mosycousis intelligent monitoring system based on acoustic ... · 5 task 1.1 – collecting...
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MOSYCOUSIS Intelligent Monitoring System based on Acoustic Emissions Sensing for
Plant Condition Monitoring and Preventative Maintenance
2
Introduction
3
MOSYCOUSIS project overview
Industrial production failures Replacement costs, man-hour resources and downtime losses.
Predictive maintenance. Operating risk reduction Plant failures minimizations Equipment reliability Production maximization
4
MOSYCOUSIS project overview Support Predictive maintenance
Allowing convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. Earlier information and reliable diagnosis
Main value
Requirements
Current solutions expectations
Manual procedure Expensive devices Simple diagnosis algorithms User experience
Automatic procedure Low-cost devices High diagnosis analysis Untrained users maintenance Flexible & Modular
5
Task 1.1 – Collecting demands - Commercial solutions
Ultrasonic Ultraprobe 100: € 1400.00 Ultrasonic Ultraprobe 9000: € 5500.00
The Ultraprobe senses high frequency sounds produced by operating equipment, leaks and electrical discharges. It then electronically translates these signals down into the audible range so that a user can hear these sounds through a headset and see them as intensity increments on the Ultraprobe "gun's" LED meter.
Ultraprobe, electrical, mechanical or leak detection by ultrasound level
Indicators dB in time, Acoustic level in frequency
Others from 20KHz to 100KHz, response in < 10 miliseconds, Trisonic Scanning Module.
Similar Products
6
MOSYCOUSIS project overview
Full Acoustic Emissions analysis
Energy autonomous system
Wireless communication Location in non-accessible places
Advanced diagnosis algorithms
Application adaptability
Compact system
Application adaptability
Optimum electronic design
Expert System
7
Targeted customers
Development of a professional, effective, safe, and accurate mechanical equipment monitoring system for industrial machinery in manufacturing plants.
Main applications • Gearboxes and shafts • Slow speed bearings and gearboxes
Secondary applications • Split and planetary gearboxes • Alternative and reciprocating machinery
Electrical motors, gearboxes, pumps, yaw control and blowers.
8
Consortium structure
8 Beneficiaries / 5 countries 2 beneficiaries in Romania 2 in Ireland 2 in Spain 1 in Estonia 1 in Poland 5 Industrial enterprises 3 Research Centers or Universities 2 years project duration: from October the 1st 2011 to October the 31st 2013
9
Acoustic emissions data analysis
10
AE data analysis – Fault Characterization with AE signals
“STATIC” EXPERIMENTS: Experimental Setup: - Fatigue testing machine
- Tensile test machine Experiments: - Micro-Hardness Test Characterization of materials - Spherical Indentation Test (AE) Failure Mechanisms in Surface - Three Point Bending (AE) Fatigue Crack Initiation and Propagation - Fatigue Test in Gear Teeth
SIMULATION: Experimental Setup: - FEM software Experiments: - Different conditions (fault size) evaluated. DYNAMIC EXPERIMENTS: Experimental Setup: - Gearbox test bench Experiments: - Different gears conditions
Analysis of Acoustic Emission Signals WP2
11
AE data analysis – Fault Characterization with AE signals
Static Analysis of Acoustic Emission Signals • Long term testing were conducted to identify the relation between the
degradation procedure and AE signal characteristics.
Laboratory Set-up for the fatigue cycle testing in F114 Gears
12
AE data analysis – Fault Characterization with AE signals
Static Analysis of Acoustic Emission Signals • Generally, in fracture process, the acoustic activity of a material can be
classified in three phases : crack initiation, crack incubation, and crack propagation.
AE Event
Threshold
[2] Shi Z, Jarzynski J, Bair S, Hurlebaus S, Jacobs LJ. Characterization of acoustic emission signals from fatigue fracture , Proceedings of the Institution of Mechanical Engineers, 2000 214:1141-1149
Theoretical response
Experimental response
13
AE data analysis – Fault Characterization with AE signals
Finite Elements Models Simulations • Simulations with a gear finite element 2D model • Identify the relation of the AE signals with:
– Geometrical aspects of the gear. – Size of the fault. – The position of measurement.
t = 880 ns t = 2040 ns t = 5160 ns t = 8800 ns
FEM crack and mesh representations
Evolution and propagation of the AE wave
14
Report of the fault modeling system, acoustic emissions FEM simulations FEM SIMULATIONS Study of acquired AE signal
AE signature Vs Fault Size:
0 0.2 0.4 0.6 0.8 1
x 10-4
-2
0
2x 10
-5 FEM simulation:fault1finiteP1
Time [s]
Amplit
ude [ba
r]
0 0.2 0.4 0.6 0.8 1
x 10-4
-2
0
2x 10
-5 FEM simulation:fault2finiteP1
Time [s]
Amplit
ude [ba
r]
0 0.2 0.4 0.6 0.8 1
x 10-4
-2
0
2x 10
-5 FEM simulation:fault3finiteP1
Time [s]
Amplit
ude [ba
r]
Incr
easi
ng a
mpl
itude
of t
he A
E si
gnal
.
AE data analysis – Fault Characterization with AE signals
15
AE data analysis – Fault Characterization with AE signals
Dynamic Experiments • Experimental tests were performed with different types of
machinery in order to extract AE information and fault patterns in applications under laboratory conditions.
Basic gearbox test bench Screw test bench
16
Review of WP2 – Fault condition characterization and Acoustic signal response
Report of the feature extraction. DYNAMIC EXPERIMENTS: Time Domain Analysis
The AE signals are highly influenced by the speed conditions of the test [2].
- (v = 150, 250 and 450rpm) - (sensor: VS150-M) - (time: 1 revolution)
0 0.05 0.1 0.15 0.2 0.25 0.3 0.35
-10
0
10X: 0.1564Y: 9.859
Healthy Gear rotating at a speed of 150� RPM
Time [s]
Am
plitu
de [V
] X: 2.38e-05Y: 19.14
X: 0.2588Y: 7.221
0 0.05 0.1 0.15 0.2
-50
0
50 X: 0.121Y: 64.71
Healthy Gear rotating at a speed of 250� RPM
Time [s]
Am
plitu
de [V
]
X: 0.02803Y: 29.4 X: 0.2213
Y: 19.31
0 0.02 0.04 0.06 0.08 0.1 0.12
-50
0
50X: 0.007324Y: 88.78
Healthy Gear rotating at a speed of 450� RPM
Time [s]
Am
plitu
de [V
]
X: 0.03258Y: 50.3
X: 0.0668Y: 40.14
[2] Shen, G. T., Geng, R. S., and Liu, S. F., 2002, “Parameter Analysis of Acoustic Emission Signal,” Chinese Journal of Non-destructive Testing, 24, pp. 72–77.
17
Review of WP2 – Fault condition characterization and Acoustic signal response
Healthy Gearbox
Faulty Gearbox
18
Diagnosis Algorithm
19
Sensor description
20
Sensor General Operation
21
Sensor structure
AE Transducer moduleSignal Processing Unit Module
Intefaces Module Power Management Module
Local digital signal processor, ADC, Signal conditioning,…
AE Sensors
Wireless and Cabled modules Energy Storage and Management
22
ACQ Board AE conditioning board
AE Transducer module
BAND PASS FILTER 100Khz TO 400khz
Sampling frequency: 2Mhz
23
Main Board Signal Processing Unit Module
24
Signal Processing Unit Processor core Low power family 1.8V-3.3V Sleep, stop and standby mode 32 bits processor
Peripherals: 1. USB 2.0 2. DIGITAL I/O 3. SERIAL COM. 4. HIGH SPEED ADC
Signal Processing Unit Module
25
AE data analysis – Diagnosis Algorithm Implementation
Sensor’s Algorithm flow chart
AE Signal Acquisition
Signal Conditioning
Signal Proccessing
Fault Indicator Storage
Historic Evaluation
Zigbee Transmission
1 2
34
Algorithm Objectives: • Isolate the spectral content from the AE signal in the bands of interest. • Detect the presence of high frequency content in the AE signal. • Find the frequency band with the highest AE energy.
Hard
war
e Software
AE signal acquisition considerations:
• 20.000 Samples @2MHz, 12 bits resolution.
• 10 ms of signal.
Algorithm Explanation
26
Review of WP2 – Fault condition characterization and Acoustic signal response
MOSYTRON as the resulting machine health indicator
150 250 350 450 550 650 750 850 9500
10
20
30
40
50
60
70
80
90
100
Speed (RPM)
Mo
sytr
on
Va
lue
MOSYCOUSIS Mosytron Value
Severe FailureContinuing DegradationInitial DegradationGood Condition
Machine Health Condition
Stage 1: From 00 to 20 %: • Status: Good Condition. • Periodic inspection of sensor’s alarm.
Stage 2: From 21 to 50 %: • Status: Initial Degradation. • Common maintenance actions.
Stage 3: From 51 to 85 %: • Status: Continuing degradation. • Carry out Expert system full analysis.
Stage 4: From 86 to 100%: • Status: Severe failure. •Mechanical components replacement.
Non dependent of the laboratory test
conditions!
27
Zigbee Board
Zigbee module
Zigbee- uC SPI protocol communication. Totally turned off if is not trasnmitting.
Intefaces Module
28
Zigbee transceiver
WSU concept
Local communication
Wireless
Intefaces Module
29
SOLAR
24Vdc
TEG Vibration
S-caps
Energy Harvesting Board
Power Management Module
30
BOARDS DESCRIPTION
MOSYCOUSIS Sensor Integration
31
Digital outputs
Status Led AE inputs
Mechanical Design
32
Mechanical Design
24 Aux. input
EH inputs USB 2.0
33
Added Features
WSU concept
Local communication
Expert System
Power and Data transfer
Wireless
Wired
34
Comms network and expert system
Graphical User Interface
Default view in ES user interface
35
Comms network and expert system
Sensor status window
36
Industrial Validation
37
AE data analysis – Industrial Validation
General Set-Up Description
AE Trans.
Data LoggerZigBeeCoordinator
InternetAE
Trans.
MosycousisSensor
ZigBee Transciver
Diagnosis Information• Mosytron• R,G,B values
• Methodology:
– Initial Analysis: Retrieve AE data from the machines under different working
scenarios.
– Continuing monitoring: Long term experiment in order to test the real
functionality of the sensor .
38
AE data analysis – Industrial Validation
Set-Up in CGI facility
Compressor model ASEA Compressor model Sabroe
39
AE data analysis – Industrial Validation
Initial Analysis in Sabroe
0 5 10 15 20 25 30 35 40 45 500
20
40
60
80
100
Acquisitions
Mosytr
on v
alu
e
y
Pos 1 MotorPos 2 Motor D
Time vs. Frequency domain AE signal
0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01-0.05
0
0.05
AE temporal form in SABROE Pos1 vs Pos2
Time [s]
Am
plitu
de [V
]
0 1 2 3 4 5 6
x 105
1
2
3
x 10-3 FFT in SABROE Pos1 vs Pos2
Frequency [Hz]
|Y(f)
|
Pos 1 MotorPos 2 Motor D
Mosytron Value in the Motorside
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