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Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach

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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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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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Authors and Affiliations

  1. STMicroelectronics, 850 Rue Jean Monnet, 38926, Crolles, France

    Anis Ben Said & Stéphane Hubac

  2. G-SCOP, Université de Grenoble Alpes, 38000, Grenoble, France

    Anis Ben Said, Muhammad Kashif Shahzad, Eric Zamai & Michel Tollenaere

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  1. Anis Ben Said
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Correspondence to Muhammad Kashif Shahzad.

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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