A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:Conclusion

Table of Links

• Abstract & Introduction
• Review of Related Work
• Proposed Research Methodology
• Numerical Case Studies
• Real-World Simulation Case Studies
• Conclusion
• References

VI. CONCLUSIONS

This paper proposes a novel sparse hierarchical Bayesian method, CSSBL, to effectively identify the sparse process faults in multistation assembly systems. The method identifies process faults by considering the spatial correlation of KCCs and nonstationary process faults among the multiple KPCs. Since posterior distributions of KCCs in the proposed method are computationally intractable, this paper derives approximate posterior distributions of KCCs via Variational Bayes inference. The proposed method’s effectiveness is validated by numerical cases and real-world simulation application using an actual auto-body assembly system. In this work, the temporal correlations of the KCCs are not considered. However, a temporal correlation exists among the KCCs because of the degradation of wear of production tooling over time [23] or the machine-tool thermal distortion [22]. Therefore, extending the proposed CSSBL to consider the temporal correlation of KCCs makes the method more realistic in the actual multistation assembly system. Since the proposed method is not designed for specific processes, it can be effectively applied to other domains, including healthcare and communication systems, for their process monitoring.

APPENDIX A

APPENDIX B

APPENDIX C

APPENDIX D

APPENDIX E

APPENDIX F