The HKSQ seminar named “Reliability & Maintenance: Enablers of Industry 4.0” co-organized by Hong Kong Society for Quality (HKSQ), ASQ Hong Kong and Dept., of Industrial and System Engineering, PolyU. Industry 4.0 principles not only demand the availability of machines, they also recommend collection and analysis of machine data through shop-floor communication and cloud computing to enable optimization of reliability and maintenance (R&M) management. This seminar would present the way in which asset management decisions could be optimized by resolving the conflicts of a decision situation. Dr. Albert Tsang (Former chairman, HKSQ; ASQ Country Counselor (HK), author of “WeibullSoft” and co-author of “Maintenance, Replacement, and Reliability: Theory and Application”) was our guest speaker to share this topic.
In the beginning, Dr. Albert Tsang briefed the implications of Industry 4.0 to the Value Chain. It indicated on demand approach (e.g. lean production), high level of flexibility, data from central library and IoT, etc. He said that without effective asset management, Industry 4.0 is only a dream. The definition of Asset Management was “Systematic and coordinated activities and practices through which an organization optimally manages its assets, and their associated performance, risks and expenditures over their lifecycle for the purpose of achieving its organizational strategic plan. Then he briefed the objectives on sustainable manner at an optimal whole-life cost.
Then Dr. Tsang mentioned ISO 55000 family of standards and Reliability Centred Maintenance (RCM) which focused on failure modes and assuring system function.
For simplified version, we could read Dr. Tsang’s previous booklet named “Reliability Centred Maintenance: A Key to Maintenance Excellence”. (http://www.hksq.org/qts.htm )
Then Dr. Tsang discussed the Bathtub Model to explain the Early Failures, Chance Failures and Wear-out Failures as follow:
a) Early Failures – caused by poor workmanship
b) Chance Failures – caused by higher than expected random loads / Act of God
c) Wear-out Failures – caused by Aging, Wearing, Degradation, Creep, Fatigue, Poor service & maintenance / repairing, and short designed-in life, etc.
After that Dr. Tsang briefed the optimizing maintenance decisions that we want Evidence-based arguments (data driven decisions) but not Intuition-based pronouncements (strength of personalities, number of mechanics’ complaints).
Finally, Dr. Tsang considered the optimizing preventive replacement costs and avoided “Data Rich, Information Poor”. Then he briefed the reliability function included Weibull Analysis and Maximum Likelihood Estimation. He concluded that there was no the best model but mode helped us to make decision could be a good model.
We took a group photo of HKSQ exco members at the end.
HKSQ – www.hksq.org
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