Environmental Data Acquisition
How to Design Effective Computer-Driven Test Measurement Systems
(DATA 501)
Summary
How do you know your mechanical engineering test measurements are valid? Since NIST Traceability actually guarantees very little about your test data, how do you know? Could you prove your data's validity to yourself and your customer? What are the right measurements solutions for your static and dynamic testing requirements? Is it really as simple as the vendors say? What is your real cost of invalid, ambiguous data causing retest or, worst of all, hardware redesign? How do you effectively educate yourself and your staff about mechanical test measurements when universities can't help you and your experience base is declining?
This three day short course is for engineers, scientists, analysts, and managers who must answer those questions and use those systems to make and understand experimental test measurements on a daily basis. The course will teach you the principles underlying design and operation of effective computer-driven measurement systems providing demonstrably valid test data on purpose, the first time, and on your tight test budget and schedule.
These fundamental & underlying engineering principles governing the design and operation of effective systems for test measurements are demonstrated experimentally. Understanding these critical design and data validation principles, not taught in American universities, allows you to field effective measurement systems with both today's and tomorrow's hardware and software.
The result in your laboratory will be skilled people running more effective testing programs, generating demonstrably valid and unambiguous data on time, lowered design verification risk, cost and cycle time, and delighted customers. Attendees receive both an 800+ page workbook and the instructor's book, Applied Measurements Engineering - How to Design Effective Mechanical Measurements Systems (Prentice Hall, 1995).
Course Outline
1. Basic Measurements Concepts. Fourteen real measurements horror stories and why they happened. Examples from across the testing spectrum. Could they happen to you? Measurements or instrumentation? Data validity or data accuracy? System Response Syndromes -- Why you want less than 1/16th of the information available from your system. Real world examples.
2. Measurement System Transfer Functions and Linearity. Frequency and phase responses -- more complicated and sensitive that most think. First, second and higher order systems. Single degree-of-freedom systems and damping. Building system transfer functions from components. Component - component isolation issues. Static and dynamic examples. Output/input linearity.
3. Frequency Content or Wave Shape Reproduction? Rules for the reproduction of frequency content. Rules for the reproduction of wave shape. What price do you pay when you violate the rules? How can you recover?
4. NonSelf Generating Transducer Responses. Load cells, strain gages, resistance temperature transducers, piezoresistive and servo transducers, etc. The basic transducer model. Proper techniques for system setup and operation.
5. The Wheatstone Bridge. The bridge as a computer. Bridge equations. Valid shunt calibration techniques and calculations. The three wire circuit. Up to ten wire circuits!
6. Self Generating Transducer Responses. Piezoelectric transducers. "Charge" amplifiers and why they work. The power of T-Insertion. Thermoelectricity and thermocouples. The gradient approach to thermocouple temperature measurements.
7. The General Transducer Model and Noise. How all transducers and components really respond and why.
8. Noise Level Documentation and Control Methodologies. Bulletproof procedures to identify, control and document your noise levels. You must identify them before you can kill them. You must do both before you can validate your data.
9. Information Conversion. Carrier systems and why they work. Sinusoidal and pulse train excitation for nonself generating transducers. Zero based and zero centered pulse trains. Digital data reconstruction using pulsed excitation. Real, world-class examples of this very powerful methodology.
10. Frequency Analysis. Fourier spectra. Power or auto spectral density. Octave and one-third octave analyses. Shock response spectra. What do they really tell you? What do you need to know to be in control?
11. Sampled Measurement Systems. The twelve things you must know before you sample. Nonsimultaneous or simultaneous sample and hold? Aliasing and undersampling errors and how to prevent them. What antialiasing filters should you use and why? Fast and slow sampling models. Reconstruction methods for sparsely sampled data.
12. Data Validation Methodologies. How do you know your data is valid? How to use your software to answer the question. Automated data validation methods. Back of envelope methods.
13. Knowledge-Based System Design Principles. The highest level of measurement system design.
14. Operating Effective Measurement Systems. World-class examples from the dynamics, thermal, and quasi-static structural mechanics test worlds.
15. The Subject of Software. Commercial software. Commercial vs. in-house developed software. Where's the risk? Who should do what to whom? Wright's Twelve Point Plan for Stressful Software Development!
16. The Crucial Stuff They Didn't Teach in Your College Engineering Education. The subjects of craft, skill, vision, responsibility, and professionalism as they relate to test measurements.
