Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System
Abstract
:1. Introduction
2. Materials and Methods
2.1. FMEA
- Identify potential failure modes which may degrade products, damage assets and property, cause incidents, jeopardize health and safety and lose assets’ performance;
- Improve the design and procedures, as well as set up barriers, in the place where they were missing efficiently and cost-effectively;
- Identify risk as part of risk assessment in the overall risk management process (ISO 31000) [34];
- Calculate risk level, which is the basis for prioritizing risk response action plans;
- Provide the basis for a reliable maintenance program and maintenance strategy.
2.2. P-F Curve
2.3. Fuzzy Logic System and Adaptive Neuro FuzzyInterface Systems
3. Results
3.1. Severity
3.2. Occurrence
3.3. Detectability
3.4. Final Calculation of RPN
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Ranking | Linguistic Ranking | Business | Health | Legal and Environment | Reputation |
---|---|---|---|---|---|
10 | Dangerous without warning | Extensive damage Unit is out of operation for more than 30 days. Impact on other unit and other production site operation. Permanent/medium term effect on market supply of several products. | Multiple fatalities through an Accident or Occupational Illness. | Massive effect persistent severe environmental damage. Severe disruption extending over a large area. Effect on recreational use or nature conservancy. Constant breaching of statutory or prescribed limits. | International impact International public attention. Attracting extensive adverse attention in international media. National/international policies with potentially severe impact on access to new areas, granting of licenses, and/or tax legislation. |
9 | Dangerous with warning | Extensive damage Unit is out of operation for 2 to 4 weeks. Impact on other unit and other production site operation. Effect on market supply of several products. | Multiple fatalities through an Accident or Occupational Illness. | Massive effect persistent severe environmental damage. Severe disruption extending over a large area. Effect on recreational use or nature conservancy. Constant breaching of statutory or prescribed limits. | International impact International public attention. Attracting extensive adverse attention in international media. National/international policies with potentially severe impact on access to new areas, granting of licenses, and/or tax legislation. |
8 | Very high | Major damage Unit is out of operation for 3 to 14 days or circulation for more than 2 days. Impact on other unit and other production site operation. Temporary effect on market supply of some products. | Single fatality or Permanent Total Disability Through an Accident or Occupational Illness. Multiple major injuries. | Major effect Severe environmental damage. The company is required to take extensive measures to restore the contaminated environment to its original state. Extended breaches of statutory or prescribed limits. | National impact National public concern. Attracting extensive adverse attention in the national media. Regional/national policies with potentially restrictive measures and/or impact on granting of licenses. Mobilization of action groups. |
7 | High | Serious damage Circulation (no feed in, no products out) for max 2 days. Impact on other unit operation and on other production site. Depending on actual season it could have effect on the market supply. | Serious injury or health effects including Permanent Partial Disability. Affecting work performance in the longer term, such as prolonged absences from work. Irreversible health damage without loss of life, e.g., noise-induced hearing loss, chronic back injuries. | Local effect Limited discharges of known toxicity. Repeated breaches of statutory or prescribed limits. Affecting neighborhood. | Considerable impact Regional public concern. Attracting extensive adverse attention in local media and slight national media and/or local/regional political attention. Adverse stance of local government and/or action groups. |
6 | Medium | Serious damage Unit is running with more than 25% reduced throughput vs. daily plan and/or off-spec product quality. Impact on other unit operation but no impact on other production site. No transfer required. No effect on market supply. | Serious injury or health effects including Permanent Partial Disability. Affecting work performance in the longer term, such as prolonged absences from work. Irreversible health damage without loss of life, e.g., noise-induced hearing loss, chronic back injuries. | Local effect Limited discharges of known toxicity. Repeated breaches of statutory or prescribed limits. Affecting neighborhood. | Considerable impact Regional public concern. Attracting extensive adverse attention in local media and slight national media and/or local/regional political attention. Adverse stance of local government and/or action groups. |
5 | Low | Unit is running with less than 25% reduced throughput vs. daily plan and/or off-spec product quality. Impact on other unit operation but no impact on other production site. No transfer required. No effect on market supply. | Minor injury or health effect (Lost Time Injury) -Affecting work performance, such as restrictions on activities (Restricted Work Cases) -Need to take a few days off to fully recover (Lost Workday Cases) -Limited health effects which are reversible, e.g., skin irritation, food poisoning. | Minor effect -Contamination. Damage sufficiently large to attack the environment. -Single breach of statutory or prescribed criteria. -Single complaint. -No permanent effect on the environment. | Limited impact -Some local public concern. -Attracting some local media and/or local political attention with potentially adverse aspects for company operations. |
4 | Very low | Slight damageSpared machine failure (e.g., paired pump, control valve with by-pass, level transmitter with level gauge). No impact on other unit operation. No effect on market supply. | Slight injury or health effect -including first aid case and medical treatment case -Not affecting work performance or causing disability. | Slight effect -Local environmental damage within Company premises and systems. | Slight impact—Public awareness may exist, but there is no concern among the general public. |
3 | Weak | Fit&Finish/Squeak&Rattle item does not conform. Defect noticed by average operators. | |||
2 | Very weak | Fit&Finish/Squeak&Rattle item does not conform. Defect noticed by discriminating operators. | |||
1 | None | No effect |
Ranking | Linguistic Ranking | Detectability Description |
---|---|---|
10 | Absoluteuncertainty | Neither Process control nor Diagnostic system nor operator will and/or can detect a potential cause/mechanism and subsequent failure mode; or the reason process control, no supervisory staff. (e.g., underground (buried) system) |
9 | Very remote | Neither Process control nor Diagnostic system nor operator will and/or can detect a potential cause/mechanism and subsequent failure mode. Operator staff provide supervision only. No diagnostic is available or feasible even when the system is out of operation. (Leaks, lab checks, destructive inspection techniques needed, strange noises, etc.) Master operator, engineer, is needed to observe fault. |
8 | Remote | Process control will not and/or cannot detect a potential cause/mechanism and subsequent failure mode. Supervision by operators exists. Diagnostics and inspection are possible only in out of operation mode. (Due to high temperature, intrusive inspection needed, etc.) Lab check is possible. Master operator, engineer, is needed to observe fault. |
7 | Very low | Process control will not and/or cannot detect a potential cause/mechanism and subsequent failure mode. Supervision by operators exists. Off-line diagnostics and condition monitoring exists during operation. Lab checks available. Experienced operator, engineer, is needed to observe fault. |
6 | Low | Based on data analysis from Process control and/or diagnostic systems (trends, lab check results) the potential cause/mechanism can be identified. (No direct sign of failure, it can only be derived from other system parameters.) Supervision by operators exists. Diagnostics and condition monitoring exists during operation. Less experience is enough to observe deviation. |
5 | Moderate | Based on data from Process control and/or diagnostic systems (trends, lab checks) the signs of a potential cause/mechanism can be identified. Supervision by operators exists. Real-time diagnostics and condition monitoring exists during operation. Less experience is enough to observe deviation. |
4 | Moderately high | The process control system and/or the real time diagnostic will detect a potential cause/mechanism and subsequent failure mode and send signal to the staff. The intervention is taken by the staff. Does not need personnel observation to detect deviation. |
3 | High | The process control system and/or real-time diagnostics will detect a potential cause/mechanism and subsequent failure mode and send signals to the staff. Parallelly make intervention automatically (e.g., interlock, autostart). Does not need personnel observation to detect deviation. |
2 | Very high | The process control system and/or real-time diagnostics will detect the signs of a potential cause/mechanism and send signals to the staff. The intervention is taken by the staff. Does not need personnel observation to detect deviation. |
1 | Almost certain | The process control system and/or real-time diagnostics will detect the sign of a potential cause/mechanism and send signal to the staff. Parallel make intervention automatically (e.g., interlock, autostart, CCC antisurge). |
Appendix B
Exp.no. | Temp. [°C] | Flow [t/h] | RMS Motor [mm/s] | RMS Pump [mm/s] | Failure | S1 | S2 | S3 | Mean Value (Round) | Data Sets |
---|---|---|---|---|---|---|---|---|---|---|
1 | 327 | 450 | 4.16 | 12 | f1 | 8 | 8 | 8 | 8 | Training Data SETS |
2 | 380 | 80 | 4.16 | 0.1 | f2 | 7 | 5 | 8 | 7 | |
3 | 327 | 203 | 4.16 | 0.1 | f3 | 2 | 2 | 1 | 2 | |
4 | 380 | 203 | 4.16 | 12 | f4 | 8 | 8 | 8 | 8 | |
5 | 380 | 203 | 4.16 | 4.07 | f5 | 7 | 4 | 4 | 5 | |
6 | 353 | 327 | 0.19 | 0.1 | f6 | 1 | 4 | 4 | 3 | |
7 | 380 | 450 | 2.84 | 4.07 | f7 | 7 | 4 | 4 | 5 | |
8 | 300 | 80 | 0.19 | 0.1 | f8 | 5 | 5 | 6 | 5 | |
9 | 300 | 80 | 1.51 | 12 | f9 | 8 | 8 | 8 | 8 | |
10 | 380 | 80 | 0.19 | 0.1 | f10 | 7 | 3 | 8 | 6 | |
11 | 353 | 450 | 4.16 | 0.1 | f11 | 2 | 4 | 4 | 3 | |
12 | 353 | 327 | 0.19 | 8.03 | f12 | 8 | 8 | 8 | 8 | |
13 | 300 | 450 | 4.16 | 0.1 | f13 | 1 | 5 | 4 | 3 | |
14 | 300 | 203 | 0.19 | 12 | f14 | 8 | 8 | 8 | 8 | |
15 | 380 | 450 | 0.19 | 12 | f15 | 9 | 8 | 8 | 8 | |
16 | 300 | 450 | 2.84 | 8.03 | f16 | 8 | 8 | 8 | 8 | |
17 | 353 | 80 | 2.84 | 4.07 | f17 | 1 | 1 | 1 | 1 | |
18 | 353 | 450 | 0.19 | 4.07 | f18 | 2 | 4 | 4 | 3 | |
19 | 353 | 80 | 2.84 | 12 | f19 | 8 | 8 | 8 | 8 | |
20 | 300 | 450 | 2.84 | 12 | f20 | 8 | 8 | 8 | 8 | |
21 | 300 | 80 | 4.16 | 12 | f21 | 8 | 8 | 8 | 8 | |
22 | 356 | 89 | 0.58 | 4.55 | f22 | 7 | 4 | 4 | 5 | |
23 | 355 | 90 | 0.58 | 4.55 | f23 | 7 | 4 | 4 | 5 | |
24 | 355 | 92 | 0.58 | 4.55 | f24 | 7 | 4 | 4 | 5 | |
25 | 355 | 130 | 0.58 | 4.55 | f25 | 7 | 4 | 4 | 5 | |
26 | 277 | 119 | 0.58 | 4.55 | f26 | 7 | 7 | 7 | 7 | |
27 | 227 | 146 | 0.58 | 4.55 | f27 | 7 | 7 | 7 | 7 | |
28 | 162 | 139 | 0.58 | 4.55 | f28 | 7 | 7 | 7 | 7 | |
29 | 116 | 167 | 0.58 | 4.55 | f29 | 7 | 7 | 7 | 7 | |
30 | 99 | 81 | 0.58 | 4.55 | f30 | 7 | 8 | 8 | 8 | |
31 | 323 | 95 | 0.58 | 4.55 | f31 | 7 | 4 | 4 | 5 | |
32 | 296 | 116 | 0.58 | 4.55 | f32 | 7 | 4 | 7 | 6 | |
33 | 252 | 131 | 0.58 | 4.55 | f33 | 7 | 7 | 7 | 7 | |
34 | 185 | 121 | 0.58 | 4.55 | f34 | 7 | 7 | 7 | 7 | |
35 | 141 | 18 | 0.58 | 4.55 | f35 | 7 | 8 | 8 | 8 | |
36 | 128 | 4 | 0.58 | 4.55 | f36 | 7 | 8 | 8 | 8 | |
37 | 124 | 1 | 0.58 | 4.55 | f37 | 7 | 8 | 8 | 8 | |
38 | 356 | 89 | 0.39 | 3.2 | f38 | 4 | 2 | 1 | 2 | |
39 | 355 | 90 | 0.39 | 3.2 | f39 | 4 | 2 | 1 | 2 | |
40 | 355 | 92 | 0.39 | 3.2 | f40 | 4 | 2 | 1 | 2 | |
41 | 277 | 119 | 0.39 | 3.2 | f41 | 7 | 7 | 7 | 7 | |
42 | 227 | 146 | 0.39 | 3.2 | f42 | 7 | 7 | 7 | 7 | |
43 | 162 | 139 | 0.39 | 3.2 | f43 | 7 | 7 | 7 | 7 | |
44 | 116 | 167 | 0.39 | 3.2 | f44 | 7 | 7 | 8 | 7 | |
45 | 99 | 81 | 0.39 | 3.2 | f45 | 7 | 8 | 8 | 8 | |
46 | 323 | 95 | 0.39 | 3.2 | f46 | 4 | 2 | 1 | 2 | |
47 | 296 | 116 | 0.39 | 3.2 | f47 | 7 | 3 | 4 | 5 | |
48 | 185 | 121 | 0.39 | 3.2 | f48 | 7 | 7 | 7 | 7 | |
49 | 141 | 18 | 0.39 | 3.2 | f49 | 7 | 8 | 8 | 8 | |
50 | 128 | 4 | 0.39 | 3.2 | f50 | 7 | 8 | 8 | 8 | |
51 | 124 | 1 | 0.39 | 3.2 | f51 | 7 | 8 | 8 | 8 | |
52 | 380 | 450 | 4.16 | 0.1 | f52 | 4 | 4 | 5 | 4 | |
53 | 290 | 75 | 4.16 | 0.1 | f53 | 5 | 4 | 5 | 5 | |
54 | 380 | 450 | 4.16 | 12.1 | f54 | 8 | 8 | 8 | 8 | |
55 | 380 | 75 | 4.16 | 12.1 | f55 | 8 | 8 | 8 | 8 | |
1 | 120 | 1 | 0.39 | 3.2 | f56 | 7 | 8 | 8 | 8 | Testing Dana Sets |
2 | 252 | 131 | 0.39 | 3.2 | f57 | 7 | 7 | 7 | 7 | |
3 | 355 | 130 | 0.39 | 3.2 | f58 | 4 | 2 | 1 | 2 | |
4 | 120 | 1 | 0.58 | 4.55 | f59 | 7 | 8 | 8 | 8 | |
5 | 290 | 450 | 4.16 | 12.1 | f60 | 8 | 8 | 8 | 8 | |
6 | 380 | 75 | 0.19 | 0.1 | f61 | 8 | 7 | 8 | 8 | |
7 | 380 | 75 | 4.16 | 0.1 | f62 | 8 | 7 | 8 | 8 | |
8 | 290 | 75 | 4.16 | 12.1 | f63 | 8 | 8 | 8 | 8 | |
9 | 290 | 75 | 0.19 | 0.1 | f64 | 7 | 5 | 6 | 6 | |
10 | 380 | 450 | 0.19 | 12.1 | f65 | 8 | 8 | 8 | 8 | |
11 | 290 | 450 | 4.16 | 0.1 | f66 | 5 | 4 | 5 | 5 |
Data Sets | Failure | M1 | M2 | M3 | M4 | M5 | M6 | M7 |
---|---|---|---|---|---|---|---|---|
Training Data Sets | f1 | 7.999989067 | 7.999651393 | 7.999988701 | 7.99995429 | 7.999983784 | 7.999755957 | 8.000184974 |
f2 | 6.999988065 | 7.000106491 | 6.999987365 | 7.000042491 | 6.999980369 | 7.000102306 | 6.999752825 | |
f3 | 1.999998799 | 2.00002253 | 1.999998311 | 1.999901552 | 2.000016019 | 1.999990333 | 2.000055382 | |
f4 | 7.999990325 | 8.000098341 | 7.999989991 | 7.999651548 | 7.999991407 | 7.999633554 | 7.999723766 | |
f5 | 4.99998697 | 4.999984263 | 4.999992413 | 5.000019081 | 4.999988727 | 4.999786769 | 4.999967374 | |
f6 | 2.999993113 | 2.999964972 | 2.999993305 | 2.999926361 | 2.999949991 | 2.999694009 | 3.001887862 | |
f7 | 4.999984696 | 5.000011033 | 4.999994006 | 4.999732608 | 4.999991683 | 5.000004912 | 5.000091361 | |
f8 | 4.999988742 | 4.999981866 | 4.999988064 | 4.999916726 | 4.999986839 | 4.999989191 | 4.996664961 | |
f9 | 7.999970955 | 7.999946709 | 7.999979605 | 7.999995398 | 7.999975146 | 7.999704862 | 8.00013682 | |
f10 | 5.99998971 | 6.000158415 | 5.999989317 | 5.999643317 | 5.999991196 | 5.999918854 | 6.000134233 | |
f11 | 3.000207021 | 3.000006045 | 3.000341299 | 3.000030048 | 3.034899942 | 3.000096687 | 3.426682421 | |
f12 | 7.99996597 | 7.999868173 | 7.999981975 | 7.99978235 | 7.999966847 | 8.000604343 | 7.999971198 | |
f13 | 2.999906087 | 3.000061955 | 2.999879053 | 3.000013441 | 2.992229858 | 3.000089092 | 2.855980364 | |
f14 | 7.999984149 | 7.999995982 | 7.999983663 | 8.000507964 | 7.999979149 | 7.999715976 | 7.999995796 | |
f15 | 7.999991788 | 8.000116339 | 7.99999042 | 8.000086068 | 7.999989271 | 8.00025871 | 8.000000075 | |
f16 | 7.999966831 | 8.000011738 | 7.999987038 | 7.999642613 | 7.999985315 | 7.99989108 | 7.999991987 | |
f17 | 0.999990036 | 0.999998764 | 0.99999688 | 0.999988903 | 0.999996681 | 1.000007097 | 1.000076509 | |
f18 | 2.999989916 | 3.000025731 | 2.999994077 | 2.999931749 | 2.999983416 | 3.000009117 | 2.99928059 | |
f19 | 7.999956683 | 8.000023883 | 7.999975048 | 7.999746806 | 7.999969999 | 7.999992864 | 7.999998749 | |
f20 | 7.999981653 | 7.999895314 | 7.99998714 | 7.999816507 | 7.999987065 | 7.999676006 | 8.000000068 | |
f21 | 7.99998151 | 7.999974236 | 7.9999807 | 8.000310525 | 7.999979522 | 8.00027594 | 7.999998003 | |
f22 | 4.997205648 | 5.00000111 | 5.002665289 | 4.999997065 | 5.029261776 | 5.000009725 | 5.046233923 | |
f23 | 4.995340507 | 5.000014243 | 4.995072721 | 4.999983126 | 5.002543323 | 5.000078794 | 4.998585025 | |
f24 | 5.008291113 | 5.00000794 | 5.002206988 | 4.999978774 | 4.964074288 | 4.999987713 | 4.997886878 | |
f25 | 4.999100926 | 5.000009005 | 4.99992063 | 4.999979545 | 4.985958324 | 5.000006645 | 4.989612699 | |
f26 | 6.96392433 | 7.000018266 | 6.994200759 | 7.000008928 | 6.640920719 | 7.000016088 | 6.886794895 | |
f27 | 6.987907694 | 6.99999929 | 6.998368466 | 6.999991982 | 6.979669884 | 6.999980236 | 6.827191871 | |
f28 | 7.003203101 | 6.999998125 | 7.00035879 | 6.99997972 | 7.01226368 | 7.000015676 | 7.071260248 | |
f29 | 6.999762392 | 6.999997915 | 6.999970474 | 6.9999614 | 6.998777954 | 7.000008395 | 6.972207497 | |
f30 | 7.999787478 | 7.999997873 | 7.999966773 | 7.999999369 | 7.995750788 | 8.000007859 | 7.963422567 | |
f31 | 4.999789938 | 5.000007383 | 4.999986995 | 4.999997565 | 4.980579377 | 5.000004062 | 4.905196618 | |
f32 | 6.017804172 | 6.00000505 | 6.002827899 | 6.000000005 | 6.318278986 | 6.000006472 | 6.113630939 | |
f33 | 7.028556762 | 7.000000305 | 7.004521553 | 6.999994463 | 7.088801878 | 7.000008032 | 7.16473149 | |
f34 | 6.999543505 | 7.000000678 | 6.999767257 | 7.000001738 | 7.000223725 | 7.000009877 | 7.068797882 | |
f35 | 7.99325016 | 7.999998504 | 8.000851415 | 7.999995605 | 8.001595948 | 8.000062728 | 7.972711516 | |
f36 | 8.024514849 | 7.999999821 | 7.996362743 | 7.999998317 | 8.00289024 | 8.000022382 | 8.005525083 | |
f37 | 7.981895305 | 7.999999841 | 8.002810888 | 7.999998641 | 7.998268724 | 7.999987388 | 8.013239193 | |
f38 | 2.004514646 | 2.000019021 | 2.007134207 | 1.999956556 | 2.212324526 | 2.000071623 | 2.027584684 | |
f39 | 1.993075785 | 1.999994924 | 1.98842483 | 1.99999298 | 2.039823732 | 1.999887111 | 1.958246677 | |
f40 | 2.002217229 | 2.000001727 | 2.004152531 | 2.000002552 | 1.726009168 | 2.000035516 | 1.959815995 | |
f41 | 6.999454266 | 7.000002476 | 6.999970005 | 6.99999505 | 6.555826323 | 6.999997931 | 6.921736613 | |
f42 | 7.000055631 | 6.999999656 | 7.000034421 | 7.000001453 | 7.046512094 | 7.000001037 | 7.098674021 | |
f43 | 6.999738207 | 6.999999815 | 6.999839334 | 7.000020052 | 6.923194269 | 6.999999529 | 6.945323633 | |
f44 | 7.000022023 | 7.000000526 | 6.999992177 | 7.000001396 | 7.007221511 | 6.999999858 | 7.023418169 | |
f45 | 8.000047891 | 7.999999937 | 8.000013843 | 8.000000837 | 8.017949393 | 8.000000084 | 8.028826756 | |
f46 | 2.000362627 | 2.00000033 | 2.000546799 | 2.000000988 | 1.950034163 | 2.000002586 | 2.116738534 | |
f47 | 5.000179724 | 5.000000165 | 4.999699543 | 5.000000476 | 5.47159183 | 5.000002303 | 4.979234171 | |
f48 | 7.000809029 | 7.000000073 | 7.00021984 | 6.999999719 | 7.058343475 | 6.999999038 | 6.945171327 | |
f49 | 7.99170088 | 7.999999453 | 7.998151132 | 7.99999884 | 8.002310648 | 8.000003881 | 7.966838702 | |
f50 | 8.03083664 | 8.000000017 | 8.007726772 | 7.999999966 | 7.997843975 | 7.999983022 | 8.011512999 | |
f51 | 7.977016937 | 8.000000009 | 7.994032983 | 8.000000012 | 7.991180928 | 8.00001343 | 8.021993044 | |
f52 | 3.999868509 | 3.999974539 | 3.999756293 | 3.999991868 | 3.972688688 | 4.000004666 | 3.717334404 | |
f53 | 4.999985047 | 4.999987478 | 4.99998441 | 5.000020785 | 4.999981597 | 4.999982546 | 5.00002159 | |
f54 | 7.999994651 | 7.999892179 | 7.999992941 | 7.999973573 | 7.99999663 | 7.999995601 | 7.99999981 | |
f55 | 7.999987765 | 8.000086762 | 7.999987123 | 7.999950408 | 7.999990936 | 7.99996105 | 8.000001979 | |
Testing Dana Sets | f56 | 7.883699303 | 7.988041227 | 7.977005623 | 7.979612408 | 7.984127519 | 8.289646734 | 8.035697248 |
f57 | 7.713050858 | 7.551198462 | 8.149253454 | 7.556692548 | 7.584457266 | 8.179188946 | 7.761410804 | |
f58 | 2.638067778 | 3.629625103 | 2.117475983 | 3.576437191 | 3.745496258 | 1.631037575 | 1.998904615 | |
f59 | 7.907930283 | 7.984396245 | 8.008376836 | 7.978873927 | 7.9926925 | 8.359863144 | 8.022374128 | |
f60 | 5.85898468 | 5.871803405 | 5.739332778 | 6.240054657 | 7.778465255 | 7.728993255 | 8.003131572 | |
f61 | 5.813177534 | 5.797911204 | 5.82252024 | 5.735630026 | 6.238863869 | 5.494827714 | 6.024048229 | |
f62 | 6.780702484 | 6.763367006 | 6.793731899 | 6.722898753 | 7.124383799 | 7.110573848 | 7.203624214 | |
f63 | 6.794233305 | 6.682533226 | 6.677880756 | 6.731826717 | 7.82254437 | 7.655578373 | 8.005303185 | |
f64 | 4.191618331 | 4.041865156 | 4.162423686 | 4.371713753 | 4.549963976 | 4.076169004 | 7.742776761 | |
f65 | 8.104538866 | 8.266181523 | 8.00061357 | 8.070323522 | 8.025620331 | 8.077698425 | 8.051998386 | |
f66 | 4.643670645 | 4.015833861 | 5.312580977 | 4.080856774 | 5.775450348 | 3.22594546 | 2.74824902 |
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Grid Partitioning Generated Fis | Sub Clustering Generated FIS | ||||||
---|---|---|---|---|---|---|---|
Trimf, NO. MF 5, Constant Output | Trimf, No. Mf 5, Linear Output | Gaussmf, No mf 5, Outputconstant | Gaussmf, No mf 5, Output Linear | Gaussmf, No mf 4, Output Constant | Gaussmf, No mf 4, Output Linear | ||
R² value | M1 | M2 | M3 | M4 | M5 | M6 | M7 |
Trenningdata | 1 | 1 | 1 | 1 | 0.9968 | 1 | 0.9982 |
Testing data | 0.7018 | 0.5948 | 0.6729 | 0.6411 | 0.8642 | 0.7855 | 0.7428 |
All data | 0.9458 | 0.9325 | 0.9418 | 0.9414 | 0.8945 | 0.9497 | 0.9547 |
Ranking | Linguistic Ranking | Description | Frequency in Days | Fuzzy Numbers (Trapmf) |
---|---|---|---|---|
10 | Extremely high | Failure is almost inevitable. Occurs every day (even more than once). | 0–2 | [0 0 2 3] |
9 | Very high | Happens (almost) every day. | 3–7 | [2 3 7 8] |
8 | High | Repeated failures. Occurs once per week. | 8–13 | [7 8 13 14] |
7 | Frequent | It takes more time than a week for one occurrence, but it happens every month. | 14–31 | [13 14 31 32] |
6 | Moderately frequent | Occurs every three months at least. | 32–91 | [31 32 91 92] |
5 | Randomly | Occurs once per six months. | 92–181 | [91 92 181 182] |
4 | Not frequently | Failure happens once per 12 months. | 182–365 | [181 182 365 366] |
3 | Rarely | One occurrence per 1–3 years. (e.g., two failures per TA cycle) | 366–1095 | [365 366 1095 1096] |
2 | Very rarely | One occurrence per 3–5 years. (e.g., one failure per TA cycle) | 1096–1825 | [1095 1096 1825 1826] |
1 | None | One occurrence in over 5 years. (e.g., one failure over more than one TA cycle) | 1825–2000 | [1825 1826 2000 2000] |
Summary | ||||||
---|---|---|---|---|---|---|
Groups | Count | Sum | Average | Variance | ||
Occurrence criteria | 2000 | 5608 | 2.804 | 1.5804 | ||
Model MAMDANI FIS | 2000 | 5653.1 | 2.826 | 1.6719 | ||
Model ANFIS | 2000 | 5608 | 2.804 | 1.4774 | ||
Y = −A*ln(x) + B | 2000 | 5608 | 2.805 | 1.4597 | ||
Anova | ||||||
Source of Variation | SS | df | MS | F | p-value | F crit |
Between Groups | 0.7614 | 3 | 0.2538 | 0.1640 | 0.9206 | 2.606 |
Within Groups | 12,372.54 | 7996 | 1.5473 | |||
Total | 12,373.3 | 7999 |
Criteria | Ranking | Fuzzy Set | Description | Fuzzy Numbers |
---|---|---|---|---|
Process/Control | 0 | No | No control | (0 0 1) |
1 | OS | People | (0 1 2) | |
2 | OS + LAB | People + lab check | (1 2 3) | |
3 | OFF-LINE | Off-line diagnostic | (2 3 4) | |
4 | I + LS | Local Instrument -Indirect indication | (3 4 5) | |
5 | I + LS + Signal | Local Instrument- Direct indication | (4 5 6) | |
6 | I + BPCS | Instrument + BPCS No Real time diagnostic | (5 6 7) | |
7 | I + BPCS + Failure | Instrument + BPCS Real time diagnostic | (6 7 8) | |
8 | SIS | Safety Instrument System SIS | (7 8 9) | |
Decision to Act | 0 | No Act | No mitigation action | (0 0 1) |
1 | SME | Measurement requires an SME engineer to observe. | (0 1 2) | |
2 | STAFF | The intervention is made by the staff. | (1 2 3) | |
3 | AUTO | The intervention is made automatically (in built). | (2 3 3) |
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Ivančan, J.; Lisjak, D.; Pavletić, D.; Kolar, D. Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System. Machines 2023, 11, 739. https://doi.org/10.3390/machines11070739
Ivančan J, Lisjak D, Pavletić D, Kolar D. Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System. Machines. 2023; 11(7):739. https://doi.org/10.3390/machines11070739
Chicago/Turabian StyleIvančan, Jelena, Dragutin Lisjak, Duško Pavletić, and Davor Kolar. 2023. "Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System" Machines 11, no. 7: 739. https://doi.org/10.3390/machines11070739
APA StyleIvančan, J., Lisjak, D., Pavletić, D., & Kolar, D. (2023). Improvement of Failure Mode and Effects Analysis Using Fuzzy and Adaptive Neuro-Fuzzy Inference System. Machines, 11(7), 739. https://doi.org/10.3390/machines11070739