulic Accumulator (HYDAC)

Methods of failure analysis

A combination of PoF, NSWC and SSI methods can be used appropriately to arrive at more accurate component and system-level reliability predictions. As field data is rarely available, the use of field data for Reliability Prediction is likely to be ruled out as an alternative.

A hydraulic accumulator is a pressure storage reservoir. In this a non-compressible hydraulic fluid is held under pressure by an external source like a spring, a raised weight, or compressed gas. An accumulator enables a hydraulic system to cope with extremes of pressure using a less powerful pump. This enables the hydraulic system to respond more quickly to a temporary demand, and to smooth out pulsations.

Even though all 3 alternative methods can help to arrive at the part failure rate, selecting an appropriate method of Reliability Prediction is the key. Figure 1 explains the logic behind finalizing a prediction technique.

As seen in Figure 1, a good starting point would be the life-limited items. Aircraft life-limited parts are those parts that are As seen in Figure 1, a good starting point would be the life-limited items. Aircraft life-limited parts are those parts that are identified by the aircraft manufacturer or production certificate holder as being limited to a total life counted in hours, cycles, landings, or by calendar. Typical life-limited parts are seals, bearings and springs.

In case prediction models are not available in NSWC then either the PoF or SSI approach can be selected by following the logic explained in above diagram.

The prediction method chosen for each of the accumulator components is shown in Table 3 -“Summary of analysis”.

a. Physics of Failure (PoF) is a science-based mathematical approach for reliability predictions that uses modeling and simulation to design-in reliability. This approach models the root causes of failure such as fatigue, fracture, wear, and corrosion. Fatigue and fracture are the two root-causes considered in this case study. Various models were applied for these two causes and the failure rates were predicted.

Figure1. Determination of the prediction technique



Failure rate calculation using the PoF theory

The accumulator Barrel will be subjected to 1,951,000 pressure cycles at different pressure levels shown in Table 1. It is also subjected to a static pressure of 3500 psi. As per the logic explained in an earlier section “Approach to select an appropriate method:”, the SSI method would be used for static failure rate estimation caused by 5000psi pressure and fatigue calculations would be used for failures caused by 1,951,000 pressure cycles. A combination of SSI and PoF approach should be used for ‘Barrel’ failure predictions.

Step 1: List all types of stress on the Barrel (Cylinder):Types of stresses: Hoop stress, longitudinal stress and stress at the weakest location.

In Figure 2, and This Barrel is a thin walled cylinder. (Criteria for thin wall cylinder:

Step 2: Convert all pressure cycles into stress values: Convert all minimum and maximum pressure levels into both ‘longitudinal’ and ‘hoop stress values’. The ‘stress at the weakest location’ test would be calculated by Finite Element Analysis.

Hope Stress,

Longitudinal Stress,

Whereas,

)

AIRPORT

PISTON

LUBRICATION PASSAGE

HYDRAULIC FLUID PORT

END CAP

END CAPPACKING & BACKUP RING

BARREL ASSEMBLY

Pressure Cycle(psig)

Total Cycles 1,951,000

5,000 to 3,950 to 5,000

5,000 to 3,700 to 5,000

5,000 to 3,600 to 5,000

5,000 to 1,900 to 5,000

5,000 to 3,450 to 5,000

5,000 to 2,000 to 5,000

200 to 5,000 to 200200 to 5,000 to 200

160,000

800,000

500,000

15,000

24,000

350,000

1,951,0001,951,000

Cycles per Aircraftlife (ni)

Table 1: Pressure cycles at different Pressure Levels

Figure 2. Barrel showing all the stresses



Step 3: Convert the spectrum of stress into an equivalent stress:

Equivalent stress equation for Barrel material 13‐8Mo CRES, as taken from MMPDS database is Seq = Smax(1-R)0.11

Where, Seq = Equivalent Stress,

Smax = Maximum Stress (Stress induced by maximum pressure in a pressure cycle)

Repeat the above step for all three stresses (hoop, longitudinal and stress at the weakest location).Repeat the above step for all three stresses (hoop, longitudinal and stress at the weakest location).

Step 4: Calculate number of cycles before failure:

The empirical relationship for S-N curve as given in MMPRD for 13‐8Mo CRES.

Step 5: Use Minor’s rule to calculate cumulative damage caused by each set of pressure cycles

Cumulative damage caused by each Pressure cycles

R = Stress Ratio = Minimum Stress (Stress Induced by minimum pressure in a pressure cycle)

Maximum Stress

Log Nf = 18.12 - 6.54 lod (Seq)

Where Nf = Number cycles to failure at an Equivalent stress of Seq

Table 2: Cumulative Damage Calculation

Number of cycles over the design life, ni Life to failure corresponding to stress, Nf

Damage due to each set ofpressure cycle

Cumulative Damage

ni

Nf



a. In Table 2 Cumulative damage of 8.25E-02 % damage is caused by 1,951,000 cycles

b. 1,951,000 cycles would take 102,000 hours to complete

c. This means 8.25E-02 % damage is caused in 102,000 hours

d. Time for first failure (MTBF) = Time for 100% damage = 102000 / 8.25E-02 = 1.2E6 hours

e. Failure rate = 8.09E - 09 failure per flight hour

The above values 8.09E-09 are the failure rate due to longitudinal stress . But in Hoop stress , the equivalent stress is

less than the Endurance Limit of the 13‐8Mo CRES (AMS 5629). This means that failure rates should be almost zero

under such stress levels. Then the failure rate caused by stress at the weakest location is calculated as,

= 9.27E-03 and as zero. The weakest locations and levels of stress at those

locations are calculated from Finite Element Analysis.

b. SSI theory: Fracture and deformation analysis was performed using Stress/Strength Interference theory. Stress/Strength

Interference analysis is a practical engineering tool used for designing and quantitatively predicting the reliability of

mechanical components subjected to mechanical loading. This method treats both stress and strength as random variables

subject to natural scatter. If failure is defined by Stress > Strength, then the failure probability would be equivalent to the

interference of stress and strength distribution.

In case of ‘Barrel’, the SSI theory was used for calculating failure rate resulting from a static pressure of 5000psi. In this

method, the corresponding Hoop stress, Longitudinal stress, Stress at the weakest location on the air and fluid side of the

‘Barrel’ are calculated at a pressure level of 5000 psi. The interference when stress exceeds strength is calculated using

Total Failure rate for Barrel: The ‘Barrel’ failure rate would be the addition of failure rates calculated from both PoF and

SSI methods.

a. Using the standard Normal distribution table, the Interference probability was calculated for each stress based on ‘Z’ value

b. Reliability = 1- Interference Probability

c. Failure rate was calculated using the relationship: , where R = Reliability, = Failure rate and t = flight operating hours

d. Failure rate was calculated as, 1.46E-05 failures per flight hour

Failure rate calculation using SSI Theory



c. NSWC methods are based upon identified failure modes and their causes. The equations were derived for each failure

mode from design and published experimental data. An example model for dynamic seals is given below

Failure rate model for a dynamic seal as given in the NSWC hand book:

Where, = Generic failure rate of the PTFE seal in failures/million hours

= Base failure rate of PTFE seal, 22.8 failures/million hours

After identifying and quantifying all design and environmental parameters, the applicable Pi factors (multiplication) factors are finally calculated. A DFMEA would help to identify relevant Pi factors and remove irrelevant factors. Each multiplication factor value is calculated using the empirical relationship given in the NSWC handbook.

As explained above, the failure rate for each of the applicable items in the Hydraulic Accumulator were calculated and then summarized in table 3. It can be seen that the NPRD failure rate for the ‘Barrel’ is very low and thus the design seems to be good. But the PoF approach, which considers all applicable design and environmental parameters and applies them on proven mathematical models, reveals the possibility of high failure rate in the ‘Barrel’ assembly. This is applicable for Piston seals as well as NPRD in the same way.

An example: The Pi factor for allowable leakage CQ .

In our case the allowable leakage is 0.025 cu.in / min. So = 2.225Similarly all the factors are calculated and multiplied with base failure rate of 22.8 to arrive at the failure rate of 0.6986 failures per million flight hours.

Effect of fluid pressure

Effect of allowable leakage

Effect of seal size

Effect of contact stress and seal hardness

Effect of seat smoothness

Effect of fluid viscosity

Effect of temperatureEffect of temperature

Effect of contaminants

Effect of pressure velocity co-efficient

CP

CQ

CDL

CH

CF

CV

CCT

CN

CPV

0.250

2.225

2.927

0.553

1.000

0.089

0.3540.354

1.081

1.000

PI Factors Description Calculated Value

Failure rate of Dynamic Seal = 22.8 * 0.25 * 2.225 * 0.553 * 1.00 * 0.089 * 0.354 * 1.081 * 1.00 = 0.6986 failures per million flight hours

Allowable Leakage Multiplying Factor (CQ): Determination of allowable leakage multiplicationfactor can be done be using below equations.

Sample Prediction using NSWC for a Dynamic Seal

Findings



This would help the designer to improve upon his design during the early design phase rather than making design changes after noticing frequent field failures.

Even though these alternate methods provide more accurate and reliable failure rate predictions, the best practices should focus on field data analysis and test data analysis.

Field data analysis: A field item similar to the new item under design is considered for predicting failure rate. The drawback in this method is that the field failure data is often not available. Even when available, the data is not grouped and time to failure for individual items is not available.

Test data analysis:Test data analysis: Testing the actual component would yield prediction with the highest accuracy as compared to all other methods, as actual loading conditions are simulated during testing. However, most of the predictions are performed during the early design stage. Hence, this method may not be practical as it needs a physical prototype for testing. Moreover, testing a completely new design needs a dedicated testing program which may turn out to be an expensive proposition. Due to the limitations mentioned above, designs cannot completely rely on field data and test data analysis results, thus we prefer to go with alternative methods of predictions.

Table 3: Summary of Analysis

Description QtyMethod ofReliabilityPrediction

FailureRate

(Failure rate xQty)

PoF ResultsNPRDResults

RemarksS.No

Barrel SSI, Miner’srule (PoF)

NSWC

SSI

NSWC

NSWC

1

1

1

4

2

1

1

1

2

3

4

5

6

7

15.388

0.1588

0.0256

0.176

0.015

0.6986

0.00060.0006

15.388

0.1588

0.0256

0.704

0.03

0.6986

0.00060.0006

0.008

0.2

0.032

0.2564

0.064

0.0107

0.0320.032

Possibility of Highfailure rate is revealedby PoF method.

Not a high differenceinfailure rate

Exact equivalent partfor Helical Insert is notfound in NPRD

PoF method reveals thepossibility of high failurerate

NPRD value is verygeneric not applicationspecific

Piston

End cap

MountingStrap

HelicalInsert

Seal, Piston

Packing &Backup ring

Total failure rate per 10 6 Hours 17.0056 0.6031

1,658,009 NPRD method is toooptimistic

> 50,000 hours

58,804

MTBF Requirement in Hours

Calculated MTBF in Hours

V

Industry Best Practices



To get more accurate failure rates, the reliability engineers need to spent more time and effort. They need significant volumes of design inputs, analysis results, material properties, usage environment data, and valid assumptions.

Some of the common concerns are

Need for detailed design information (like operating pressure, temperature range, types of loads, material used etc.). Though this is difficult to obtain in the early design stage, it is still not entirely impossible.

Need to make many design assumptions which should be valid enough (e.g. operating temperature range, level of vibration etc.)

Structural analysis results (FEA results) are necessary to make reliability predictions for structural items (e.g. Accumulator Barrel, Piston and End Cap)

Accuracy of prediction is not as good as field/ test data analysis but still better than NPRD predictions

No methods available to validate the results of predictions, unless the design is tested or exposed to field. This No methods available to validate the results of predictions, unless the design is tested or exposed to field. This limitation is also applicable for NPRD methods.

Involves complicated calculations, which take time and effort on the part of the reliability engineer. (The reliability team has already developed standard input templates for PoF/ SSI methods and Excel Macros for performing NSWC calculations for standard items like seals, springs and bearings)

Common Issues with alternative methods

Conclusion

The current industry practice of using the NPRD database for making reliability predictions is quick, easy and inexpensive.

However, it ignores the fact that a new design would be used in a different environment which is not the same in which the

NPRD data has been collected. Moreover, the design and material properties of new items under design may not be similar

to the one considered in the NPRD database. Also, at times, the exact matching part cannot be found in NPRD.

As leading aircraft manufacturers increasingly appreciate the accuracy of NPRD, they are driving their suppliers to use more As leading aircraft manufacturers increasingly appreciate the accuracy of NPRD, they are driving their suppliers to use more

accurate and reliable methods for reliability predictions. It has now become a necessity for reliability engineers to explore

new methods for prediction and then bring them into practice on a large scale.



Highly recommended methods like field / test data analysis are either highly difficult to perform or not practical to perform

during the design phase. Additionally, these methods have proven to be expensive. If we consider any new methods, they

should reflect the actual usage environment unlike NPRD. Also, unlike field / test data analysis, those methods should be should reflect the actual usage environment unlike NPRD. Also, unlike field / test data analysis, those methods should be

possible to use in actual practice and should not be expensive. Considering all these requirements, we can strongly

conclude that the methods explained in this paper - namely NSWC, the PoF approach, and SSI theory - would be highly

suitable to meet the new demands in the aerospace industry for making accurate and cost effective reliability

predictions.

A. NSWC-11 Handbook of Reliability Prediction Procedures for Mechanical Equipment

B. RADC-TR-66-710 Reliability Prediction Mechanical Stress/Strength Interference Models

C. MMPDS Metallic Materials Properties Development and Standardization

D. NPRD 95 Non-electrical Parts database

E. FMD 97 Failure Mode / Mechanism Distribution 1997

F. “Uncertainties in Material Strength Geometric and Load Variables” by Paul E.Hess, Daniel Bruchman, Ibrahim A. Assakkaf, Bilal M. Ayub.

Murali KrishnamoorthyHCL Engineering and R&D Services

Abhay WaghmareHCL Engineering and R&D Services

Designed By: Mayuri Infomedia

This whitepaper is published by HCL Engineering and R&D Services.

The views and opinions in this article are for informational purposes only and should not be considered as a substitute for professional business advice. The use herein of any trademarks is not an assertion of ownership of such trademarks by HCL nor intended to imply any association between HCL and lawful owners of such trademarks.

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Mechanical Reliability Prediction: A Different Approach

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