NATIONAL UNIVERSITY OF SINGAPORE
MCH5004: Mechatronics System Design
Miniproject (ECE_E1): Vibration Monitoring and Fault Diagnosis
1. Object
The miniproject is designed to provide hands-on experience in the application of an
accelerometer and digital signal processing techniques to vibration monitoring and
fault diagnosis of machines. Students are expected to set up the monitoring system
from components to detect pre-specified faulty conditions.
2. Introduction
The practice of machinery condition monitoring and fault detection has become
widely accepted in the industrial predictive maintenance market. The main reasons
are:

Availability of lower cost fault detection sensors and technology which can meet
the requirements of machine condition monitoring, and

Recognition of the importance of machinery condition monitoring for the purpose
of minimizing maintenance costs and production shutdown time.
A major consideration for the installation of a fault monitoring device lies with the
installation costs. For companies to consider installing these devices on a large
scale, the installation should be simple, requiring minimal reconfiguration of the
physical system, and yet sufficiently robust to provide suitable signals reflecting the
health of the machine.
One approach towards achieving this objective can be based on an analysis of the
vibration signal. This is very intuitive to the way an experienced operator will detect a
fault from the abnormal machinery sound/noise generated. The accelerometer is a
typical mechatronic device, which is able to yield an electrical signal representative
of the mechanical vibration. Analysis of this signal can provide insights into the
health of the machine.
In this project, this approach will be adopted to develop a fault detection system. An
accelerometer is used to derive the vibration signals. The system should be able to
run under two modes. A learning mode can be initiated with the machine running
under normal functional conditions. This mode can be reinitiated any time when the
operator feels a re-learning/re-training is necessary (for example, after the machine
has undergone modifications/retrofitting). Thus, normal vibration signatures can be
extracted in this way automatically. Thereafter, the monitoring system can enter a
continuous monitoring mode, where the vibration signals are continuously acquired
and compared to the pre-acquired “normal” signatures. If the deviation from the
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signature exceeds a certain specified threshold, an alarm can be raised to alert the
operator of possible machine malfunction.
The student will develop this monitoring system independently from components.
Although, the development will be mainly PC-based, a demonstration will be done
towards the end to show how the same function can be readily achieved on an
embedded DSP device, so that the monitoring device is portable and appropriately
sized for practical implementation.
3. Equipment
The equipment available for use in this mini-project include:

Pentium workstation

PCI-MIO-16E-4 data acquisition card (with LabVIEW)

Accelerometer

Shaker table

Embedded DSP device and development kit
4. Preliminaries
4.1 Hardware Configuration
The schematic diagram of the proposed fault detection system is as shown in Figure
1. The hardware, to be used in the development of the fault detection system,
consists of a National Instrument's data acquisition card PCI-MIO-16E-4 installed in
a Pentium workstation. The acquisition card supplies two channels of analog output
and up to eight channels of analog input. The accelerometer acts as an interface
between the motor (or machine) and the data acquisition card. It converts
mechanical acceleration into electrical signals. The analog electrical signals from the
accelerometer are then acquired by the DAQ card as raw data and stored in the PC
for further conditioning and analysis.
Accelerometer
M
Pentium
workstation
LabVIEW
DAQ card
Motor or
machine
Figure 1: Schematic diagram of the data acquisition system
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4.2 Software Development Platform
The recommended software development platform for this miniproject is Laboratory
Virtual Instrument Engineering Workbench (LabVIEW). It is a graphical programming
language. The software is windows-driven, and various functions can be performed
simply by placing icons and carrying out the soft-wiring connections. LabVIEW is
integrated fully for communication using standards such as GPIB, RS232, RS485,
and it facilitates interfacing to plug-in data acquisition cards.
Using LabVIEW, 32-bit compiled programs that provide fast execution speeds
needed for custom data acquisition, test and measurement solutions can be created.
As LabVIEW is a true 32-bit compiler, standalone executables can also be created
easily. It contains comprehensive libraries for data collection, analysis, presentation
and storage. Traditional program tools are also included in LabVIEW. Breakpoints
and single-stepping through the program can be done in the LabVIEW environment.
These make debugging and program development easier. LabVIEW provides
numerous mechanisms for connecting to external code or software through DLLs,
shared libraries, ActiveX and more. In addition, many add-ons toolkits (for example,
the internet toolkit and DSP toolkit) are available for a variety of application needs.
The student is expected to produce the software to implement the two operational
modes (learning and monitoring modes). In the learning mode, appropriate vibration
signatures (to be specified in Section 5) are to be extracted for subsequent
comparison purposes. In the monitoring mode, the status of the machine is to be
closely monitored with respect to the “normal” signatures. This development will
encompass the use of digital signal processing techniques and tools.
5. Recommended Procedures
The mini-project is allocated five sessions in total for one semester, comprising of a
lecture followed by four 3-hours laboratory sessions. A brief description of the
recommended activities to be accomplished in each session is given in this section.
Self-study sections are expected to be included in the report.
NOTE: If you are new to LabVIEW, it is compulsory to go through the online tutorial
on LabVIEW before coming to the lab. After going to the webpage
http://www.ni.com/academic/students/learn-labview/, you should follow the 9 parts as
shown in Figure 2. This tutorial is video-based and interactive, and it should take less
than 90 minutes to complete. You can download the LabVIEW trial version to get
hands-on practice. At the end of this tutorial, we assume you should have learnt
about basic programming in LabVIEW.
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Figure 2: LabVIEW online tutorial
Session 1:
Familiarization with LabVIEW and the Accelerometer Hardware
In this session, you will be briefed on the key requirements of this mini-project and
the expected deliverables.
The main objective of this session is to familiarize the students in using LabVIEW to
acquire data from acceleration sensing. There are two parts for this session.
Part 1: LabVIEW Data Acquisition (60 minutes)
To enhance your skill in LabVIEW signal processing, especially with regards to tools
used in this project, students are also recommended to go through the examples and
demos that are listed under http://www.ni.com/academic/students/learn-daq/.
In particular, the following examples are relevant to this project.
-
Acquire and generate data
-
Create applications (adding data logging, monitoring and alarming)
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Part 2: Accelerometer (90 minutes)
Accelerometers (a typical mechatronic device) are available in a large variety of
sizes, shapes and styles to suit a broad range of test and control applications. Each
accelerometer series is designed with a particular group of measurement and control
applications in mind. In this application (referring to Figure 1), the accelerometer acts
as an interface between the motor (or machine) and the data acquisition card. It
converts mechanical vibration into electrical signals.
The pin configuration of the accelerometer is provided in Table 1. The lab assistant
will give you a demo on how to connect the hardware and obtain the output signal.
You should explore the data acquisition process in this session. Guidelines are given
below:
1.
The outputs of the accelerometer are to be connected to the LabVIEW
DAQ card supplied. Connect the ‘Sensor’ wire of the accelerometer to the
analog input of the DAQ card, the ‘Ground’ wire of the accelerometer to
the analog ground of the DAQ card.
2.
Create a new control panel in LabVIEW that reads in the signals from the
accelerometer. The control panel should read in the data from the same
analog input as the accelerometer is connected. Use a waveform chart VI
to display the data obtained.

Provide a plot of the signal when the accelerometer is unexcited
(stationary).

What do you think are the signal sources giving rise to this non-zero
signal even when there is apparently no motion?
3.
Excite the accelerometer manually and observe the output on the
waveform chart of the VI created. Is the time interval between consecutive
digital samples a constant according to your specification? If not, provide
some possible reasons and explain how a constant sampling interval can
be reinforced.
4.
Using the control panel that was created earlier, read in the analog signals
from the LabVIEW DAQ signal accessory’s on-board function generator
(see Figure 3). The function generator produces a 2V (peak-to-peak
voltage) sine wave and square wave. Connect the output terminals of the
function generator to the analog input terminals as shown in Figure 3. Use
the Frequency Range selector switch to select the frequency range of 1
kHz to 100 kHz. Fine tune the signal frequency to 5 kHz by turning the
Frequency Adjust knob. Use a waveform chart VI to display the signal
obtained. Verify with the laboratory assistant that your control panel is
working properly.
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Figure 3: LabVIEW DAQ Signal Accessory parts locator diagram
Table 1: Pin configuration of the accelerometer
Colour
Pin
Signal
Red
1
5V DC
Black
2
Ground
White
3
Sensor
Table 2: Specifications of the accelerometer
Description
Value
Remark
Span (G)
4
5%
Sensitivity (mV/G)
500
5%
Bandwidth (Hz)
DC-100
5%
Noise (mg rms)
5
Typical
Zero G output (V)
+2.50.1
@25 deg C
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Zero G Drift (mV)
60
0-70 deg C
Span output (V)
2.00.1
@25 deg C
Nonlinearity (%FS)
0.2
Typical
Temperature range(deg C) -40 to +85
Alignment (degrees)
2
Shock (G)
1000
Output loading
10K 1nF
Supply voltage (V)
+50.25
Supply current (mA)
8
Typical
Max
Typical
Table 3: Specifications of the DAQ card PCI-MIO-16E-4
Specifications
Input characteristics
1. Number of channels
16 single-ended
2. Type of ADC
Successive approximation
3. Resolution
12 bits, 1 in 4096
4. Max sampling rate
500 KS/s (KS: kilo samples)
5. Input coupling
DC
6. Input range
5V
Bandwidth
600kHz
Digital I/O
1. Number of channels
8 input/ output
2. Compatibility
TTL/CMOS
3. Power-on state
Input (High-Z)
4. Input Low voltage
0.0 to 0.8 V
5. Input High voltage
2.0 to 5.0 V
Slew rate
20 V/s
Power requirement
+5VDC (0.9 A)
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Self-Study Report

Explain in details the operational principles of any type of accelerometers,
showing in particulars how acceleration is finally converted into a final
standard full-scale electrical signal.

The specifications of an accelerometer are provided in Table 2. Explain the
implication of each of the specifications.

Apart from providing vibration signals as it is used in this project, where do
you think are the other possible application areas of an accelerometer?
The analog output of the accelerometer can be converted into a digital signal and
stored in the PC using the LabVIEW DAQ card (Figure 1). The LabVIEW DAQ card
to be used is the PCI-MIO-16E-4 type (Refer to the Appendix for the pin
configuration of the DAQ card). The important specifications are given in Table 3.
Self-Study Report

With reference to these specifications, what is the resolution of acceleration
measurement achievable (in G/bit)?

Given the maximum sampling rate, what will be the maximum frequency
component in the input signal that can be analyzed? Given this maximum
frequency, what then will be the range of mechanical vibration that can be
reasonably monitored using this setup?
NOTE: Any block function, if not found through the paths specified below, can be
searched through the Search Palettes function.
Session 2:
Fault Diagnosis in Time Domain
In this session, the main objective is to perform time-domain analysis on the
acquired digital signals to derive the required information necessary for machine fault
detection and diagnosis.
This session is divided into two steps. First, signal processing techniques must be
used to extracts time-domain features from a reference signal. These features will be
stored under the form of variables or a data file. Next, a monitored signal is observed
online (continuously) to compare its feature with the previous obtained set. The two
signals here are the vibration signals of the same machine under normal and fault
operation modes. Alarm can be raised when the comparison shows unfavourable
differences.
In the time domain, a simple way to detect fault is comparing the peak in the
acquired signal with the acceptable maximum value. Alternatively, the root-mean-
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squares (RMS) of the time-domain signal can also be compared with the acceptable
values.
The following steps need to be performed:
1. Implement a diagnosis algorithm that obtains the signatures of a signal.
Remove the offset in the signal and use either of the two ways:
-
Use any two of the following three function blocks: Amplitude and Levels,
Histogram, Statistics. Try to use at least two features for the diagnosis.
-
Matrix manipulation in with Matlab Script VI.
Loops can be used to update the desirable values through a time period. For
example, a peak-to-peak value must be updated with the highest output from
the Amplitude and Levels bock.
Save these values as variables or as data in a file. Test your program using
simple standard signals.
2. Develop the fault detection platform. Data from the learning mode will be
passed to the monitoring to perform comparison.
The learning mode and monitoring mode can be implemented through the
Case or Sequence structure. Mode 1 is the learning mode where your
program should derive the vibration signature. Mode 2 is the monitoring mode
where your program should carry out the continuous monitoring function and
provide the alarm signal where adequate. The alarm can be triggered and
maintained until it is reset by the user. Implement a LED to light up when this
occurs.
Sessions 3: Fault Diagnosis in Frequency Domain
In the frequency domain, the Fast Fourier Transform (FFT) can be used to provide
information on the frequency contents of the signal. This can be a more sensitive
and revealing tool as compared to the earlier one. A component can be defective
and failing or may have already failed without seriously affecting the vibration levels.
Yet signs of these defect or abnormal vibrations can be found in the spectrum of the
signal. The spectrum of the vibration can be obtained in frequency domain using
FFT. Thus, by studying the frequency spectrum, machine fault detection and
diagnosis can be done.
Self-Study Report

Explain how Fourier Transform works.

Explain the relationship between FFT and DFT (Discrete Fourier Transform),
clarifying why FFT is the popular choice in practice.
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In this application, FFT can be used to derive the resonant frequency (frequency
component with the maximum amplitude) and compare with the acceptable one.
Alternatively, the root-mean-squares (RMS) of a frequency range can also be
compared with the acceptable values. Alarm can be raised when the comparison is
unfavorable.
Follow the below procedure to carry out fault detection in frequency domain:
1. Implement an algorithm to perform FFT on the vibration signals by
-
A standard FFT function to extract the resonant frequency of the vibration
signals can be obtained from one of the combinations: FFT Power spectrum +
Peak detection, or simply Tone extraction (equivalent to Extract single tone
information).
-
Functions imported from Matlab with the Matlab Script VI.
Save the extracted information into variables or a data file (for example,
5 ). Test your programs using the standard signals.
2. Implement the two modes of the system and make them executable online.
You need to compare the resonant frequency of the monitored signal to a
segment around the learned resonant frequency (the so-called envelope, in
this case, 5 1 ).
The above approach can be used when the faulty frequency component is so large
that it affects the resonant frequency (the largest tone in the signal). For a more
subtle detection, consider examining two or more tones. Denote the detection
resolution , you should construct not only one envelope around the resonant
frequency, but a series of envelopes that cover also the 2 -largest, …,
- largest
components in the signals. After that, you need to examine if components of the
monitored signal fall within these envelopes.
For illustration purpose, refer to Figure 4. In the learning modes, two envelopes
around
5, 10
are constructed. In the monitoring modes, selected components
of the monitored signals with sufficient power are examined (to ignore small noise
signals, they should be larger than 1). Consequently, the 18
component is
acceptable, but the 15
component will raise the alarm.
NOTE: You can use the VI Extract multiple tone information to complete this task.
In the next session, your program will be tested on a real-time motion system by the
laboratory assistant.
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Figure 4: Illustration for multiple tone detection.
Session 4:
Real-time Test on Shaker Table
In this session, the main objective is to perform fault detection and diagnosis on a
real platform, i.e. shaker table (Figure 5) itself. The fault diagnosis programs (written
in the earlier sessions) should take in the data directly from the shaker table and
perform the fault detection and diagnosis. The laboratory assistant will excite your
shaker with a normal signal. Your program should learn from this experiment.
Subsequently, the assistant will excite your shaker table with other signals; your
program should be able to differentiate the excitation which gives rise to an abnormal
machine operational condition from those which result in a normal one. The
laboratory assistant will note your performance.
Self-Study Report

Do a survey of embedded DSP systems available and their applications.

Report on the various key components in a typical embedded DSP system.
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Figure 5: Shaker table and its connections
6. Report
The program and results of each stage of the mini-project should be properly logged
and explained. All self-study portions should be addressed. Adequate comments and
explanation on the flow and logic of the overall program should also be provided.
References
Application notes and other references on National Instruments LabVIEW webpage,
“http://www.ni.com/academic/students”.
Clarence W. de Silva, Vibration, Fundamentals and Practice, CRC Press LLC:
Washington DC, 1999.
C. D. Johnson, Process Control Instrumentation Technology, 4th Edition,
Regents/Prentice Hall.
Robert W. Ramirez, The FFT Fundamentals and Concepts, Prentice-Hall Inc.,
Englewood Cliffs, 1985.
A.S. Sedra and K.C. Smith, Microelectronic circuits, New York: Holt, Rinehart and
Winston, 1982.
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Appendix
Vibration Monitoring and Fault Diagnosis