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