Big Data & Analytics

Introduction

Automotive Electronic parts are qualified before usage in serial production. When product quality and reliability are poor, then assembly problems, high warranty costs, poor service, and recalls occur. The OEM’s and suppliers followed different qualification plans.  Ford, GM, and Chrysler and large parts suppliers started the Automotive Electronics Council (AEC) in the 1990’s, with the mission of defining a suite of common qualification tests. 

The first qualification standard was Q100, which defined stress tests for electronic components containing integrated circuits. These components are in every electronic control module, some sensors, entertainment systems, safety systems, and, in the future, autonomous driving systems. 

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Introduction

When analyzing a continuous variable or type of measurement using statistics, an analyst often assumes data is normally distributed. But, how can this normal assumption be verified? While there are numerical normality tests, an alternate approach is to use graphical methods. The old adage, “A picture is worth a thousand words”. This captures the idea that the human mind is good at discerning patterns.

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Introduction

In my prior article, Measurement Systems, the total variation in the measurement data was partitioned into part variation (PV), assessor variation (AV), and equipment variation (EV). GR&R is the square root of the sum of the squares of AV and EV. If the ratio of GRR/TV is less than 10%, then the measurement system variation was acceptable.

In addition to variation, data bias needs to be considered. This bias is created by systematic measurement errors.
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Introduction

Statistical Process Controls (SPC) is a suite of methods that can be employed to control a manufacturing or assembly process. It has a wide range of potential applications ranging from consumer products to defense. It can be employed at the lowest element of component manufacturing or an assembly operation.

This article discusses the assumptions necessary to understand SPC.

Fundamental Assumptions

• The process is stable and in control.
• The data are independent of each other.
• The data of each subgroup are identically distributed.
• Real valued data are approximately normally distributed and counting data may be approximated by the normal distribution.
• A measurement can occur in only one subgroup, i.e., sampling without replacement.

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Introduction

Quality Costs for manufacturing or services can be categorized as prevention and appraisal costs, and internal and external failure costs. Control occur in prevention and appraisal activities, both of which rely on data. However, when data is collected, it shows variation. One must understand variation to know how to react.

Dr. Deming indicated that 94% of variation is from common causes and about 6% is from special causes. So what are the common and special causes of variation?
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Introduction

A state variable is a parameter that is categorized into a countable number of defined states. Examples would include transmission gear states, PRNDL positions, ignition switch states, and others. Sometimes continuous variable, like pedal positions, may be binned into discrete states to be displayed as a histogram.  State  change timing is unpredictable since vehicle operation is highly variable. A way to deal with this data is Markov Analysis.

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Introduction

Reliability and durability are essential in today’s competitive market place. However, component reliability verification tests and system durability tests take a long time, cycles, or miles to complete. This puts these tests in direct conflict with program timing, product development budgets, and limited testing resources. To minimize this conflict, it is essential to accelerate these tests.

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Extended Bogy Testing

Introduction

Extended bogy testing builds on test to bogy (TTB), discussed in a prior article. TTB focused on calculating the number (N) of parts tested to one life bogy, with 0 failures allowed, to a specified reliability (R) and confidence (C) levels.

Using TTB to verify conformance to high reliability and confidence targets requires very large sample sizes, increasing testing cost. The capacity to test large samples may require large facility capital expenditures. Also, the zero failures allowed paradigm removes the opportunity to learn about product failure modes and the opportunity to improve the product through design or manufacturing process changes.

This article focuses on extended bogy test plans as an alternative to TTB.

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