Abstract
Increasing demand diversity has resulted in high-mix low-volume production where success depends on the ability to quickly design and develop new products. This requires sustainable production capacities and efficient equipment utilization which are ensured through appropriate maintenance strategies. Presently, these are derived from experts’ knowledge, capitalized in FMECA (failure mode, effect and criticality analysis), and effective maintenance procedures. Abu-Samah et al. (Failure prognosis methodology for improved proactive maintenance using bayesian approach. In: 9th IFAC symposium on fault detection, supervision and safety for technical processes. Paris, France, 2015) found increasing unscheduled breakdowns, failure durations and number of repair actions in each failure as the key challenges while sustaining production capacities in complex production environment. Obviously, maintenance based on the historical knowledge is not always effective to cope up with an evolving nature of equipment failure behaviors. Therefore, this paper presents an operational methodology based on Bayesian approach and an extended FMECA method to support experts’ knowledge renewal and maintenance actions effectiveness. In the proposed methodology, FMECA files capitalize and model experts’ existing knowledge as an operational Bayesian network (O-BN) to provide real-time feedback on poorly executed maintenance actions. The accuracy of O-BN is monitored through drifts in maintenance performance measurement (MPM) indicators that result in learning an unsupervised Bayesian network (U-BN) to discover new causal relations from historical data. The structural difference between O-BN and U-BN highlights potential new knowledge which is validated by experts prior to updating existing FMECA and associated maintenance procedures. The proposed methodology is evaluated in a well-reputed high-mix low-volume semiconductor production line to demonstrate its ability to dynamically renew experts’ knowledge and improve maintenance actions effectiveness.
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References
Abu-Samah A, Shahzad MK, Zamaï E, Hubac S (2014) Methodology for integrated failure-cause diagnosis with bayesian approach: application to semiconductor manufacturing equipment. In: Proceedings of second European conference of the prognostics and health management society, Nantes, France
Abu-Samah A, Shahzad MK, Zamaï E, Ben Said A (2015) Failure prognosis methodology for improved proactive maintenance using bayesian approach. In: 9th IFAC symposium on fault detection, supervision and safety for technical processes, Sept 2015, Paris, France
Baly R, Hajj H (2012) Wafer classification using support vector machines. IEEE Trans Semicond Manuf 25(3):373–383
Bertling L, Allan R, Eriksson R (2005) A reliability-centered asset maintenance method for assessing the impact of maintenance in power distribution systems. IEEE Trans Power Syst 20(1):75–82
Bouaziz MF, Zamaï E, Duvivier F (2013) Towards Bayesian network methodology for predicting the equipment health factor of complex semiconductor systems. Int J Prod Res 51(15):4597–4617
Bouckaert RR, (1993) Probabilistic network construction using the minimum description length principle. In: Lecture Notes in Computer Science, vol 747, pp 41–48
Brown SM, Hanschke T, Meents I, Wheeler BR, Zisgen H (2010) Queueing model improves IBM’s semiconductor capacity and lead-time management. Interfaces 40(5):397–407
Bruseberg A (2008) Presenting the value of human factors integration: guidance, arguments and evidence. Cogn Technol Work 10(3):181–189
Cacciabue PC, Mauri C, Owen D (2003) The development of a model and simulation of an aviation maintenance technician task performance. Cogn Technol Work 5(4):229–247
Chang YH, Wang YC (2010) Significant human risk factors in aircraft maintenance technicians. Saf Sci 48(1):54–62
Chickering DM (2002) Learning equivalence classes of Bayesian-network structures. J Mach Learn Res 2:445–498
Crocker J (1999) Effectiveness of maintenance. J Qual Maint Eng 5(4):307–314
Dai W, Maropoulos PG, Zhao Y (2015) Reliability modelling and verification of manufacturing processes based on process knowledge management. Int J Comput Integr Manuf 28(1):98–111
Denson B, Tang SY, Gerber K, Blaignan V (2014) An effective and systematic design FMEA approach. In: Proceeding of reliability and maintainability symposium (RAMS), annual IEEE conference, pp 1–6
Friedman N, Koller D (2000). Being Bayesian about network structure. In: Proceedings of the sixteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann, pp 201–210
Garcia A, Gilabert E (2011) Mapping FMEA into Bayesian networks. Int J Perform Eng 7(6):525–537
He QP, Wang J (2010) Large-scale semiconductor process fault detection using a fast pattern recognition-based method. IEEE Trans Semicond Manuf 23(2):194–200
Heckerman D (1997) Bayesian networks for data mining. Data Min Knowl Disc 1(1):79–119
Hsieh YS, Cheng FT, Huang HC, Wang CR, Wang SC, Yang HC (2013) Vm-based Baseline predictive maintenance scheme. IEEE Trans Semicond Manuf 26:132–144
Hubac S, Zamai E (2013) Politiques de maintenance equipment en flux de production stressant—equipment maintenance policy in stressed manufacturing flow (technology or product). Edition TI (Technique de l’ingenieur) [AG 3535]
Ison A, Spanos CJ (1996) Robust fault detection and fault classification of semiconductor manufacturing equipment. In: Proceedings of the 5th international symposium on semiconductor manufacturing, pp 1–4
Jensen FV, Nielsen TD (2007) Bayesian networks and decision graphs, 2nd edn. Springer, New York
Jordan WE (1972) Failure modes, effects and criticality analyses. In: Proceedings of the annual reliability maintainability symposium, pp 30–37
Kjærulff UB, Madsen AL (2006) Probabilistic networks for practitioners—a guide to construction and analysis of Bayesian networks and influence diagrams. Department of Computer Science, Aalborg University, HUGIN Expert A/S
Krishnamurthy L, Adler R, Buonadonna P, Chhabra J, Flanigan M, Kushalnagar N, Nachman L, Yarvis M (2005) Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the North Sea. In: Proceedings of the 3rd international conference on embedded networked sensor systems, San Diego, California, USA, November 2005, pp 02–04
Lee B (2001) Using Bayes belief networks in industrial FMEA modelling and analysis. Proc Annu Reliab Maintainab Symp 15(4):281–293
Lin XJ, Lin Q, Zhang GN (2015) Effectivity of total productive maintenance (TPM) in large size organizations—a case study in Shandong Lingong. Appl Mech Mater 701:1249–1252
Liu Y (2008) Predictive modeling for intelligent maintenance in complex semiconductor manufacturing processes. ProQuest, Doctorate thesis, University of Michigan
Luo SH, Lee GG (2015) Applying failure mode and effects analysis for successful knowledge management. Total Qual Manag Bus Excell 26(1–2):62–75
Masson M, Koning Y (2001) How to manage human error in aviation maintenance? The example of a Jar 66-HF education and training programme. Cogn Technol Work 3(4):189–204
McKone KE, Schroeder RG, Cua KO (1999) Total productive maintenance: a contextual view. J Oper Manag 17(2):123–144
McLachlan GJ, Do KA, Ambroise C (2004) Analyzing microarray gene expression data. Wiley, New York
Medina-Oliva G, Weber P, Iung B (2015) Industrial system knowledge formalization to aid decision making in maintenance strategies assessment. Eng Appl Artif Intell 37:343–360
Meihami B, Meihami H (2014) Knowledge management a way to gain a competitive advantage in firms (evidence of manufacturing companies). Int Lett Soc Human Sci 03:80–91
Mili A, Siadat A, Hubac S, Bassetto S (2008) Dynamic management of detected factory events and estimated risks using FMECA. In: Proceeding of management of innovation and technology, ICMIT 2008 4th IEEE international conference on, pp 1204–1209
Mili A, Bassetto S, Siadat A, Tollenaere M (2009) Dynamic risk management unveil productivity improvements. J Loss Prev Process Ind 22:25–34
Military US (1949) Procedure for performing a failure mode effect and criticality analysis. United States military procedure MIL-P-1629
Mönch L, Fowler JW, Mason SJ (2012) Production planning and control for semiconductor wafer fabrication facilities: modeling, analysis, and systems. Springer, New York
Moore T, Harner B, Kestner G, Baab C, Stanchfield J (2006) Intel’s FDC proliferation in 300 mm HVM: progress and lessons learned. In: Proceeding of AEC/APC Symp. XVIII, Westminster, CO
Muchiri P, Pintelon L, Martin H, De Meyer AM (2010) Empirical analysis of maintenance performance measurement in Belgian industries. Int J Prod Res 48(20):5905–5924
Muchiri P, Pintelon L, Gelders L, Martin H (2011) Development of maintenance function performance measurement framework and indicators. Int J Prod Econ 131(1):295–302
Munteanu P, Bendou M (2001) The EQ framework for learning equivalence classes of Bayesian networks. In: First IEEE international conference on data mining (IEEE ICDM), San José
Mustapha I, Jusoh A, Nor KM (2015) A review on quality management systems maintenance framework based on process based management, knowledge quality and knowledge self-efficacy. J Teknol 72(4):7–12
Omdahl TP (1988) Reliability, availability, and maintainability (RAM) dictionary. ASQC Quality Press, Milwaukee
Parida A, Kumar U (2006) Maintenance performance measurement (MPM): issues and challenges. J Qual Maint Eng 12(3):239–251
Peter ML (2012) Bayesian statistics: an introduction. Wiley, New York
Pourret O, Naïm P, Marcot B (2008) Bayesian networks: a practical guide to applications. Wiley, Chichester
Rashid HSJ, Place CS, Braithwaite GR (2014) Eradicating root causes of aviation maintenance errors: introducing the AMMP. Cogn Technol Work 16(1):71–90
Rasmussen J (2000) Human factors in a dynamic information society: where are we heading? Ergonomics 43(7):869–879
Redmill F (2002) Risk analysis—a subjective process. Eng Manag J 12(2):91–96
Rissanen J (1978) Modeling by shortest data description. Automatica 14(5):465–658. doi:10.1016/0005-1098(78)90005-5
Schirru A, Pampuri S, DeNicolao G (2010) Particle filtering of hidden gamma processes for robust predictive maintenance in semiconductor manufacturing. In: Proceedings of 6th IEEE CASE
SEMI E79-0200 (2000) Standard for definition and measurement of equipment productivity. Semiconductor Equipment and Material International (Mountain View, CA)
Shahzad MK, Hubac S, Siadat A, Tollenaere M (2011) An extended business model to ensure time-to-quality in semiconductor manufacturing industry. In: International conference on enterprise information systems, Portugal, 2011
Simões JM, Gomes CF, Yasin MM (2011) A literature review of maintenance performance measurement: a conceptual framework and directions for future research. J Qual Maint Eng 17(2):116–137
Susto G, Beghi A, DeLuca C (2011) A predictive maintenance system for silicon epitaxial deposition. In: Proceedings of IEEE conference on automation science and engineering (CASE), pp 262–267
Susto G, Pampuri S, Schirru A, Beghi A (2012) Optimal tuning of epitaxy pyrometers. In: Proceedings of 23rd IEEE/SEMI advanced semiconductor manufacturing conference, pp 294–299
Teece DJ (2000) Strategies for managing knowledge assets: the role of firm structure and industrial context. Long Range Plan 33(1):35–54
Teyssier M, Koller D (2005) Ordering-based search: a simple and effective algorithm for learning Bayesian networks. In: Proceedings of 21st conference on uncertainty in AI (UAI), pp 584–590
Uzsoy R, Lee CY, Martin-Vega LA (1994) A review of production planning and scheduling models in the semiconductor industry part II: shop-floor control. IIE Trans 26(5):44–55
Verdier G, Ferreira A (2011) Adaptive mahalanobis distance and-nearest neighbor rule for fault detection in semiconductor manufacturing. IEEE Trans Semicond Manuf 24(1):59–68
Villacourt M (1992) Failure mode and effects analysis (FMEA): a guide for continuous/line improvement for the semiconductor equipment industry SEMATECH. Transfer 92020963B-ENG
Weber P, Jouffe L (2006) Complex system reliability modeling with dynamic object oriented Bayesian networks (DOOBN). Reliab Eng Syst Saf 91:149–162
Weber A, Thomas R (2006) Key performance indicators: measuring and managing the maintenance function. Ivara Corporation, Burlington
Weber P, Suhner MC, Iung B (2001) System approach-based Bayesian network to aid maintenance of manufacturing process. In: Proceedings of 6th IFAC symposium on cost oriented automation, low cost automation, Berlin, Germany, October 2001, pp 8–9
Yssaad B, Khiat M, Chaker A (2014) Reliability centered maintenance optimization for power distribution systems. Int J Electr Power Energy Syst 55:108–115
Acknowledgments
The authors acknowledge STMicroelectronics for providing an opportunity to carry out field study in their maintenance department. The authors also acknowledge the European project ENIAC INTEGRATE, ANRT (National French Agency for Research and Technology), and Rhone Alpes region for their support.
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Glossary
- ADCS
-
Advanced documentation control systems
- APC
-
Advanced process control
- AMT
-
Aircraft maintenance technicians
- BEOL
-
Backend of line
- BN
-
Bayesian network
- CAD
-
Computer-aided design
- CBM
-
Condition-based maintenance
- CM
-
Corrective maintenance
- CMMS
-
Computerized maintenance management system
- CPT
-
Conditional probability table
- CVD
-
Chemical vapor deposition
- DIEL
-
Dielectric deposition workshop
- EQE
-
Quivalence class algorithm
- FDC
-
Fault detection and classification
- FEOL
-
Frontend of line
- FM
-
Failure mode
- FMEA
-
Failure mode and effects analysis
- FMECA
-
Failure mode effects and criticality analysis
- He
-
Helium
- HFI
-
Human factor integration
- IC
-
Integrated circuit
- KM
-
Knowledge management
- KNN
-
K-nearest neighbor
- KPI
-
Key performance indicator
- MDL
-
Minimum description length
- MPM
-
Maintenance performance measurement
- MP
-
Maintenance procedure
- OEE
-
Overall equipment efficiency
- OFC
-
Objective fulfillment criteria
- OOC
-
Out of control
- O-BN
-
Operational Bayesian network
- PdM
-
Predictive maintenance
- PM
-
Preventive maintenance
- PRM
-
Probabilistic relational model
- RCM
-
Reliability-centered maintenance
- RMS
-
Recipe management systems
- RPN
-
Risk priority number
- RPN*
-
Normalized risk priority number
- SHELL
-
Software, hardware, environment, live-ware model
- SI
-
Semiconductor industry
- SPC
-
Statistical process control
- TPM
-
Total productive maintenance
- U-BN
-
Unsupervised Bayesian network
- WO
-
Work order
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Ben Said, A., Shahzad, M.K., Zamai, E. et al. Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach. Cogn Tech Work 18, 193–213 (2016). https://doi.org/10.1007/s10111-015-0354-y
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DOI: https://doi.org/10.1007/s10111-015-0354-y