525.778.8VL - Design for Reliability, Testability, and Quality Assurance

Course Structure

The course will be based on case studies, as is often done for law and management courses. However, it will have a strong quantitative and experimental focus. 

Illustrative assignments (actual assignments will be described in class and posted on Canvas): 

1. Compute  using Fourier and Monte Carlo techniques; reverse engineer the sqrt N spreadsheet; create a two outcome model and demonstrate by simulation that in order to reduce the uncertainty by a factor of 2, 4x samples are needed. Watch the Sarah Palin film about her vice- presidential campaign with respect to sqrt N sampling if you want to see a real world example. Explore the ISBN using Amazon. 
2. Generate a complete fault list for the provided digital circuit. Demonstrate a minimum set of test vectors that will reveal all single-stuck faults. Discuss in the context of a hidden, double-fault, and Poisson approximations (i.e., what is the probability of two errors that camouflage the existence of any faults during testing. Compute the number of electrical wires needed to control the lights on a trailer towed behind a car. 
3. Consideration of the M1496 double-balanced mixer, and the erroneous use of the wrong pin listing (metal versus plastic package) in the context of the test circuit, with respect to whether the test circuit will appear to work. 
4. Analyze and predict the experimental outcome of a relay test system. 
5. Measure a process (e.g., formation of lines at a grocery store) and decide whether the data support a Poisson model. Or, go to a produce stand and see if a bipolar model of the size of an ear of corn is valid. Or, measure gains of 2N3904 transistors, or compare 2N2906 with 2N2907 transistors; also compare their prices. 
6. Explore a medical problem (treatments for cancer, success of the shingles vaccine, whether mercury in vaccines causes autism) or anything to do with global warming. 
7. Complete your project. 
8. Incidence versus prevalence. 

Readings

Browse the entire book. Keep the text for future reference. Pay special attention to: 

1. The author’s preface material. 
2. The summary of useful distributions 
3. The various “bathtub curves” 
4. Coherent (1/N) and incoherent (1/(sqrt(N)) convergence 
5. Applicability of information theory and cryptographic concepts 
6. The central limit theorem 
7. Anecdotal information 
8. Software reliability 

Read and report on another book, read for enrichment, such as:

• The Soul of a New Machine 
• Making of the Wizard of Oz 
• Alone on the Wall
• Into Thin Air Into the Wild 
• A Gathering of Saints
• Airframe 
• Jurassic Park
• Andromeda Strain 
• Accidents in North American Mountaineering 
• Feynman’s dissenting report on the Challenger accident 
• Unsafe at any Speed The Double Helix 
• Anything about the following individuals: Frances Kathleen, Oldham Kelsey, Rosalind Franklin, Mark Hoffman, Rachael Carson, Henrietta Lacks
• Anyone else who interests you who has some connection to this course.

Assignment 1, due June 17th 

Identify as many features of your current living location, a family member’s house, etc. and identify safety features that are either present, missing, etc. Also, check the date stamp for one or more of the toilets. Explore whether there is asbestos, lead paint, ungrounded outlets, missing “drip traps”, un-tempered door glass, etc. 

Course Topics

Nominal Content summary, by lecture (one lecture per week for twelve weeks): 

1. Course summary, syllabus overview, discussion of projects; examples of reliability systems: ground fault interrupters, polarized plugs, isolated enclosures, double-insulated wires; the National Electric Code; Fire Code, incorporation by reference; Met Life actuarial tables; air bag safety and other bench-top examples. Feynman’s dissenting report on the Challenger disaster; organic versus non-organic materials; review of the text. Satellite autonomy rules and the New Horizons mission. The NEAR mission and it’s inadvertent one-year extension. The role of Greybeards: example, type 1 versus type 2 superconductors. 
2. Square root averaging, numerical computation of  by using Fourier and Monte Carlo techniques; tree diagrams, Poisson models, approximations, and computations; X-ray pulsars as an example of a time-dependent Poisson process. Margin of error. The central limit theorem. 
3. Thermodynamics and statistical mechanics; entropy, energy, and temperature; noise; bit error rate computations in communication systems. Introduction of fade margin in link budgets; convolution, aggregation; reversible and irreversible processes. 
4. Fault lists, fault tests, single-stuck faults, irreversibility and compression of fault trees to reduce the number of test vectors required to cover a given fault list; fault simulation: parallel, concurrent, etc. Deductive troubleshooting of faults in combinatoric networks; the role of redundancy; built-in test. The Quine-McCluskey algorithm. Cascade versus parallel network implementation. Electronic versus mechanical blinker relays for cars. 
5. Circuit design and simulation; testing of bipolar transistors without interrupting electrical connections; filter synthesis and its relationship to reliability; optimization of part value sensitivities; nonlinear effects: saturation, blocking, high Q filter dynamic range, physics of pendulums. 
6. Stability and instability of control systems. The Wien Bridge oscillator, Automatic gain controls. Pole-zero analyses, relation to partial fractions. Gain and phase margin. Implementation of stability controls in experimental aircraft. The yaw damper in the Boeing 737 aircraft. Probe calibration and the computation of . 
7. Phased-array antennas and their relation to Gaussian distributions and to binomial distributions, with reference to the normal distribution and the Poisson distribution. Catch-up and review of previous topics. 
8. Medical statistics. The cancer analysis flaw with respect to what constitutes a survivor. The polio vaccine. Familial diseases, genetic profiling, constant hazard rate processes, Cox- proportionate methods; Kaplan-Meier statistics; Patients like me. 
9. Least-squares; weighted least squares; Kalman filters; optimal estimation; review of square- root averaging. Use of system block diagram models. Mason’s gain rules, signal flow graphs, and generation of transfer functions; Laplace and Fourier; difference equations; stability of loans; simulation of loan processes using feedback control loops and circuit simulators. Toilet stability. 
10. Relay circuit design and testing. Improper use of snubber diodes. Adiabatic processes for accelerated testing. Garage door springs. How not to weigh a garage door. Date codes: use by, best by; RX one year dates versus medicine longevity dates; toilets. Begin project presentations. 
11. Project presentations. 
12. Project presentations. Course conclusion. 

Course Goals

To teach both the subjective and objective aspects of reliability, supported by an understanding of probability distributions that can be used for modeling, simulation, and prediction of the performance characteristics and reliability properties of a wide variety of systems. To learn the fundamentals of test vectors, fault lists, and test “coverage”. To learn about single stuck fault models for digital systems. To develop a healthy skepticism about open-source technical literature. To learn how to design and execute tests, particularly under an accelerated testing protocol. 

Course Learning Outcomes (CLOs)

• Compute probability tree diagrams using both Gaussian and Poisson approaches.
• Investigate and report on a real-world reliability analysis.
• Model and simulate reliability analyses.
• Design and implement, in hardware or software, a test of/or a stochastic system.
• Measure actual reliability data.
• Understand the notion of square root averaging using the computation of a numerical value for pi as an archetypical example.

Textbooks

Required 

O’Connor, Patrick P., Practical Reliability Engineering, 5th Edition, Wiley & Sons, 2012. 

Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website at http://ep.jhu.edu/bookstore. 

Other Materials & Online Resources

Accessibility to online resources and the ability to attend occasional online sessions using Zoom, with a headset and microphone (to eliminate feedback when speaking) is necessary. 

Required Software

MATLAB 

MatLab is optional, but you can gain access to a recent version of MATLAB with the Signal Processing Toolkit. The MATLAB Total Academic Headcount (TAH) license is now in effect. This license is provided at no cost to you. Send an email to software@jhu.edu to request your license file/code. Please indicate that you need a standalone file/code. You will need to provide your first and last name, as well as your Hopkins email address. You will receive an email from Mathworks with instructions to create a Mathworks account. The MATLAB software will be available for download from the Mathworks site. 

Excel, etc. 

Excel is always useful, as are any other programs/tools that you have experience with (Circuitmaker, MathCAD, etc.) 

Student Coursework Requirements

It is expected that each class, with preparation and homework, will take approximately 7–10 hours per week to complete (but compressed by 2x when the course is offered over a seven week period). 

This course will consist of four basic student requirements: 

Preparation and Participation (20% of Final Grade Calculation) 

You are responsible for attending class on a regular basis, although you do not need to notify the instructor if you need to miss an occasional class for work or personal reasons. 

Preparation and participation is graded as follows:

10 points per class attended, normalized to 20 points when computing its impact on the final grade. 

Assignments (50% of Final Grade Calculation) 

Assignments will include a mix of qualitative assignments (e.g. literature reviews, model summaries), quantitative problem sets, case study updates, and reading assignments. Include your name and assignment identifier, but not a separate cover page. Please upload the assignments to Canvas, although they can be submitted in hard-copy during class sessions. 

Restate the problem succinctly. Also include your name and a page number indicator (i.e., page x of y) on each page of your submissions. 

Each problem should have the problem statement, assumptions, computations, and conclusions/discussion presented with explanation, in expository form. 

Equations or computations presented without explanation will be given a 1 and returned for resubmission. All Figures and Tables should be captioned and labeled appropriately. Use graphics. Under no circumstances submit long lists of numbers, or more than one file per assignment. The assignments can be handwritten, and should be submitted as either .doc, .docx, or .pdf files. 

All assignments are due in a timely fashion (typically 1 – 2 class sessions after being assigned). Do not turn in a massive collection of assignments the last week. They will be penalized for lateness. In general, turn in your assignments when they are complete to your satisfaction and as you would turn them into your management at a career level position. 

If, after submitting a written assignment you are not satisfied with the grade received, you are encouraged to redo the assignment and resubmit it. If the resubmission results in a better grade, that grade will be substituted for the previous grade. However, “gaming the system” will not be rewarded. 

As a general example for Whiting courses, but to be taken here as general guidance, and using the percentages listed below also as general guidance, is a detailed grading rubric. For the purposes of this term, however, the following simplified simplified rubric will be used: 

10 points. Assignment is above and beyond what is normally expected (an A+). 

Typical grade for a competent, adequate submission similar to what constitutes an acceptable technical memo in an engineering environment (an A).

Assignment is incomplete or lacking in some significant way. It should be considered a B-, and resubmitted if possible.

A placeholder for an incomplete submission.

A place holder for no submission or an unacceptable submission. 

For reference per Whiting guidelines: 

Qualitative assignments are evaluated by the following grading elements: 

1. Each part of question is answered (20%) 
2. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content). 
3. Rationale for answer is provided (20%) 
4. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples). 
5. Outside references or mention of the text or lecture material are included (15%) 

Quantitative assignments are evaluated by the following grading elements: 

1. Each part of question is answered (20%) 
2. Assumptions are clearly stated (20%) 
3. Intermediate derivations and calculations are provided (25%) 
4. Answer is technically correct and is clearly indicated (25%) 
5. Answer precision and units are appropriate (10%) 

Individual Course Project (30% of Final Grade Calculation) 

A course project will be assigned several weeks into the course. The final few weeks will include in-class presentations of the course projects. Specifically, students will be asked to submit a summary of their project to the Canvas discussion forum for projects, and to present a short summary in class using ppt, handouts, whiteboard, etc. (A single presentation document is all that is required. An additional report is not required). 

The course project is evaluated using the 10-8-6-5-1 rubric using the following grading elements: 

1. Evidence of significant effort (40%) 
2. Student technical understanding of the project topic (30%) 
3. Quality of presentation (30%) 

Grading Policy

Grading turnaround for assignments: Assignments are nominally due as stated above, and graded on Canvas within one week. There is typically one assignment per week. 

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.