An application of machine learning approach to fault detection of a synchronous machine

An application of machine learning approach
to fault detection of a synchronous machine
Jose Gregorio Ferreira
Europe IT Innovation
Adam Warzecha
Institute of Electromechanical Energy Conversion
Cracow University of Technology
Kraków, Poland
GE Healthcare
Kraków, Poland
Abstract—Accurate fault diagnosis systems should consider
both the historical performance and the assessment of the
current state of a machine. Manufacturing, installation,
operation and maintenance are part of the machine’s history and
should be taken into account. The paper focuses on experimental
procedures to develop a multi-criteria methodology to classify up
to ten machine conditions. Using machine learning for signal
processing techniques, any deviation from a normal steady state
might be categorized as an abnormal behavior and, when
demonstrated, a fault. To take advantage of machine learning
algorithms, a significant amount of data is needed. To
demonstrate the procedure the authors examined a synchronous
machine. The authors recorded currents and voltages primarily,
in stator and rotor winding, well as rotational speed and
electromechanical torque. The collected signals were filtered and
pre-processed, and to 5038 features were calculated and
transformed into a tidy dataset. The sparse Linear Discriminant
Analysis algorithm was used to extract the most important
defined features. The results are shown in 3D scatter plots; in
which each machine condition is represented. It is then possible
to visualize the ability of the model to identify the most
discriminant features. The same method can be used for the
diagnostic of other types of machine conditions.
Keywords—machine
learning;
classification; fault diagnosis
synchronous
machine;
I. INTRODUCTION
The development of a fresh approach to the multi-criteria
diagnosis of electrical machines relies on rapid growth in
computational power and equipment and more accurate and
efficient numerical algorithms. The analysis aims to compare
different features extracted from a group of signals monitoring
machine state, according to a variety of prescribed criteria.
Efficient supervision, fault detection, and diagnosis of
faults, considering causal fault-symptom relationships together
with advanced methods for fault detection were studied in [8].
Another model based publication in the area of induction
motors, considering a fast computational method to perform
on-line monitoring can be seen in [12]. The same main author
in [13] highlights examples of possibilities and limitations of
using frequency analysis and mathematical models in faulty
machines.
The research presented in this paper was funded by subsidies on science
granted by Polish Ministry of Science and Higher Education under the theme:
No. E-2/581/2016/DS.
An extended review of MCSA (Motor Current Signal
Analysis) describing different types of faults and the signal
signatures they generate and their diagnostics schemes is
presented in [10]. The papers [7, 14, 15, 16] introduce the
utility of spectral analysis of currents. In the paper [2] the
authors described the recent trends in condition monitoring and
fault diagnosis of rotating machinery and their interactions with
the process they are part of. All these publications share a set of
assumptions that hardly appears in complex and composed of
many interrelated components of real-world systems. Each
machine should be identified and classified according to the
environment in which they operate.
Accurate fault diagnosis systems should consider the
historical performance and capability for assessing the current
state of the machine. Such possibilities are offered by methods
and procedures of artificial intelligence. The paper [10]
presents an application of neural networks for induction motor
diagnosis. In [6] the spectra of stator currents of
the synchronous motor were treated as feature spaces and
tested using a genetic algorithm. Apart from specifics of the
model, on each machine appears variances related to
manufacturing, installation, operation and maintenance. These
are the causes that the real monitoring signals are randomly
noisy. The machine learning methods can recognize the state of
a machine despite the noise and detect any deviation from
normal operation, [1, 3]
II.
EXPERIMENTAL SYNCHRONOUS MACHINE
AND DATA ACQUISITION SYSTEM
The synchronous machine experimental platform designed
for diagnostic purposes was made available by the Institute of
Electromechanical Energy Conversion in Cracow University of
Technology. The 7.5 kW 400V salient pole machine with rotor
embedded starting squirrel-cage, was adapted by the
manufacturer to carry out various internal faults of different
levels of severity.
The paper presents an example of using a machine learning
procedure to recognize the stator winding inter-turn faults of
the machine operating as a motor under different loads. The
two layers’ stator winding has branches formed by a group of
4-coils in series with another group of 3-coils. These parallel
branches, two per phase, are available to create a 4-pole nearly
sinusoidal distributed magnetic field. To collect the signals
from sensors installed over the rotor, two groups of specially
designed 25 slip-rings and brushes are connected through
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the shaft [5], identified as Rotor signal collector (RSC). The
total number of sensors installed is 37. In addition to the 21
sensors connected through the RSC, the apparatus includes 16
external sensors for the measurement of supply voltages, motor
phase currents, torque, and rotational speed. The list of
collected signals is as follows: currents of all phase windings
and all parallel branches, excitation current, phase voltages,
voltage between machine neutral point and ground, voltage
between machine shaft and ground, mechanical rotational
torque, rotational speed, voltages induced in Rogowski coil
mounted on selected cage rotor bars, voltage between the
terminals of each bar per pole, noise canceling membrane
microphone, omnidirectional membrane microphone, analog
hall sensors on selected rotor poles surface. A National
Instrument NI-USB 6255 data acquisition card was used to
collect 37 signals; this card has 80 analog inputs (16-bit) at 125
MS/s single-channel.
III.
FAULT TYPES AND DATA COLLECTION
External resistances were placed parallel to each coil, and it
is used to create winding asymmetries by shunting current path.
The three different set-ups are represented in Fig. 1. Each fault
type was introduced through three levels of the resistor current:
{0.5A, 1.0A, 2.0A}.
The measurements were carried out for the motor running
at half and full nominal power of the load. All experiments
were performed using the same winding configuration, and
particular attention was given to guarantee repeatability. The
signals from the sensors were collected as time series at the
sampling frequency of 19 kHz.
To acquire a workable file size, each collected dataset
contains a 120 second recording of a given machine condition,
60 datasets in total are available; 6 per each machine condition,
equivalent to 12 minutes of data collected on different days
(intended to capture any impact of different external
disturbances). Enough information is then gathered to train, test
and validate different machine learning models.
The signals were filtered in two steps. First, the spikes were
identified as those data-points with an absolute value greater
than 1.5 standard deviations. This approach is based on the socalled three-sigma rule of thumb, but considering a more
restrictive interval of 86.64% of the available data points.
Then, the removed points were replaced by Piecewise
Cubic Hermite Interpolating Polynomial (PCHIP). Finally, a
median filter, considering only three adjacent points, is applied
to smooth the signals. The observed attenuation of the signals
is negligible. Even when the theoretical limit according to the
Nyquist-Shannon sampling theorem, for the chosen sampling
frequency, allows the consideration of up to 9.5 kHz,
the frequencies that will be evaluated while training the
machine learning models are the frequencies between the
fundamental (50Hz) and the slot harmonic (2.1 kHz). As
explained before, the filtering method implemented here can be
seen as low-pass single-pole recursive filter, with cut-off
frequency value 3 kHz.
Finally the signals are ready to extract relevant features
containing information about different machine conditions.
Three datasets domain (tidy data) identified as frequency
features, Clarke transformation features and Voltage-Current
features were being created considering domain related
features. In the extended research two more datasets domain
were being considered: statistical domain features and Park
transformation features.
IV.
MACHINE LEARNING PROCESS STEPS
In statistical signal processing it is intended to identify
those patterns that represent known boundaries, symbol sets or
other known types of signal domains. On the other hand,
machine learning is directed towards detecting or learning to
unknown patterns, without underlying assumptions and
simplifications of the original system.
The idea is to get an effective pattern representation of realworld signals using signal processing, then feed these patterns
to machine learning algorithms to learn the scenario. Data
signal processing refers to the ability to create a tidy dataset
from raw sensor data. Each signal is defined as a variable, in
numerical form, stored all together as column in a table. Each
row in the table is a sample representing a particular machine
condition.
Finally, these datasets are prepared for pre-processing in
which the information is transformed into features, as a result
of considering shorter time intervals or observations of which
each describes the condition of machine winding for two
different set points.
Fig. 1. Scheme of one phase branch of stator winding. The three types
of faults identified as A31-A32, A31-A41 and A42-A41 are shown.
Taking into account that all signals were collected under
steady state condition, no local maxima or peak values are
expected. The signals were prepared using digital filters. All
the signals (time-series) were filtered in the same way, no
selection of the signals was done in this step. All the 37 signals
were used to train the classification algorithm.
A. Defining the categorical variables.
Table I contains all the categorical variables and
the numerical coding used to define the qualitative responses.
Four categorical variables were assigned to describe different
machine conditions: set-point; as the working load in %, fault
type; described in Fig. 1 and severity; representing three
different levels of shunting current. The variable Class refers
the other three categories, as all the machine conditions
combination.
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TABLE I.
INITIAL TRAINING CLASS DEFINITION
Categorical
variable
Description
Values
Condition
This category register if the state
of the machine is healthy or faulty.
0 – Healthy
1 – Faulty
Set-point
Refers to the level of load at which
the machine is operated.
1 – sp1 50%
2– sp2 100%
Fault type
Simulated stator winding shortcircuits by an adjustable external
resistor.
Severity
The amount of shunting current in
each fault type.
Class
Aggregation
of
the
other
categorical variables to be used
during supervised training.
0 – Healthy
1 – A31-A32
2 – A31-A41
3 – A41-A42
0 – Healthy
1 – 500 mA
2 – 1000 mA
3 – 2000 mA
The combination
of all others to
define one class
different domains or transformations. In the majority of the
cases considered, the function was directly applied over the
data frame containing all the signals. The pseudo algorithm,
presented in Table II, calculates the windows size with the
overlapping factor of 10% for each feature dataset.
TABLE II. ALGORITHM OF FEATURES CALCULATION
1.1
Load filtered dataset for a given machine condition
1.2
Calculate Tt iteration sequence from the t timestamp
1.3
Tt = from [min (t) + step] to [max(t)], step = 22cycles / 50Hz
1.4
For each jj along Tt do
1.5
1.6
Subset data having t >= Tt(jj) – step AND t <= Tt(jj)
Normalize data sub-set
1.7
Calculate frequency domain features
1.8
Calculate Clarke transformation features
1.9
Calculate Park transformation features
1.10
The class that is used to train the model utilizes the format
represented in Fig. 2.
Fig.2. Formatting used for initial categorical variable Class.
B. Observation; samples window size
To select the number of samples per observation, the main
criteria applied was to maintain both resolution and low
processing cost using a second-order Goertzel algorithm to
compute multiple harmonics from 50Hz up to 2,5kHz.
The Goertzel algorithm performs a more efficient Fast Fourier
Transform calculation than N-point DFT. The frequencies of
the harmonics in the DFT procedure depend on the length of
the transform N. Therefore, the chosen length is N=7600,
which gives a minimum FFT resolution of 2.5 Hz and
comprises 20 cycles of the fundamental harmonic. Inspired by
P. Welch’s methodology, time averaging estimation of the
spectrum was performed and contrasted with different
windows functions. The selected window of 7600 samples was
overlapped by a factor of 10% (2 cycles) and multiplied by
a Hamming window, hence the effect of considering
incomplete cycles was minimized. The validity of this method
has been published in [4]. Each observation comprises a subset
of 8360 samples, based on which various features were
calculated. After these transformations, each incorporating 120
seconds of the collected signals, four datasets containing 2990
observations each were generated.
C. Features calculation, modeling of the signals.
Each calculated dataset corresponds to a specific group of
features, calculated based on various functions related to three
End
Frequency features: a periodic function can be
represented using Fourier series. In this example the first
50 harmonics are calculated. A feature is defined to
contain the information on the magnitude and phase of
each harmonic, from 50 to 2500 Hz. The phase A is taken
as the phase reference, and all harmonics are divided by
the fundamental. In total 3700 features were extracted.
• Clarke transformation features: the voltages and main
and parallel branches current are referred to the 0αβ
stationary two-axis reference frame, and considering
the power invariant transformation. Then the
magnitude and phase of the main harmonics are
calculated, as well as the relationships between
voltages and currents defining the electric power, using
vector and scalar mathematical forms including
the Clarke-dataset of 1224 features.
• Voltages and currents features or electric power
features. Using calculated vectorial magnitudes or the
harmonic content the relationships between currents
and voltages were considered, including the VoltageCurrent features dataset of 114 features.
D. Supervised learning
Supervised learning is the most commonly studied learning
task. It relies on a set of input features, target features, and a set
of training examples where the mapping between the target and
the input features is given. The task is to identify the new input
features. Enough data was collected to balance the classes over
all machine conditions. Hence, the supervised learning task is
focused on a multi-class problem used to detect different fault
conditions and severities categorized into various target
features as indicated in Table I. A common method for
describing the performance of a classification model is the
confusion matrix. Given m classes (where m ≥2), a confusion
matrix is a table of at least size m by m.
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For a classifier to have good accurracy most of the elements
TP – true positives and TN – true negatives should be
represented along the diagonal, with the rest FP – false
positives and FN – false negatives of the entries being zero or
close to zero.
Table III presents the confusion matrix after training a
sparse Discriminant Analysis used to extract features from the
Voltages - Current dataset.
TABLE III.
Predicted
Classes
0100
VOLTAGE-CURRENT DATASET CONFUSION MATRIX, SP1
Reference Classes
01 11 11 11
00 11 12 13
70
0
2
0
11
21
0
11
22
1
11
23
0
11
31
0
11
32
0
11
33
0
0
1
2
0
0
0
2
1111
0
79
3
1112
0
2
55
0
0
3
0
2
0
2
1113
1
0
0
67
0
1
2
0
0
1
1121
0
0
0
0
77
0
0
0
0
0
1122
5
1
10
1
0
57
0
3
0
0
1123
0
0
0
2
0
0
71
0
0
0
1131
0
1
3
0
0
7
0
61
0
2
1132
0
0
0
1
0
0
1
0
71
0
1133
0
1
12
1
0
0
0
0
0
62
In order to evaluate the learner’s performance, the number
of correctly classified observations expressed as a percentage
of all examined observations is calculated as the accuracy.
Then, to evaluate the ability of correct classification of the
classes the sensitivity is calculated as the true positive rate as
the fraction of observations of a class that was correctly
predicted and the precision as the fraction of correct
predictions for a certain class.
E. Features extraction, multiclass problem.
No rigid rule defining the number of necessary features
needed for each classification model type was established
Classifiers that generalize easily, e.g. linear classifiers, Naïve
Bayesian, allow a greater number of features since the
classifier itself is less expensive. Classifiers that tend to model
non-linear decisions boundaries very accurately do not
generalize well. As such algorithms such as neural networks,
decision trees, k-Nearest Neighbors classifiers, etc., are prone
to overfitting the data. It is important to highlight that
overfitting occurs both when estimating several parameters in a
lower dimensional space take place, and when in a highly
dimensional space, relatively few parameters are estimated.
Based on the above-explanation, it was decided to process
each dataset independently. In the following section, the
extraction of the most important features per each set-point and
dataset domain was explained.
In datasets containing highly correlated features, it is
desirable to remove them. Many classifiers perform better if
highly correlated features were removed; one exception is the
partial least squares. Choosing the threshold to identify highly
correlated features might be a complicated task that requires
several iterations to build a model with high accuracy and no
overfitting. This step can be avoided applying ℓ2 penalty within
model regularization; this penalty helps to mitigate the effect of
correlated predictors.
It is also desired to implement models that are easy to
interpret and the computation time, at least after the learning
process, is fast enough to detect faults before they lead to
machine breakdown without compromising the accuracy to
assess machine state. Therefore, it is of interest to find a
classification algorithm that will let us identify, per class, the
most important features from each dataset. This way it can be
used as a feature extraction method and for building a
sufficient matrix of knowledge for the development of a
reliable, fast and robust fault classification model.
F. Sparse Linear Discriminant Analysis - sLDA
The authors in [17] have developed and implemented an R
package for the sparse version of the LDA. This model is
a regularized version of the LDA with two tuning parameters:
Least Absolute Shrinkage and Selection Operator (LASSO)
using the ℓ1 penalty that eliminates unimportant predictors and
hence provide feature selection, and Elastic Net using the ℓ2
penalty that shrinks the discriminant coefficients towards zero.
For a given x data matrix and a vector of length
the outcome y, the ‘LASSO’ solves the problem:
minimize β 

y − X ⋅ β2
+λ β


1
of
(1)
and the ‘Elastic Net’ solves the problem
minimize β 

y − X ⋅ β2
+λ β
1
+γ β
2


(2)
Where λ, γ are nonnegative tuning parameters and
β the coefficients used to fit the model. The pseudo-code of the
calculation is shown in Table IV.
TABLE IV.
1.1
ALGORITHM OF FEATURES EXTRACTION
For Each features dataset
1.2
Create data partition, training 75% / testing 25%
1.3
1.5
Define control training repeat 3 times,
5-fold Cross Validation
Define the number of selected variables and tune grid
for lambda values
Declare parallel backend
1.6
Pre-processing training dataset: center and scale
1.7
Fit sparseLDA model, metric Accuracy
1.8
Extract relevant predictors and importance score,
append to previous iterations
Plot fitting results
1.4
1.9
1.10
End
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Two set points at 50% and 100% of nominal load
respectively, were considered to evaluate different conditions
of the machine and for each of the domain-dataset under
consideration. Each set-point was considered independently of
the other; this doubled the total number of the data classes. To
decrease the processing time and the number of iterations, the
range of features to be evaluated per class was fixed to the
maximum number of fifteen for each case under consideration.
The selected features can be repeated between classes.
Illustration of features extraction process by sLDA algorithm
used for sp1 Clarke-dataset is shown in Fig. 3. Before fitting
the model, the dataset was standardized.
in a 3D scatter plot containing the three most important
features per each feature dataset and set-point. Results obtained
for four conditions of the synchronous machine at load 50% of
the nominal are presented in Fig. 4.
TABLE V.
SUMMARY TABLE, SLDA FEATURE EXTRACTION.
COMPARISON BETWEEN SP1 AND SP2
Total
features
Setpoint
Extracted
features
Process
Time
λ
Features
per class
Clarke
1224
sp1
59
9.89 h
1
7
sp2
117
7.84 h
0
15
VI power
114
sp1
47
45.3 min
0
9
sp2
62
40.5 min
1
15
sp1
71
1.4 days
1
8
sp2
121
1.03 days
1
14
Totals
477
77.55 h
NA
68
freq
3700
5038
Fig.3. Learning curves as the accuracy versus number of training features
for different values of parameter λ . Optimal model was achieved with λ =1
and 7 features per class.
The chosen strategy was to split the data into training
subset, with 75% of all the data-points balanced for every class,
and the remaining 25% data to evaluate the performance of the
model and to avoid overfitting. Then, the sLDA training was
validated using the repeated k-fold Cross Validation.
V.
RESULTS
The number of total features considered in the above
subsections was 5038 features. Using the sLDA approach, it
was possible to limit the procedure down to 477 most optimal
features for the classification task of assessing the condition of
the synchronous machine and for three fault types at three
different severity levels. Comparing the differences between
sp1 and sp2 yields:
Fig.4. Scatter plot of the observation points in the space of the three features
in Clarke coordinate system for four conditions of the machine at the load
50% of nominal.
• The time to process is 1.34 times higher for sp1 than sp2.
The three most significant features arisen from “sp1 Clarkedataset” are U α - alpha component of three phase voltages of
the stator, I1α - alpha component of currents in branches no. 1
and I 2α - alpha component of currents in branches no. 2 of
three-phase coils. The cluster mark ‘0’ represents no-fault
condition, cluster ‘1’ - short circuit in coil 1, cluster ‘3’ - short
circuit in coil 2, cluster ‘2’ - short circuit in the whole branch
no. 1. In each case, the current leakage due to the introduced
short circuit equals to 0.5 A. Fig. 5 presents the analogous
scatter plot in the I β , I1α , I 2α coordinate system representing
• The number of optimal features needed per class and the
total number of extracted features are twice as bigger in
sp2 than sp1.
conditions of the machine operating at the nominal load. It
should be noticed that the three arisen features are sufficient to
distinguish between healthy and faulty conditions at 100%.
The sLDA was set to select up to 15 features per class with
regularization to enhance the feature selection between
variables that might be correlated. The summary can be found
in Table V. The algorithm’s ability to classify up to 10 machine
conditions is presented in a graphical form. The distribution of
the observations for a given machine condition is represented
To classify severity levels and localization in a coil of one
branch, it is necessary to include the information contained in
the other selected features during the sLDA modeling. Using
the function varImp() from the caret package in R [18], it was
possible to evaluate the performance between classes by ROC
curve analysis for each predictor.
• The accuracy in unequivocal classification of all classes
is greater in sp1.
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[2]
[3]
[4]
[5]
[6]
Fig.5. Scatter plot of observation points in 3D space of the three most
important variables for the Clarke features dataset. Each cluster represents one
from ten conditions of the machine at the load equals 100% of nominal
[7]
The analysis is carried out by decomposing the problem
into pair-wise problems. The area under the curve AUC is
calculated by the trapezoidal rule for each class pair.
CONCLUSIONS
The methodology presented here proposes a reference
method for condition-monitoring based maintenance. The
procedure’s ability to distinguish between fault and healthy
machine conditions was demonstrated by means of machine
learning, using the sparse Discriminant Analysis. Using this
classification algorithm, it was possible to extract the most
important features.The method is considered to be effective for
the early winding short circuit detection. Although the time to
compute all features, train the model and identify the most
important features approaches 77 hours, the time to classify a
new observation is close to real time. For the cases published in
the paper, the time to only processing/classify a new
observation was 0.23 seconds only. The time to collect, store,
pre-process and classify was smaller than 4 seconds. Next, it is
proposed to improve the method by including other domains of
features and to develop features extraction and classification
independent from the workload of the machine. An application
of this method clearly requires the training of the algorithm on
each machine. The history of the machine is part of the
“knowledge” extracted and used to train the algorithm. The
presented example of the machine learning application can be
treated as an introduction to a multicriteria classification of
machine condition.
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
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