Minitab co-organized Techmax to arrange a seminar named “Machine Learning enhance Product Reliability from Prediction to Execution” on 12th July 2018. HKPC was executive organization and Hong Kong Society for Quality (HKSQ) and ASQ (HK) were supporting organization. HKSQ former chairman Dr. Albert Tsang was invited to be one of guest speakers. I took a photo with Dr. Albert Tsang for memory.
The first speaker was Mr. KT Li (Consultant, Reliability Testing Centre, HKPC) and his presentation topic entitled “Introduction of Reliability Engineering & Highly Accelerated Life Test (HALT)”. He briefed the definition of reliability that “The ability of an item to perform a required function under given conditions for a given time interval”.
Reliability is a probability that an item will last a certain time without “failure” under certain conditions including function of time and conditions. Traditional QC assures the product will work after assembly and as designed. Then Mr. Li mentioned the testing and reliability engineering as following diagram.
Then Mr. KT Li stated reliability metrics as follows:
MTBF – Mean Time between Failures
MTTF – Mean Time to Failure
MTTR – Mean Time to Repair
Median Life (50% failure)B10 Life (10% failure)
And then Mr. Li explained Censored Data that “censoring is a condition in which the value of a measurement or observation is only partially known.” There were three types of data named “Complete and Censored Data”, “Interval Censored Data” and “Left Censored Data”.
Finally, he briefed the accelerate life assessment flow to us and shared different case studies including Failure of Compact Fluorescent Lamp (CFL), 7 Inch Tablet, Control Board and Power Board of Printer, Outdoor Sprinkler Timer, Wi-Fi Base Station, etc.
Dr. Albert HC Tsang (Former Chairman, HKSQ) was the second speaker and his presentation topic named “Life Data Analysis for Maintenance Optimization & Reliability Improvement”. After introduction of HKSQ, he briefed four subjects to be discussed and they were:
1. Evidence-based Maintenance Decision Models
2. Characterizing the Risk of Failure
3. Life Data Analysis4. Statistical Tools for Reliability Improvement
Typical maintenance decisions for when and how frequent to do preventive replacement after inspected items. “Preventive replacement will make sense only if it can reduce the risk of failure (hazard rate).” Dr. Albert Tsang said. Then he briefed the preventive replacement cost and optimizing such cost.
And the Dr. Albert Tsang said things fail because applied stresses (load) larger then strength. He introduced that safety factor (uS/uL) and margin of safety ((uS - uL)/uL) but they did not consider uncertainty of Load and Strength. Dr. Tsang showed two design with same safety factor but different reliability.
After that Dr. Tsang stated the Bathtub Model which had high failure rate in the beginning and then kept constant unit wear-out failure. Sources of product life data came from in-house laboratory testing, beta site testing, audit testing and warranty & other field data. Dr. Tsang mentioned how to use Weibull Distribution to show the Bathtub Model through ꞵ = 0.5, ꞵ = 1.0 and ꞵ = 2.5.
Finally, Dr. Albert Tsang briefed two types of failure below.
Hard Failure: Complete loss of function.
Soft Failure: Occur when a critical performance parameter has degraded to a predefined level – the item continues to function, but unsatisfactorily.
He also introduced different statistical tools for reliability improvement as follows:
- Evaluating individual failure modes
- Stratified and regression analysis
- Degradation analysis
- Accelerated testing
- Robust-design experimentsDr. Albert Tsang introduced his book at the end if participants were interested in Reliability.
The third speaker was Mr. Ming Dong (Senior Technical Training Manager, Minitab Pty Ltd) and his presentation was “Using Minitab and Machine Learning Tools to Increase Maintenance Reliability and Reduce Defective Products”. He said reliability was “The probability (0 ~ 1) that a system or product would perform its intended function under normal operation conditions for a specified period of time.
Then Mr. Ming Dong used an example to describe the limitations of assets that engineer test the individual wires in a dragline for their breaking strength. Risk from cable damage were Electric Shock, Ignition of gas or dust, and Fires. Engineers wanted to determine the probability that a wire can withstand 50 tons of force and at what load 0.1% of those wires start to break?
The curse of dimensionality were described and referred to phenomena that arise when analyzing data in high-dimensional spaces that do not occur in low-dimensional situations.
1st Dimension – Large Number of Predictors (Variable Dimensions)
2nd Dimension – Large Number of Observations (Dataset Dimension)3rd Dimension – Complexity Due to Non-Linear Effects or Complex Interactions
After that Mr. Ming Done introduced “Salford Predictive Modeler (SPM)” that Salford Systems was a machine learning softward company founded in 1983 by Dr. Dan Steinbery after he completed his PhD in Econometrics at Harvard. Minitab acquired Salford System in March 2017.
Mr. Dong explained machine learning was modern statistics plus computer science. Machine learning revolutionize manufacturing included manufacturing operation management, asset monitoring and tracking, as well as, product & business services.
Salford Predictive Modeler aimed to optimize a large plant / company / service with many complex process steps, many predictors, huge data sets, many complex nonlinear effects or interactions, many missing values and outliers.The terminology of machine learning were described as following diagram.
Minitab and Salford Predictive Modeler functionality comparison and they could complement with each other. At the end, Mr. Dong demonstrated the software to us.
Mr. KW To (IT Manager, HKPC) was the last speaker and he introduced the Technology Voucher Programme (TVP) and its related contents.
Minitab – www.minitab.com
HKPC – https://www.hkpc.org/zh-HK/
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