276 IEEE TRANSACTIONS ON EDUCATION, VOL. 42, NO. 4, NOVEMBER 1999

Applications of Dynamic Data Analysis: AMultidisciplinary Laboratory Course

Sally A. McInerny,Member, IEEE,Harold P. Stern,Member, IEEE,and Tim A. Haskew,Member, IEEE

Abstract—This paper describes a junior-level multidisciplinarylaboratory course centered around industrial applications ofdynamic data acquisition and analysis. The course was developedwith funding from an NSF Instrumentation and LaboratoryImprovement (ILI) grant and is offered as a NSF FoundationCoalition (FC) course. It is also open to traditional aerospace,electrical, industrial, and mechanical engineering students. In thecourse, teams of four (maximum) students with complementaryinterests and skills are drawn from more than one discipline. Itis intended that the associations developed within and among theteams will enable cooperative multidisciplinary design projects inthe senior year. The course consists of four weeks of introductorymaterial followed by four laboratory modules, each concern-ing a specific application of signal acquisition and analysis.Currently, these modules include speech encoding and enhance-ment, machinery sound power measurement, machine conditionmonitoring, and motor condition monitoring. Each module isindependent, so the modules may be presented in any order. Thecourse concludes with a small design project. Three instructorshave been involved in teaching the course, one from mechanicaland two from electrical engineering.

Index Terms—Communications, engineering education, ma-chine condition monitoring, power quality, signal processing,sound power.

I. INTRODUCTION

STUDENTS enter the COE at the University of Alabama(UA) in either the traditional or the foundation coali-

tion (FC) programs. Freshman and sophomore FC studentstake a coordinated series of courses that integrate calculus,physics, and engineering. These students make extensive useof technological tools and software and engage in team basedactive learning beginning in their first semester. The laboratorycourse discussed here serves as a junior-level elective forboth FC and traditional aerospace, electrical, industrial, andmechanical engineering (AE, EE, IE, and ME) students. Todate, only ME and EE students have enrolled in the course.

Manufacturing industries have seen explosive growth inpredictive maintenance programs relying in part on spec-tral analysis of machine vibrations. ME students consistentlyexpress an interest in this area as a result of their coop

Manuscript received July 20, 1998; revised March 29, 1999. This workwas supported by a 1995–1997 National Science Foundation ILI grant andThe University of Alabama. Later support from the NSF Foundation Coalitionallowed for curricular technology enhancements.

S. A. McInerny is with the Department of Mechanical Engineering, TheUniversity of Alabama, Tuscaloosa, AL 35487-0276 USA.

H. P Stern and T. A. Haskew are with the Departmental of Electrical andComputer Engineering, The University of Alabama, Tuscaloosa, AL 35487-0286 USA.

Publisher Item Identifier S 0018-9359(99)09161-X.

experiences. In both manufacturing and the electric powerindustry, there is a need for graduates with an understandingof the issues associated with the use of power semiconductormotor drives. Many of these issues are defined in terms of thefrequency content of the voltages and currents into and outof the drives.

One of the motivations for developing this course wasexpressed in a recent IEEE Spectrum in the “signal processingforum” column [1]. “DSP is no longer the sole province ofEE.” Researchers in other fields apply frequency spectrumestimation methods to “their data, which now comes in digitalform from an A/D converter. It is quite likely that studentsin engineering and science will work with experimental datathat must be analyzed with the FFT or more sophisticatedmethods, so a fundamental background in DSP looks moreand more like a necessity” [1]. The ME department at UAoffers courses in noise control, dynamic systems analysis, andvibrations while the EE department offers courses in electronicsignals and systems, and communications, electric machinesand power systems. With this course, the EE and ME curriculaoffer hands on experience in application specific dynamic dataacquisition and analysis.

II. COURSE OBJECTIVES

The objectives for this course are straightforward. First andforemost, students who complete the course should be ableto use and understand a two channel spectral analyzer. Thismeans the students must have a fundamental understandingof time and frequency domain analysis. It was our aim indeveloping this junior-level course to deemphasize DSP details(e.g., -transforms and digital filter design) and focus on amore qualitative, basic understanding of what the analysisresults mean. Interested students have the opportunity topursue DSP, communications, acoustics, vibrations, electricmachines, and power topics in greater depth in senior elec-tives. Other course objectives include strengthening curricularstrands in communications (EE), acoustics and vibrations(ME), and electric machines and power systems (EE) andproviding students with the background necessary to pursuemore sophisticated senior design projects.

III. COURSE STRUCTURE AND INSTRUMENTATION

The course begins with an introductory module covering thefundamentals of Fourier analysis and its digital implementa-tion. This is followed by a series of independent laboratorymodules centered around specific industrial and commercial

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applications of dynamic data acquisition and analysis. Thecourse concludes with each student team performing a two-week project based on one of the application modules. Teamsgive oral reports on their projects. The compromise betweentechnical depth and breadth of the topics covered in thecourse is under reconsideration. Presently, it appears that eitherone laboratory module or the culminating project should beeliminated. This would give students more time to absorb theapplication specific concepts and become proficient in the useof the different analysis hardware and software.

The three-credit-hour semester-long course consists of bothclassroom activities and laboratory exercises. Many of theearly classroom activities are interactive, making use of PC-based demonstrations and exercises. This requires a classroomequipped with networked computers and projection capabili-ties for the instructor’s PC. During the fall semester of 1996,when the course was offered for the first time, MATLABmovies developed for the fundamentals section were availableon the network and on the WWW. Use could be made ofother WWW signal processing resources (see, for example,[2]) when deemed appropriate for this multidisciplinary course.The advantages of computer based instructional tools areclear. They include active involvement of the students andthe ability to rapidly construct and visualize solutions (e.g.,successive approximations to periodic waveforms obtainedwith Fourier series expansions). These are very importantadvantages, but there are disadvantages too, as discussed ina later section.

Team teaching makes this course a truly multidisciplinaryexperience for the students. One of the two instructors forthe Fundamentals module and the instructor for the SpeechEncoding and Enhancement module is an EE professor whospecializes in communication systems. The other instructorfor the Fundamentals module and the instructor for the soundpower and machine condition monitoring modules is an MEprofessor who specializes in noise, vibration, and machinecondition monitoring. The instructor for the Motor Monitoringmodule is an EE professor who specializes in power conver-sion systems and fault detection. Students get the benefit ofeach instructor’s enthusiasm for their specialized fields as wellas access to their laboratories.

In selecting instrumentation for the application modules, aneffort was made to obtain equipment and software representa-tive of that used in industry. A Stanford Research Systems SRS780 was chosen for the Fundamentals and Speech Encodingmodules based on a compromise between bandwidth and price.Although the basic spectral functions behind the acoustic andmachine condition monitoring applications are the same, therewas not an analyzer on the market that performed all of thenecessary calculations. Thus, two other types of dual channelanalyzers were purchased for the Sound Power and ConditionMonitoring laboratory modules. The analyzer purchased forthe Sound Power module is an ANSI standard Type I soundlevel meter, which also makes it useful for measurementsoutside of the classroom. The use of multiple, applicationspecific analyzers has the advantage of exposing the students avariety of analyzers. The analyzers and other instrumentationused in this course are listed in Table I.

TABLE IINSTRUMENTATION USED IN COURSE MODULES

Instrumentation Module in Which UsedStanford Research Systems SRS 780- 100 kHz, two-channel bench topanalyzer; function generators, filters

Fundamentals, Speech Encoding

100 MHz PC with sound card andspeakers; inexpensive microphones

Speech Encoding

Larson Davis LD2900 - portablebattery-powered two-channelreal-time analyzer with 1/3 octave,octave, FFT, 3.5" floppy drive; LD1260 sound intensity probe withphase matched microphones; LDCAL290 sound intensity calibrator.

Sound Power

Diagnostic Instruments PL302 -portable battery–poweredtwo-channel FFT analyzer/fatalogger (can perform run-up andrun-down testing, time synchronousaveraging, and standard spectralanalyzer functions); tachometer withoptical probe; two to six IMIindustrial use accelerometers withcables; PCB portable accelerometercalibrator.

Machinery Monitoring, MotorMonitoring/Power Quality

Lab Volt Power Electronics Station Motor Monitoring/Power Quality

The cost of instrumentation for this course would be lower ifa single analyzer were used for all of the laboratory modules.PCMCIA card-based spectral analyzers, which can be usedwith any notebook or desktop PC with a PCMCIA slot, arenow available on the market. (Strictly speaking, these analyz-ers do not have the bandwidth required for communicationapplications.) The cards have antialiasing filters, an analogto digital converter, and digital signal processing (DSP). InPC software, the recorded data can be converted into avariety of formats for importation into programs such as orMATLAB. Application specific software (e.g., sound intensitycalculations) has to be purchased separately or written by theuser, and external power supplies are needed for microphonesand accelerometers. A number of issues remain to be addressedbefore PC-based data acquisition systems can be used forstandardized acoustic measurements, where ANSI standardsapply.

IV. COURSE MODULES

The selection of the laboratory modules was based onindustry applications and the backgrounds of the instructors.With the exception of the Fundamentals module, which mustcome first, application-modules are independent and can bepresented in any order. The modular structure of the courseallows for the addition or deletion of modules in responseto changes in industry, as well as providing the team ofinstructors considerable scheduling flexibility.

A. Fundamentals Module

This initial module lasts five weeks and covers the basicconcepts of data acquisition and DSP. Topics include con-tinuous signal Fourier transforms and properties of periodicsignals, signal sampling and reconstruction, aliasing, linear andnonlinear quantization, instrument and system dynamic range,

278 IEEE TRANSACTIONS ON EDUCATION, VOL. 42, NO. 4, NOVEMBER 1999

fast Fourier transform relationships, windowing, and filtering.Emphasis is on the qualitative and applied aspects. Details ofdigital signal processing algorithms and digital filtering areomitted. Concepts are reinforced through laboratory experi-ments and MATLAB. Sensors are discussed only in the contextof example measurements, but are more thoroughly coveredin the later application modules in which they are used.

In order to begin acquainting students with the various defi-nitions and notations employed by instrumentation vendors,some of the course material is drawn from manufacturersapplication notes and other sources, such as web sites [2].Material is reproduced with written permission and includedin the course packet available for purchase at the studentbookstore. Lecture material is also drawn from standard texts[3]–[7].

B. Speech Encoding and Enhancement

This module demonstrates the data acquisition principlesand certain signal processing techniques used in speech en-coding and enhancement. Commercial applications of digitalspeech encoding and enhancement include new-generationdigital cellular telephones, speech encryption devices for pri-vacy, and compact disks. This module consists of experimentscovering performance evaluation of various speech encodingtechniques and the use of digital signal processing to reducebackground noise.

Using a PC-based data acquisition system, students beginthe module by making digital recordings of their voice, bothin a quiet environment and with background noise. Usingthe “quiet” recordings, students investigate various speechencoding techniques, including Pulse Code Modulation (PCM)at 64 Kbits/s and Delta Modulation (DM) at 16 Kbit/s. Studentsthen examine the performance of each encoding techniquein terms of speech quality, computational complexity, andencoding rate.

Next, students examine the recordings made with back-ground noise. Various processing techniques such as additionalfiltering and adaptive noise cancellation are evaluated. As withspeech encoding, the noise reduction techniques are rated interms of computational complexity and improvement to speechquality.

A potential senior design project related to this modulewould be the development of speech enhancement techniquesto improve the speech intelligibility and quality for a speaking-impaired individual. The project might be performed in co-operation with faculty and students from the Department ofCommunicative Disorders.

C. Sound Power Measurements

In this module, students gain an understanding of whymachinery sound power measurements are needed (hearingprotection and annoyance issues) and how engineers utilizesound power data. The basic equations for free-field acous-tic radiation and room acoustics are introduced. Decibels,1/3-octave levels, octave band levels, and A-weighting arecovered. The relationship of A-weighting to human hearingsensitivity is established.

Students are introduced to standards (ANSI and ISO) appli-cable to the measurement of sound power. Various methods ofsound power measurement are discussed: free-field (outdooror hemi-anechoic room) measurements, reverberation roommethods, and sound intensity measurements. The relativemerits of the three methods are discussed. The use of soundintensity in this laboratory provides the students with their firstexposure to the use of two channel measurements and issuesassociated with phase accuracy.

In the first laboratory exercise, steady-state noise is gener-ated with a white noise source, an amplifier, and speakers.Students calibrate the measurement system using a piston-phone and then record, for later comparison, 1/3-octave band,A-weighted 1/3-octave band, and high resolution FFT soundpressure level measurements. The measured data is recordedto analyzer memory, then transferred to a floppy disk for postprocessing in EXCEL.

In the second and last laboratory, students measure thesound power radiated by a dehumidifier (though any othersmall machine generating steady-state noise would work)using the scanning sound intensity method. The system iscalibrated with a sound intensity calibrator. Measurements aremade on five faces of a 1-m cube around the dehumidifier.The limits of the cube are defined by a frame constructed withwooden dowels. Although the calculations could be performedwithin the analyzer, students download the measured 1/3-octave band sound intensity data and calculate the overallsound power in 1/3-octave bands using EXCEL. In theirreports, students are asked to comment on the predominantnoise frequencies and radiation directions.

D. Machine Condition Monitoring and Diagnostics

This module introduces students to vibration measurementsfor machine health monitoring and diagnostics. Machineryhealth conditions reflected in the running speed andhigher harmonics (imbalance, misalignment, and loose cou-plings) are examined. Non-order-related vibrations are dis-cussed only briefly. Topics covered include sensors (prox-imeters, velocimeters, and accelerometers), optimal sensorlocations and mounting, and the use of RPM and orders(multiples of the shaft RPM) for the frequency scale.

In the first laboratory exercise, students use a tachometerand an accelerometer on a simple imbalance demonstrationunit (a disk with holes for imbalance weights mounted ona shaft supported between two bearings and driven by adirect coupled, variable speed motor). Students examine thevariability of spectral levels in a single spectrum and the effectsof spectral averaging. The tradeoff between spectral resolutionand measurement time are also examined. To demonstratethe effect of a soft foundation, measurements are made withand without the unit firmly tied down to a solid base. Withthe imbalance mass in place, time synchronously averagedacceleration data are recorded. In their write up, studentsare asked to explain the relationship between the timing ofpeaks in this time waveform and the relative locations of theaccelerometer, imbalance mass and tachometer trigger. Datais transferred from the analyzer used in this module using a

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PCMCIA SRAM card and then translated into ASCII for postprocessing in EXCEL.

In the final laboratory, students acquire and analyze vibra-tion data acquired on a 15-Hp induction motor. This motoris controlled by a variable speed drive and is available in aresearch laboratory. The load is provided by a dc generatorcoupled directly to the motor. On one side of the coupler,a steel disk with holes at two different radii is mounted.Industrial use accelerometers with plug in type connectorsare stud mounted to mounting pads permanently bonded tothe motor-generator. The optical probe and tachometer arealso used. Students acquire data with the system in “good”operating condition (balanced and aligned); out of balance(using solid 3/4-in bolts in holes of the disk); and out ofalignment (support legs on the outboard side of the motor arelowered). Students are asked to show how the imbalance andout of alignment conditions are reflected and differentiated inthe data.

E. Motor Condition Monitoring and Diagnosis

The use of adjustable speed motors is growing rapidlyin manufacturing and variable flow heating, ventilating andair conditioning systems. Associated with these drives arepower system harmonics, audible noise, motor overheating(due harmonics on the motor side of the drive), and insulationbreakdown in retrofitted and rewound motors. When thiscourse was offered for the first time, we chose to focusthis module on power system harmonics associated withvariable speed drives. Students are introduced to the basicprinciples behind three phase induction motors and pulse widthmodulated (PWM) motor control.

Laboratory demonstrations and procedures are performedon a Lab Volt power electronics training station consisting ofvarious power sources, machine loads, and power convertersat relatively low power levels. Procedures are performed witha strict focus on constant induction motor drives. Thestudents connect both the power and control stages of the ex-periment from the modular equipment within the work station.Voltage and current waveforms at various points within thesystem are collected for postprocessing and spectral analysisusing the tools and software covered in the fundamentalsmodule of the course. With the background developed at lowpower levels, the students move to a more industrial setting toperform experiments on a commercial 15-Hp variable speeddrive system.

The same 15-Hp variable-speed motor used in Module D isused for this module. Hall effect transducers are available tomeasure currents and resistive voltage dividers for voltages.Typically, current transformers and potential transducers areused in the field. Students operate the variable speed motor,record currents and voltages (time domain data and spectra),and develop an understanding of the harmonic distortioninduced by a PWM controller.

F. Culminating Project

For the final two week project in the Fall of 1996, eachteam chose from a list of projects proposed by the instructors

and drawn from the application modules. These included:demonstration and explanation of a database/trending programused for machine condition monitoring; measurements ofthe directivity and frequency distribution of sound generatedby a PWM controlled ac induction motor, as a functionof drive frequency; design and performance of a ventilatedsound reduction enclosure for an air compressor; frequencydependent impedance of an ac induction motor; and design ofvoice encoding techniques for Internet voice communications.Each team made a final presentation to the class and tothe instructors. Projects may be drawn from local industryin the future.

V. COURSE DIFFICULTIES

When interactive demonstration tools or computer-based ex-ercises are used in the classroom, students require considerablymore time than the instructor does to complete them. This hasa significant impact on the amount of new material that can becovered in a lecture period. These educational tools are bestsuited to longer instruction periods.

This course was conceived as a multidisciplinary,applications-based laboratory course incorporating team-based active learning elements. Laboratory reports andhomework were submitted and graded on a team basis. At theend of the semester, team members completed anonymouspeer reviews of their fellow team members. Based on thesereviews, adjustments were made to individual scores for teamassignments. This is a common method of adjusting teamgrades for differences in individual performance [8]. Threeexams constituting a total of 60% of the course grade weregiven during the semester. The exams were administered andgraded on an individual basis.

Our use of teaming and active learning has seen mixedsuccess. As a rule, groups of three perform better as a team(cooperating, sharing the workload, meeting regularly) thando groups of four. However, the most obvious disparity inteam performance is between groups that include two or moreFC students and those that do not. Compared to traditionalengineering students, FC students really work as a team. Seniorlevel students in the traditional engineering curriculum can bevocally averse to teaming.

Two measures suggested by the students to improve teamperformance are: 1) give the team the power to fire unproduc-tive team members; and 2) use anonymous team performancereviews after each laboratory module (not just at the end ofthe semester). We learned from our first semester experiencesthat team training exercises should be incorporated into theclassroom activities during the first weeks of class. Trainingmay well be a cure for many teamwork woes [8]. Studentscan be educated as to the need for teaming skills in theindustrial workplace, and methods for making a team workeffectively. Specific training can be included to address theissue of unproductive team members. The meaning and extentof teamwork in the course should be strongly emphasized inthe first class meeting.

While active learning most definitely took place in thelaboratories, it was not as fully employed in the classroom.

280 IEEE TRANSACTIONS ON EDUCATION, VOL. 42, NO. 4, NOVEMBER 1999

Even in the laboratories, there was substantial variation in theextent to which the three different professors employed activelearning. This resulted in comments on students’ appraisalssuch as:

“I think every module should follow Dr. X’s method whichis, explain, show us how to do them and have answers forall the labs.”

“Dr. XX makes us dig for things, but, as frustrating as itsometimes was, this was probably good.”

One student wrote on a course appraisal form “This classchanged my perspective in a lot of ways. I knew nothing aboutanything we covered prior to this class, but I realized howmuch I am able to ’self teach’ myself and draw from othersin their fields.” As with teaming, it is important that activelearning expectations be clearly stated on the first day of class.

We also learned in our first semester that maintainingconsistency in terminology, instructional methods, as wellas homework and laboratory report formats, is both veryimportant and not a trivial task. We did a good job ofmaintaining consistency in terminology and in continuityof subject matter, but were not as consistent in terms ofexpectations for homework, prelaboratory assignments, andlaboratory reports (despite established laboratory report guide-lines). Another issue that requires careful coordination isthe return of assignments and tests from previous modulesduring another professor’s classroom activity periods. Withplanning and ongoing communication, these difficulties canbe overcome. Further insights on team teaching can be foundin [9].

VI. CONCLUSION

A multidisciplinary junior-level laboratory course centeredaround diverse commercial and industrial applications of timeand frequency domain data analysis has been described. Thecourse incorporates team based, active learning, and beginswith five weeks covering the basic data acquisition and sig-nal processing concepts that underlie all of the applications.In the laboratory modules, coverage of application specificbackground material must be thorough, yet not too detailed.Team teaching provides students with instructors who areenthusiastic about their fields as well as providing them withaccess to specialized laboratories. Team teaching also requiresclose coordination between instructors in order to maintainconsistency and continuity. The course is a popular electiveand several former students are employed in fields where theyuse the skills acquired in the class.

REFERENCES

[1] J. H. McClellan, R. W. Schafer, and M. A. Yoder, “A changing rolefor DSP education,”IEEE Signal Processing Mag., May 1998, vol. 15,no. 3, pp. 16–18.

[2] G. C. Orsak and D. M. Etter, “Collaborative SP education ising theinternet and MATLAB,” IEEE Signal Processing Mag., Nov. 1995, pp.23–32, vol. 12, no. 6.

[3] K. Steiglitz,Digital Signal Processing Primer. Reading, MA: Addison-Wesley, 1996.

[4] F. J. Taylor,Principles of Signals and Systems. New York: McGraw-Hill, 1994.

[5] G. White, Introduction to Machine Vibration. Bainbridge Island, WA:DLI Engr. Corp., 1995.

[6] K. Steiglitz, A Digital Signal Processing Primer with Applications toDigital Audio and Computer Music. Reading, MA: Addison-Wesley,1996.

[7] V. Stonick and K. Bradley,Labs for Signals and Systems Using MAT-LAB. Boston, MA: PWS, 1995.

[8] Aldridge, M. Dayne, and T. Walter, “Cross-disciplinary teaming anddesign,”1996 ASEE Annu. Conf. Exposition, Session 0230, Washington,D.C., June 23–26, 1996.

[9] D. C. S. Summers and G. A. Bohlen, “Team teaching and interdisci-plinary course: Lessons learned,”1996 ASEE Annu. Conf. Exposition,Session 1275, Washington, D.C., June 23–26, 1996.

Sally A. McInerny (M’94) received the B.S.M.E. degree from CaliforniaState University, Long Beach (CSULB), in 1979. After working in industryfor several years, she attended graduate school at the University of California,Los Angeles, where she received the Ph.D. degree in 1987.

Following graduate school, she joined The Aerospace Corporation asmember of the technical staff and, a few years later, the faculty at CSULB.She was promoted and tenured at CSULB prior to moving to The Universityof Alabama (UA) in 1993. She is an Associate Professor in the Department ofMechanical Engineering at UA. Her research activities include fault detectionand diagnostics in helicopters (drivetrain) and variable speed motors. Herteaching responsibilities include courses in vibrations, acoustics, and appliedsignal processing.

Harold P. Stern (M’87) received the B.S.E.E. degree from the Universityof Texas, Austin, in 1978, and the M.S.E.E. and Ph.D. degrees from theUniversity of Texas, Arlington, in 1986 and 1991, respectively.

He is currently an Associate Professor of Electrical and Computer Engineer-ing at the University of Alabama. He is a participant in the NSF FoundationCoalition and is currently writing a senior-level textbook,CommunicationSystem Design(Englewood Cliffs, NJ: Prentice-Hall, to be published). Hisresearch interests include wireless and mobile communications, digital signalprocessing, speech encoding, modulation, advanced switching techniques, andprotocols.

Dr. Stern has received numerous teaching awards including the university-wide Burlington Northern Foundation Faculty Achievement Award and theNational Alumni Association Award for Outstanding Commitment to Teach-ing.

Tim A. Haskew (S’86–M’87) received the B.E.E., M.S., and Ph.D. degreesfrom Auburn University in 1987, 1988, and 1991, respectively.

He has been an Associate Professor of Electrical and Computer Engineeringat The University of Alabama since 1991. His research interests include powerelectronics and electric machinery with a primary focus on electromechanicalactuation systems.