Condition Monitoring, Fault Detection, Diagnosis and Remaining Life Time of Electrical Machines: What are the Differences? Gérard-André Capolino, PhD, DSc, IEEE Fellow Chair Professor of Electrical Engineering IEEE Industrial Electronics Society Distinguished Lecturer University of Picardie “Jules Verne” - Amiens - France Democritus University of Thrace Xanthi, Greece
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Outline
Introduction: the « wording »! Condition monitoring Fault detection: electrical, mechanical, thermal, insulation Diagnosis Remaining life time - Prognosis Fault-tolerance Trends and future in industry
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Introduction: the “wording” (1/6) What is an electrical machine? Why condition monitoring, fault detection, diagnosis, prognosis? Using the electrical machine as a « sensor » Electrical machines and sensors Electrical machines as multiphysics systems
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Introduction: the “wording” (2/6) What is an electrical machine?
1 – Stator core 2 – Stator windings 3 – Rotor
4 - Frame 5 – End shield 6 – Bearing 7 - Shaft 3
Introduction: the “wording” (3/6) Why condition monitoring, fault detection , diagnosis, prognosis? Electrical machines are key elements of electromechanical energy conversion: there is no other mean to perform the task Condition monitoring is almost mandatory in « sensible applications » (nuclear plants, aerospace energy, marine energy and more) Early fault detection allows predictive maintenance and avoids catastrophic failures Diagnosis is a way to inform about health, faults and their degree of gravity Prognosis is a way to predict failures before they will happen and/or end of life of the equipement 4
Introduction: the “wording” (4/6) Using the electrical machine as a « sensor »? Interactions with the grid or the power converter Interactions with the shaft load or the turbine
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Introduction: the “wording” (5/6) Electrical machines and sensors
Voltage Current Stray flux
Vibration Sound Torque Speed Position
Temperature
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Introduction: the “wording” (6/6) Electrical machines as multiphysics systems (very different time constants)
Electrical (with or without capacitive effects) Mechanical Thermal Chemical Environmental
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Condition monitoring (1/10) A brief history and basis Need of « sensors » and permanent recording of data 1960-1970: Health monitoring systems in hospital to collect patients parameters 1970: US Navy’s Integrated Condition Assessment System 1970-1980: Oil industry and off-shore platform CM 1980-1990: Aerospace industry and space shuttle development 1980-1990: Civil engineering (bridges, buildings and more) 8
Condition monitoring (2/10) Sensors and instrumentation Practicaly all has been passed at the industrial level
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Condition monitoring (3/10) Sensors and instrumentation Signal conditionner
Sensors for EM
DAQ
Transmission: local or remote
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Condition monitoring (4/10) Test-rig in labs: example 1 (CM for WTG)
Durham’s test-rig
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Condition monitoring (5/10) Test-rig in labs: example 1bis (CM for WTG) Real-time system Development PC
R ST
Ethernet link
Ethernet link Back-to-back converter
AC/DC
DC/AC
Acc1-Acc8 : accelerometers
Adjustable spring AC drive
Speed and torque (T, Ω)
Main bearing
Wind profile
Rotor currents (IRr, ISr, ITr) Rotor voltages (VRr, VSr, VTr)
Stator currents (IRs, ISs, ITs) Stator voltages (VRs, VSs, VTs)
Planetary gearbox Gear ratio = 6.57 Acc1 Coupling Acc3 Acc6 Bearing Bearing Acc2
Electrical characteristics of WRIM
Motor-gear unit Gear ratio = 10.9
Acc7
Acc5 Acc4
Amiens’ test-rig
Acc8
Pn = 5.5kW Vns = 380V Vnr = 190V Isn = 17A Irn = 19A p=8 Ωn = 700tr/min
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Condition monitoring (6/10) Test-rig in labs: example 2 (CM for gears and bearings)
Test-rig for shaft couplers, pulleys, pinions, gearboxes and bearings (Amiens) 13
Condition monitoring (7/10) Test-rig in labs: example 3 (CM for high speed traction)
Bogie at reduced scale: 1/200 (Amiens) 14
Condition monitoring (8/10) Test-rig in labs: example 4 (CM for crane simulator)
Crane simulator at reduced scale: 1/100 (Amiens) 15
Condition monitoring (9/10) Objectives: reliability improvement and usage optimization
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Condition monitoring (10/10)
Good face of the moon « Sensors » everywhere, DAQ everywhere, flow of data (big data), everything is known with accuracy and can be transmitted even remotely (« spy ») « Bad » face of the moon Need of methods to process data Need of robust expert systems to interpretate These are two opened problems Plenty of industrial CM systems have been commercialized but they have failed to be used on a long term basis (too close, too expensive, not enough flexible) 17
Fault detection (1/10) A brief history and basis Coming from academia in late 1970’s First papers on rotor asymmetries in induction machines (W. Deleroi et al.) Proposal of MCSA by G. Kliman from GE in late 1980’s Since then, thousands of papers mainly coming from academia have been published There are several methodologies: sensors, data, signal processing, decision process and more One conference fully dedicated to the topic: SDEMPED created in 1997 (next edition in 2017 in Greece) Few transfers to industry 18
Fault detection (2/10) What are faults to be detected?
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Fault detection (3/10) What types of sensors to be implemented? In labs, anything can be used (current, voltage, flux, speed/position, torque, temperature, vibration, noise and more) Acceptability for industry: no additional sensors, non-invasive sensors, reduced additive cost One current sensor is enough for MCSA, three current sensors are better for demodulation and space vector computation Stray flux sensors are acceptable since noninvasive and low cost equipments Other sensors are almost prohibited basically for invasive and cost reasons 20
Fault detection (4/10) MCSA: what is possible to detect with and how? MCSA has been proposed by G. Kliman for rotor bars fault detection One single sensor has been used with success until recent years Accuracy in data collection and frequency detection has been an issue for a while It is still an opened problem since no universal method in DSP has been proposed and adopted Problems are: stationarity of the signal (steady state does not exist in industry), length of data collection for frequency sensivity (equal to reverse of acquisition time), cost of DAQ systems (with 12-bit nothing is possible with 16bit or 24-bit DAQ, it is much better) 21
Fault detection (5/10) MCSA: what is possible to detect with and how? MCSA electrical fault detection: rotor broken bars, windings short-circuits, partial discharges
46,7Hz
46,7Hz
53,3Hz
Healthy machine
53,3Hz
One rotor broken bar
Sensivity ≈ 7dB fbc stator (1 2s) f s 22
Fault detection (6/10) MCSA: what is possible to detect with and how? MCSA electrical fault detection: rotor broken bars, windings short-circuits, partial discharges |Is| 20 [dB]
20
50Hz
550Hz
250Hz
0
0
631Hz
350Hz -20
650Hz
623Hz
731Hz
723Hz
-20
850Hz 950Hz
-40
-40
-60
-60
-80
-80
-100
-100 -120
|Is| [dB]
-120 0
100 200 300 400 500 600 700 800 900 1000
0
100 200 300 400 500 600 700 800 900 1000
f [Hz]
f [Hz]
Healthy machine f stator
3% stator shorted turns R (1 s) f s p
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Fault detection (7/10) MCSA: what is possible to detect with and how? MCSA mechanical fault detection: eccenticities, pinions, gearboxes, pulleys, shaft torsional vibrations, load imperfections 0 -20
Healthy pinion
a)
-40
Amplitude [dB]
-60 -80 0
50
100
f [Hz]
0
150
7fr1 6fr1
-20
200
8fr1
9fr1
10fr1 b)
-40 -60 -80 0
250
50
100
150
200
Pinion surface wear 250
f [Hz]
Pinon health observation by stator current space vector
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Fault detection (8/10) MCSA: what is possible to detect with and how? MCSA mechanical fault detection: eccenticities, pinions, gearboxes, pulleys, shaft torsional vibrations, load imperfections
« Birdcaging » in cables for cranes
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Fault detection (9/10) FCSA: what is possible to detect with and how? FCSA is able to detect almost the same things compared to MCSA: electrical faults, mechanical faults There is ony one sensor combining both stator and rotor effects at the same time (no demodulation) |s| [dB] 20 1.3Hz 0
|s| [dB] 20
169Hz 172Hz
71Hz 74Hz
-40
-40
-60
-60 -80
120Hz 123Hz
-100
166Hz 170Hz
889Hz 893Hz
576Hz 580Hz
-20
266Hz 269Hz
-80
70Hz 74Hz
0
217Hz 220Hz
-20
1.8Hz
94Hz 98Hz
-100
23Hz 26Hz
22Hz 26Hz
-120
-120 0
100
200
300
400
500
600
700
800
900 1000
f [Hz]
Healthy machine
0
100
200
300
400
500
600
700
800
900
1000
f [Hz]
3% stator shorted turns 26
Fault detection (10/10) FCSA: what is possible to detect with and how? FCSA is able to detect almost the same things compared to MCSA: electrical faults, mechanical faults Stator Flux Spectrum(Healthy) 0
k=-5
PSD (dB)
-68.20dB
-6
-7
-8
-70.10dB
-72.74dB
-79.45dB
-9
5
6
-69.54dB -76.00dB
-50
7
-69.53dB
8
-70.97dB
9
-58.67dB
-50.89dB
-100 60
80
100
120
140
160 Frequency (Hz)
180
200
220
240
Healthy pinion
260
Stator Flux Spectrum(Faulty) 0
k=-5
PSD (dB)
-67.68dB
-6
-7
-8
-69.76dB
-66.12dB
-74.55dB
-9
5
6
-62.56dB -70.44dB
-50
7
-66.79dB
8
-64.44dB
9
-56.45dB
-50.42dB
-100 60
80
100
120
140
160 Frequency (Hz)
180
200
220
240
Pinion surface wear
260
Difference (dB)
dB Difference 20
0.52dB
0.34dB
6.62dB
4.90dB
6.98dB
-5
-6
-7
-8
-9
5.56dB
2.74dB
6.53dB
2.22dB
0.47dB
5
6
7
8
9
10
0
k
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Diagnosis (1/10)
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Diagnosis (2/10) A brief history and facts Coming from medecine and biology: anatomy (Morgagni 1750 & Laennec 1820), physiology (Claude Bernard 1850), microscopy (Virchow 1860), bacteriology (Pasteur 1880) Mechanical engineering has pioneered such activities before electrical engineering Diagnosis is related to data acquisition, transformation, signal processing and interpretation Need of a strong methodology Coming first from academia in the late 80s Still many opened problems 29
Diagnosis (3/10) A typical diagnostics system 3-phase voltages
3-phase currents Flux sensor
Electrical machine Signal conditioning Data sampling Signal / Estimator Feature extraction Fault classification
The « core »
Decision making 30
Diagnosis (4/10) Signal processing techniques are fundamental! Steady state or transients: stationarity is hard to be achieved Time domain, frequency domain, timefrequency/time-scale domains Data acquisition time (a problem to store data in embedded systems) Diagnosis vs control High resolution and frequency detection/classification are key issues Statistics are also important for the final decision process
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Diagnosis (5/10) Highlight on frequency domain techniques DFT and its derivative FFT are the basic tools but with large data acquisition time if high resolution is needed Frequency classification is required and usage of MUSIC is also a basic process Zooming techniques are solutions to improve frequency resolution by decreasing acquisition time (ZFFT, ZMUSIC) Does not solve stationarity of the signal if missing! Need high performance digital processor (DSP, FPGA) and data acquisition board (DAQ) 32
Diagnosis (6/10) x t
x t
Sampling Fs
Sampling Fs
x n Multiplication by exp(j2πnfshift /Fs)
x n
Shifting
xs n
Low-pass filter of bandwidth Fp / 2
Decimation
xs n
Downsampling of rate A=Fs / Fp
Downsampling of rate A=Fs / 2.Fp
xs nA
xs nA
Spectrum analysis of N points
ZFFT
Shifting
xs n
Low-pass filter of bandwidth Fp / 2 xs n
Multiplication by exp(j2πnfshift /Fs)
Preprocessing
MUSIC on N/A points
Decimation
MUSIC again
ZMUSIC 33
Diagnosis (7/10)
46.8 Hz
53.2 Hz
ZFFT: T=10s, [0Hz, 100Hz] A=50, Δf=0.1Hz
46.8 Hz
53.2 Hz
FFT: T=1s, [0Hz, 5000Hz] Δf=1Hz
47 Hz
53 Hz
ZMUSIC: m = 18, T=1s, [0Hz, 100Hz] A=50, Δf=0.1Hz
Example: early detection of RBB 34
Diagnosis (8/10) Highlight on time-frequency/time-scale domain techniques Non stationarity can be approximated by sliding windows in which the signal is stationary (lost of accuracy) Wavelet transforms are parts of a tool to solve the problem globally (replacing frequency shifting by time scaling): it is more a time-scale method Discrete wavelet transforms (DWT) and packet discrete wavelet transforms (PDWT) are the most interesting Computation is more sophisticated but time of observation can be shorter 35
Diagnosis (9/10) X[k], B=[0, Fs/2]
X[k], B=[0, Fs/2]
Low-pass filter
High-pass filter
H D11 [Fs/4, Fs/2]
A1 [0, Fs/4]
A1 [0, Fs/4]
2
D1 [Fs/4, Fs/2]
2
H
A2 [0, Fs/8]
High-pass filter
G
H
G
G
Low-pass filter
D2 [Fs/8, Fs/4]
DWT
2
G
H
A2 [0, Fs/8]
D12 [Fs/8, Fs/4]
G
D22 [Fs/4, 3Fs/8]
H
D32 [3Fs/8, Fs/2]
PDWT 36
Diagnosis (10/10) Healthy rotor
1 RBB
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Prognosis (1/9) History and objectives of the failure prognosis Based on statistics The most ancient known publication is the Book of Prognosis by Hippocrate 400 years BC Modern prognosis was invented by the French School of Medicine in the middle of the 19th century (to evaluate chances of patients to survive to any health diseases) Maintenance performed on schedule or earlier? Electrical machine available for the next task? How much is its remaining life time? Trusting the statistical information? Supposed to improve reliability 38
Prognosis (2/9) What is needed for prognosis? Electrical machine as a system with several components (sub-systems): lamination, windings, connections, insulation, bearings, shaft, couplers, load, fan and more Following any fault evolution in the different system components Predicting the state of each component at the next time step of observation Threshold to launch convenient alarm(s) at the right time when a failure will be expected
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Prognosis (3/9) Prognosis approach
RUL=RLT 40
Prognosis (4/9) Prognosis methods It is still an opened problem since no universal method is known Bayesian method:computing the prior state distribution p(θ), which is the belief that θ represents all the states (all levels of fault severity) Hidden Markov model: all states are Markovian (history is not kept, states are unknown), find hidden variables (states) from observations When a fault(s) is(are) detected, observation of fault(s) evolution(s) by using a Kalman predictor
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Prognosis (5/9) Hidden Markov model
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Prognosis (6/9) Hidden Markov model to predict fault evolution
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Prognosis (7/9) Fault evolution by using a Kalman predictor
Fault evolution trajectory
Membership functions 44
Prognosis (8/9) Example: RBB propagation
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Prognosis (9/9) How prognosis improve reliability?
Probability of failure state as given by observations
State probability 46
Fault-tolerant techniques (1/5) Introduction of fault-tolerant electrical machines Principle: more than 3 phases First paper by T. Lipo in ICEM’1980 Sharing of power on more than 3 phases When a fault is detected, the related phase is removed and the machine is operating with one phase less Allow to reconfigurate the control to « smooth » the process Need of one mandatory interface in between the electrical machine (n-phase with n>3) and the grid (3-phase) 47
Fault-tolerant techniques (2/5)
Multiphase solution (>3-phase) Power sharing Less torque oscillations Operation with open phases Easy to manufacture windings
Example 6-phase=2 times 3 (similar windings)
S
q V I
P S2
fp S
P2 Q2
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Fault-tolerant techniques (3/5) Example: 6-phase induction machine Lamination 0.55mm width and 1.03m square 6 phases, 24 poles with 144 stator slots (1 slot/pole/phase) and 106 rotor slots
Rotor diameter 831mm Airgap 1.5mm Stator diameter 1m (external)
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Fault-tolerant techniques (4/5) Example: 6-phase induction machine
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Fault-tolerant techniques (5/5) Example: 6-phase induction machine
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Conclusions and future (1/2) There is still a place for electrical machines in modern industrial systems There is a need of CM in sensible applications Sensors have to be implemented (for control and fault detection purposes) CM of power equipments is an industrial reality Fault detection is feasible at the price of sensors and DAQs 52
Conclusions and future (2/2) Fault detection is a key issue for diagnosis and for prognosis Diagnosis needs a deep knowledge in DSP methods Prognosis is based only on statisitics but it uses most of data coming from the diagnosis Fault-tolerant techniques are a way to still operate a faulted system with less power
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Thanks for your attention !
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