Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships

ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

Contents lists available at ScienceDirect

ISA Transactions journal homepage: www.elsevier.com/locate/isatrans

Research article

Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships Andre A. Silva a, Shalabh Gupta b,n, Ali M. Bazzi b, Arthur Ulatowski c a

Raytheon Missile Systems, Tucson, AZ, USA Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA c DRS Consolidated Controls, Bridgeport, CT, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 25 May 2016 Received in revised form 25 July 2017 Accepted 25 August 2017

Electric machines and drives have enjoyed extensive applications in the field of electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) due to their ease of scalability and wide range of operating conditions. This stems from their ability to generate the desired torque and power levels for propulsion under various external load conditions. However, as with the most electrical systems, the electric drives are prone to component failures that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures such as broken rotor bars and electrical phase failures. In this regard, this paper presents a fault diagnosis methodology for electric drives in electric ships. This methodology utilizes the two-dimensional, i.e. scale-shift, wavelet transform of the sensor data to filter optimal information-rich regions which can enhance the diagnosis accuracy as well as reduce the computational complexity of the classifier. The methodology was tested on sensor data generated from an experimentally validated simulation model of electric drives under various cruising speed conditions. The results in comparison with other existing techniques show a high correct classification rate with low false alarm and miss detection rates. & 2017 ISA. Published by Elsevier Ltd. All rights reserved.

1. Introduction The induction motor, and in general electric machine and drive systems, are the de facto standard in the industry due to their consistency of speed control, cost effectiveness, and range of applications including electric vehicles (e.g., electric ships, boats, cars, and underwater vessels) and other applications such as air handling systems, extruders, hoists and conveyors. However, as with the most electrical systems, the electric drives are prone to component faults that can degrade their performance, reduce the efficiency, and require expensive maintenance. Therefore, for safe and reliable operation of electric vehicles, there is a need for automated early diagnostics of critical failures (e.g., broken rotor bars and electrical phase failures) for early warnings that may help in system recovery and condition-based maintenance (CBM) [1–4]. This paper presents a fault diagnosis methodology for the electric drive systems in electric ships, with focus on the broken rotor bar faults and the stator winding short-circuit faults. The n

Correponding author. E-mail address: [email protected] (S. Gupta).

stator winding short-circuit faults are some of the most common types of faults that encompass 21% of the distribution of all faults in electric motor drive systems [5]. The main reason for occurrence of these faults is due to the unforeseen breakdown of insulation between components, which may occur across one or more of the phase windings in the stator or across the phase and nearby components. Typically these faults are caused by intermittent voltage overloads, winding displacements due to mechanical vibrations, excessive heating, etc. The effects of such faults are high currents and more heating which results in further growth of faults. The broken rotor bar faults have also received notable attention which are more difficult to detect. Broken rotor bars are generally caused by stresses from electromagnetic forces or overloaded operation conditions, inadequate rotor fabrication, and rotor component wear from poor operating environments or lack of maintenance [6]. Broken rotor bars affect the distribution of current to other bars, sparking, torque fluctuations leading to premature wearing of bearings and other components, etc. Most of the techniques reported in literature for motor fault diagnosis rely on current and/or voltage signal analysis. Since the effects of the above faults directly affect the current signals, the

http://dx.doi.org/10.1016/j.isatra.2017.08.013 0019-0578/& 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

2

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

motor current-signature analysis (MCSA) technique has gained wide popularity for motor fault diagnosis [7]. Another advantage of the MCSA technique is that it is non-invasive. Using the currentsignature analysis both the time-domain methods (e.g., observerbased residual computation [8] and neural networks based detection [9]) and frequency-domain methods [10] have been investigated. However, due to the periodic nature of motor rotation it was observed that the effects of faults are well reflected in the frequency spectrum of the motor current. Thus, frequency-domain based techniques were accepted in literature as more reliable means for motor fault diagnosis. As such, methods such as the Fast Fourier transform (FFT) were used to extract the most pertinent information for motor fault diagnosis [11]. However, due to averaging properties of the FFT in the time-domain, the FFT based methods proved to be insufficient for general operating conditions. Therefore, more recently wavelet transform based methods were proposed which provided both time and frequency resolution [10,12]. The wavelet transform converts the one-dimensional time series data to two-dimensional scale-shift data, and contains more information in the sense that it provides both time and frequency localization. However, despite the additional benefits of the twodimensional information present in the wavelet domain, there exists added computational complexity for machine learning for fault classification. Second, the information pertinent for the fault classification problem might be hidden in localized regions in the wavelet domain. Thus, certain regions might contain useful information that can facilitate better separation of classes while the other regions might not be so useful for class separation. As such, the use of such information that is not useful for separation of fault classes can in fact degrade the performance of any well-known classifier. Thus, there exists a gap in understanding whether the entire two-dimensional domain of the wavelet transform is necessary for motor diagnosis. In this regard, this paper presents a wavelet-based filtering method that selects the optimal information-rich regions in the wavelet-domain which provide maximal separation between fault classes. The data from these regions is then used to extract features which are compact and used in training a classifier for fault diagnosis. The advantages are enhancement of the diagnosis accuracy as well as reduction of the computational complexity. The fault diagnosis methodology is built upon the following four main processes: 1) Wavelet transformation of the motor current time-series data, 2) Filtering of optimal regions from the wavelet domain based on their available information content to separate different fault classes, 3) Feature extraction via further reduction of the filtered data using the Principal Component Analysis (PCA), and 4) Pattern classification using a diagnostic-tree classifier to diagnose different faults in the system. The methodology was validated under various cruising speed conditions on the motor current data generated from an experimentally validated model of the electric drives [13,14]. The results show a high correct classification rate with low false alarm and miss detection rates. The main contributions of the paper are as follows:


Development of a method for filtering of information-rich re



gions in the wavelet-domain for extraction of useful features for enhancement of the fault classification accuracy. Construction of a diagnostic-tree classifier for sequential fault diagnosis. Testing and validation of the methodology using an experimentally validated simulation model of the motor drive system for electric ships.

The paper is organized in six sections including the introduction. Section 2 provides the relevant background information.

Section 3 presents a brief description of the motor drive system in electric ships and provides the details of data generation for the nominal and different faulty conditions. Section 4 describes the wavelet-based fault diagnosis methodology developed in this paper. Section 5 presents the results and discussion, and finally, the paper is concluded in Section 6 with suggestions for future work.

2. Literature review Technical literature reports several approaches for motor current-signature analysis (MCSA) for the purpose of fault diagnosis. Amongst these neural networks based classification methods have been commonly used. Schoen et al. [11] implemented an Artificial Neural Network (ANN) classifier for unsupervised, online learning of induction motor failures. Tallam et al. [9] extended the application of stator winding turn-fault detection for closed-loop induction motor drives based on ANNs. Murphey et al. [15] proposed the fault diagnostic ANN for single-switch and post-short circuit faults. Martins et al. [16] proposed a Hebbian-based ANN for unsupervised, online diagnosis of the stator faults utilizing vector current information. Ghate et al. [17] proposed an optimal multilayer perceptron ANN and later explored cascaded ANN systems for induction motor fault detection [18]. Some methods based on residual computation have also been proposed. Kallesoe et al. [8] presented an observer-based estimation of interturn short circuit faults in delta-connected induction motors. Tabbache et al. [19] implemented the Extended Kalman Filter (EKF) for residual generation of the motor parameters for sensor fault detection and post fault tolerance. De Angelo et al. [7] generated vectorial residuals for stator-interturn short-circuit detection. Cheng et al. [20] proposed a fault detection and identification approach of stator-turn faults using the transfer impedance of closed-loop multiple-motor drives. Besides some machine learning methods have also been proposed [21]. Georgoulas et al. [22] applied the Principal Component Analysis (PCA) with Hidden Markov Models (HMM) for broken rotor fault diagnosis in asynchronous machines. Tran et al. [23] proposed a feature selection of current sensors based on decision trees to implement a neurofuzzy inference system. As discussed earlier, the frequency-domain methods have been accepted as reliable diagnostic tools for motor fault diagnosis. Specifically, pattern classification using wavelet analysis [24] for fault diagnosis [25,26] have gained recent attention. Mohammed et al. [27] implemented the wavelets for faults diagnosis of permanent magnet machines using a recently validated model based on Finite Element Analysis (FEA). Ordaz-Moreno et al. [28] designed a broken bar detection algorithm based on discrete wavelets for FPGA implementation. Cusido et al. [10] utilized the power spectral density techniques in wavelet decomposition for machine fault detection. Li et al. [29] applied wavelet-based kurtosis statistics for fault diagnosis in rolling bearings. Rajagopalan et al. [30] implemented the Zhao-Atlas-Marks distribution for nonstationary motor fault detection. Rosero et al. [31] utilized the Empirical Mode Decomposition (EMD) and Wigner-Ville distribution for short-circuit detection of permanent magnet machines. Sadeghian et al. [32] detected broken rotor bar faults using wavelet packets and neural networks. Konar et al. [33] utilized wavelet analysis with Support Vector Machines (SVM) for bearing fault detection. Seshadrinath et al. [34,35] proposed Dual Tree complex wavelets for interturn fault diagnosis, and more recently a classification methodology by applying the wavelet analysis for optimized Bayesian inference [12]. However, the applications of above methods for electric vehicles have been limited [36–39]. The above methods have shown the utility of using the wavelet transform for extracting features for motor fault diagnosis. But, as

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

mentioned earlier, there is still a gap in understanding whether the entire scale-shift domain of wavelets is necessary for fault classification or there are some specific regions which carry more pertinent information to separate classes. Furthermore, the wavelet transform converts the one-dimensional time series data to two-dimensional data, thus adding complexity for data analysis. Moreover, the information in the wavelet domain that is not useful for separation of fault classes can in fact degrade the performance of any well-known classifier. Thus, this paper presents a novel filtering approach that takes advantage of the benefits of two-dimensional wavelet information while reducing the computational complexity via selecting the optimal regions in the wavelet domain to extract features for improving the overall classifier performance.

3. Model description & data generation A simulation model of the electric motor drive [13,14] that was developed and experimentally validated in [40] is used for this research. The block diagram of the model is shown in Fig. 1. The system under study is a three-phase induction motor drive operating under indirect Field-Oriented Control (FOC). The motor drive includes a 2300 V inverter fed from a 3500 V dc source and connected to a 2300 V/500A, four-pole, 2250 HP induction machine. Typical motors of this rating are used for driving voluminous marine vessels, such as cargo ships, cruise ships, and other watercraft. Fig. 2 shows a typical drive schedule of an electric ship which is a trapezoidal profile consisting of the following three velocity phases: i) start-up phase with an upward ramp, ii) steady state at the cruising speed, and iii) deceleration phase with a downward ramp. The profile is scaled-down in time as compared to an actual ship driving profile for faster execution. The phase times of the driving profiles of actual ships could be significantly different especially for the cruising phase which depends on the total distance travelled. In order to study the variations of the fault signatures with respect to the motor speed, sixteen trapezoidal profiles, similar to the one shown in Fig. 2, were simulated with different cruising speeds ranging from 1562.5 RPM to 1750 RPM with increments of 12.5 RPM. For this study, the input flux is set to 0.4 V · sec and the torque is set proportional to a quadratic load.

3

Fig. 2. Driving profile for the 1750 peak RPM. Table 1 Fault classes and descriptions. Class Abbreviation

Fault Description

NOM BR PP SCG

Nominal No Fault Present Broken Rotor Bar Phase-to-Phase Short Circuit to Ground

The sensor outputs of speed and current are collected after each simulation run. Table 1 summarizes the typical faults of the electric drive system and their locations are shown in Fig. 1 with red marks. The broken rotor bar (BR) faults were simulated by increasing the resistance of the squirrel-cage rotor. This amounts to about 9- 10% loss of torque compared to the Nominal (NOM) condition. This was determined by collecting the torque values at steady state for the BR and NOM conditions. On the other hand, the short circuit (SC) faults included in the analysis are the short circuits from phase to ground (SCG) and phase to phase (PP), respectively. These faults were simulated by shorting the desired locations using switches. Furthermore, to model the effects of uncertainties, the sensor data is corrupted with Additive White Gaussian Noise (AWGN) which leads to 20 dB SNR. For each cruising speed and vehicle health condition, four random instances of noise are generated that serve as additional observations of the sensor data. Table 2 shows the specifications for sensor data generation. Thus, in total 256 data sets were generated from the combination of data collected for 4 classes (i.e., the nominal condition and the three fault

Fig. 1. Block diagram of electric drive system for an electric ship with fault locations.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

4

Table 2 Data generation and specifications. Simulation Parameters

Values

Fault Class Cruising Speed Inputs Sensor Outputs Sampling Rate Sensor Noise

NOM, BR, PP, SCG [1562.50: 12.50: 1750] RPM Speed command (RPM), Rotor flux (V · s) Speed (RPM), Current (A) 1 kHz AWGN with 20 dB SNR

trained classifier is applied to make the correct diagnosis of the system using the current sensor data with a priori unknown class. Further details are presented in the following subsections.

4.1. Wavelet analysis of the current data For a time-domain signal f (t ) in any  2() space, the signal can be expanded by the use of a family of orthonormal wavelet functions, such that

1 |s|

⎛

⎞

∫ f (t )ψ *⎝ t −s τ ⎠dt

classes), 16 different cruising speed profiles, and 4 instances of AWGN to the sensor data. Fig. 3 shows instances of the time-series data collected for the nominal and the faulty conditions at the 1575, 1625, 1675, and 1725 RPM cruising speeds. As seen in Fig. 3, the time-series data for different fault classes are overlapping, especially under the broken rotor bar and the nominal conditions; thus they are hard to separate in the time-domain. Therefore, this paper presents the wavelet-based method which showed promising results in separating the different fault classes as explained below.

[Wψ f ](s , τ ) =

4. Fault diagnosis methodology

4.2. Partitioning of the wavelet domain

This section presents the fault diagnosis methodology for classifying the signatures of the nominal and the three electric drive system faults, as described above. Fig. 4 shows the architecture of the methodology, which is divided into training and testing phases. In the training phase, the system model is simulated for different cruise speed conditions. For each cruising speed, the simulation is run to generate sensor data for the nominal and the three faulty conditions as described in the previous section. Subsequently, the wavelet transform is computed for each timeseries data of the stator current. Then the optimal regions in the wavelet domain called pockets or cells are filtered, which contain the information to maximize the separation between different classes. The filtered data is then used for feature extraction and for training a classifier for fault diagnosis. In the testing phase, the

Once the wavelet transform is computed from the current data, the two-dimensional wavelet domain is partitioned into a series of regions called cells, as described here. Let a ∈ + and b ∈ + be the number of partitions of the scale and translation axes of the wavelet domain respectively, such that m mod a = 0 and n mod b = 0. Then the total number of cells is equal to ab and each cell is of size m n ( a × b ). Now let (i, j ) denote the index of any particular cell where i ∈ 1, … , a and j ∈ 1, … , b. Then the contents or the wavelet coefficients inside this cell are given by

⎜

⎟

(1)

where ψ (t ) is the mother wavelet, s = 1, … , m and τ = 1, … , n are the scale and translation parameters respectively, and [Wψ f ](s, τ ) is the wavelet transform of the signal f (t ) [25]. Wavelet analysis is an effective tool that extracts the two-dimensional scale-shift information from time-domain signals. Thus the m × n wavelet coefficient matrix is generated from the current time-series data. The magnitude-square is computed from the wavelet data for mathematical convenience.

Wi, j = [Wψ f ](Si, Tj )

(2)

where

Fig. 3. Stator current time-series data collected for the nominal condition and for different fault classes, at different cruising speeds: a) 1575 RPM, b) 1625 RPM, c) 1675 RPM, and d) 1725 RPM.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

5

Fig. 4. Fault diagnosis methodology including the training and the testing phases.

Si = Tj =

{ {

m m + 1, …, i a a n n ( j − 1) + 1, …, j b b

} }

(i − 1)

(3)

are the subsets containing the indices of points inside the cell (i, j ) along the scale and translation axes respectively. Further, for the sake of convenience let the index of a cell be represented a single parameter θ ∈ {1, … , ab}. Then θ = (i − 1)b + j; ∀ i = 1, … , a , and j = 1, … , b. Also, let R(θ ) denote the data matrix for a cell θ such that

R(θ ) = WiT, j

(4)

4.3. Filtering of optimal cells

0

Cα = {C 0⧹Cα}, |Cα | = N − 1, which contains all classes in C 0 ex0

cluding the class Cα . Let Cβ ∈ Cα be any other class. Now we will describe the process of ranking each cell. Lets pick any cell θ ∈ {1, … , ab}. As per Eq. (4), denote RCα(θ ) and RCβ (θ ) to be the data matrices containing the wavelet coefficient data in the cell with index θ , when the data is generated for class Cα ∈ C 0 and 0

Cβ ∈Cα , respectively. Furthermore, let P (R•(θ )) be the probability distribution of the data in R•(θ ), where • is either Cα or Cβ . This distribution is obtained by dividing the range of wavelet coefficient values into eight uniformly spaced intervals forming bins and computing the number of points falling inside each bin. Then the efficacy of the cell θ ∈ {1, … , ab} to separate the class pair Cα and Cβ is measured by the total variation distance [41] between the probability distributions P (RCα(θ )) and P (RCβ (θ )) as follows

In

)

(

manner

the

(

this

)

distance

(5) dCα, Cβ (θ )

is

computed

for all cells θ ∈ {1, … , ab} and the set of distances DCα, Cβ = {dCα, Cβ (1) , dCα, Cβ (2)…dCα, Cβ (ab)} is constructed that consists of the measures of all cells to separate the class pair Cα and Cβ . Subsequently, the set DCα, Cβ is sorted in descending order as follows

dCα, Cβ (θ1) ≥ dCα, Cβ (θ 2) ≥ ··· ≥ dCα, Cβ (θ ab)

(6)

k

where θ ∈ {1, … , ab}. This also defines the rank of cells, such that rank(θ1) ≥ rank(θ 2) ≥ … ≥ rank(θ ab). Thus higher the distance a cell generates, the higher is its rank. Then the set of top r ranked cells which can maximally separate class Cα with Cβ is obtained as

ΘCα, Cβ =

Once the wavelet domain is partitioned into cells, a set of optimal cells is filtered from all cells, which contain the information to maximize the separability between all classes, thus enhancing the classifier performance for fault diagnosis. Therefore, the filtering approach utilizes a metric that measures the separability between fault classes as described here. To begin with, let C 0 = {C1, … , CN} represent the set of all classes, which is equal to { NOM , BR, SCG, PP } in this paper. Then pick any class say Cα ∈ C 0 , α ∈ {1, …N}. Now lets define a set 0

1 P RCα(θ ) − P RCβ (θ ) 2

dCα, Cβ (θ ) =

and

{ θ , …, θ } 1

r

(7)

Similarly, the above process including Eqs. (5)–(7) is repeated 0

for every other class Cβ ∈ Cα to generate the corresponding optimal cells. Now, the optimal cells that can separate class Cα with all other classes are obtained from intersection of the sets 0

{ΘCα, Cβ , ∀ Cβ ∈ Cα }, as follows:

ΘC*α = ⋂ ΘCα, Cβ 0

Cβ ∈ Cα

(8)

where the number of winning cells is denoted by |ΘC*α| = η. Here ΘC*α is the set of optimal cells for class Cα that could be used to separate it from all other classes. Now the above process is repeated to generate the set ΘC*α for all classes Cα ∈ C 0 , ∀ α = 1, … , N . However, this is done in a manner such that every time a class is separated, that class is excluded from the list. This forms a diagnostic tree classifier which separates one class at each node using its optimal cells. Before delving into details of the diagnostic tree classifier construction, a data reduction method is presented below. 4.4. Data reduction for classification Once the wavelet data from the optimal cells is extracted, the Principal Component Analysis (PCA) method is applied for data reduction and feature extraction [42–44]. For this purpose, the data from the η winning cells of each class Cα are placed into a matrix XCα , where XCα = ⎡⎣ R(θ ) , ∀ θ ∈ ΘC*α ⎤⎦.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

6

Using the Karhunen-Loève (KL) algorithm, the uncorrelated features, called Principal Components (PC's) or score vectors, are inferred from the data matrix XCα based on the variance maximization principle. These PC's capture the most information in the classes from the original data matrix, as can be seen by the formation of separable clusters in the feature space. The KL algorithm is briefly summarized as follows: (1) The covariance matrix Σ of XCα is computed and the left eigenvalues {λ i} and the corresponding eigenvectors {ei} are obtained where i = 1, … , η. (2) The eigenvalues are sorted and the q < η largest dominating eigenvalues are selected. (3) Using the q eigenvectors that correspond to the largest eigenvalues, a transformation matrix  is obtained which transforms the data set XCα into a feature vector YCα , using Eq. (9) below.

other branch of the tree starts at node 2 with entry class set as C1 = {C 0⧹ϕ1}. The above process is repeated at all nodes until all classes are separated. Now we explain how the optimal cells and class are obtained at every level of the tree for sequential classification. In general, let ℓ represent a certain level of the tree, where ℓ = 1, … , N − 1. Let C ℓ− 1 be the entering class set at level ℓ of the tree, such that it contains |C ℓ− 1| = N − ℓ + 1 classes. Further, let Cα ∈ C ℓ− 1 be any class that has not already been separated at a ℓ− 1

previous level. Let Cα

= {C ℓ− 1⧹Cα} be the set of other remaining ℓ− 1

classes such that it contains |Cα

| = N − ℓ classes. Now the optiℓ− 1

mal cells to separate class Cα ∈ C ℓ− 1 from all other classes Cβ ∈ Cα are defined in a set ΘCℓ * which is obtained using the procedure α

described in Eqs. (5)–(8). However, as described above the procedure is performed only over the available classes at level ℓ , i.e. the ℓ− 1 class set Cα . Similarly the optimal cells ΘCℓ * are obtained for α

YCα = XCα × 

(9)

The feature vector YCα could be viewed as n feature points in a q dimensional feature space. The above process is repeated for all classes Cα to obtain the feature points for all classes which are included in the feature space. Furthermore, since electric ships could possibly operate at different cruising speeds, the feature vectors YCα are obtained for different cruising speeds as described earlier. The feature space is then augmented by an additional axis of the cruising speed ω to make the approach adaptive to different cruising conditions. 4.5. Fault classification using a diagnostic tree The fault diagnosis approach is formulated as a diagnostic tree classifier which separates one class from the rest at each node as shown in Fig. 5. This approach is useful for sequential diagnosis if there are multiple classes and their optimal cells are different. Hence, each node of the diagnostic tree uses the optimal cells for the class that is separated at that node. First, the tree performs optimization to identify the best class to separate at each level. Then, a classifier is trained to isolate that class on the feature data extracted from the optimal cells for that class. The construction of the tree is explained here. At node 1 of the tree, the full class set C 0 = {C1, C2, …CN} forms the entering class set. Then the most separable class say ϕ1 ∈ C 0 is separated from others using the optimal cells for ϕ1. Then the

every class Cα ∈ C ℓ− 1 at level ℓ of the tree. Now the optimal class ϕℓ ∈ C ℓ− 1 to be separated at level ℓ of the tree is obtained as follows. First, the total separability measure of each class Cα ∈ C ℓ− 1 is computed as

ΔCℓα =

1 η

∑

∑

ℓ− 1 θ ∈ Θ ℓ * Cα

dCα, Cβ (θ )

Cβ ∈ Cα

(10)

Then, the optimal class to separate at level ℓ is

ϕ ℓ = arg max(ΔCℓα ) Cα ∈ C ℓ− 1

(11)

The exit class set of level ℓ that forms the entering class set of level ℓ + 1 is then given as

C ℓ = {C ℓ− 1⧹ϕ ℓ}

(12)

Fig. 5 shows the tree for the electric ship fault diagnosis problem and the table underneath describes the optimal class separated as well as the entering class set at each level. This tree will be further discussed in the results section. At each level of the tree, the optimal class is selected using Eq. (11) and features are extracted from the optimal cells for that class using the method described in section 4.4. Subsequently, a classifier is constructed at each level to separate the optimal class vs the rest. The k-Nearest Neighbor (k-NN) classifier is used in this paper that acts according to majority vote rule where any test point is assigned to a class which has the majority occurrence in its k-nearest neighbors [42] in the feature space. Since at each level of the diagnostic tree the classifier makes only binary decisions for separation of the optimal class with the rest, the feature data of all classes other than the optimal class are grouped together and re-labeled. It was observed that the binary tree architecture simplifies the construction of the classifier and also improves the classification accuracy. 4.6. Training and testing of the tree

Fig. 5. Diagnostic Tree.

In the training phase, the diagnostic tree is constructed and fixed. The tree construction includes the following at each level: the optimal class to be separated, the optimal cells for that class, and the corresponding classifier. In the testing phase, the decisions are made for the new test data with unknown class label. Starting with ℓ = 1, the test data is transformed into the wavelet domain. Then the optimal cells for the optimal class at level ℓ = 1, that were found in the training phase, are used to extract features. Subsequently, the classifier for this level is employed to make a binary decision between the optimal class vs the rest. If the decision happens to be that of the optimal class, then the operation stops. Otherwise it moves down to the next level of the tree and

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

the same process is repeated as above and so on until we reach the bottom of the tree thus making a final decision. The performance of the overall diagnostic tree classifier was evaluated using the K-fold Cross-Validation (CV) algorithm, where (K-1) data sets are randomly selected for training the classifier while the remaining one is used for testing, and this process is repeated K times. In this paper K ¼256, which is equal to the total number of data sets generated from different simulation runs. At each iteration, the output of the diagnostic tree is recorded into a confusion matrix, which compares the classifier decisions vs. the actual class labels of the test data.

5. Results & discussion This section presents the results for fault diagnosis of the motor drives of electric ships, obtained by applying the wavelet-based methodology presented in this paper. First, the effect of faults is observed in the wavelet transforms of the current sensor data. Figs. 6a i)-iv) show examples of the wavelet transforms of the current data over m = 25 scales generated at the 1575 RPM

7

cruising speed. The wavelet data is generated for the four classes studied in this paper, i.e., the nominal (NOM) and the three fault classes (BR, PP and SCG), respectively. After experimentation with different mother wavelets, the Meyer wavelet was chosen since it provided the best classification accuracy. As seen in Figs. 6a i)-iv), the wavelet transforms capture the changes in different classes, especially between the NOM and BR classes. However, the information is disbursed over different regions in the wavelet domain which need to be filtered for improving classification accuracy. In this respect, Figs. 6b i)-iv) show the corresponding optimal cells of size 1 × 250 that were filtered for each class using the procedure described in Section 4.3. The optimal class separated at each level is found using the diagnostic tree as explained in Sec. 4.5. From the results of the diagnostic tree, shown in Fig. 5, it is observed that the best class to separate at ℓ = 1 is ϕ1 ¼PP from C1 = {NOM , BR, SCG}, at ℓ = 2 is ϕ2 ¼SCG from C 2 = {NOM , BR}, and at ℓ = 3 is ϕ3 ¼BR from C 3 = {NOM}. When testing an unknown class data using the diagnostic tree, the wavelet transform of the testing data is first filtered using the optimal cells at ℓ = 1 to separate PP from the rest {NOM , BR, SCG}. The data of optimal cells is passed through PCA and

Fig. 6. Top row shows the wavelet transform for different classes NOM, PP, SCG, and BR at 1575 RPM cruising speed. Bottom row shows the corresponding optimal cells.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

8

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

Fig. 7. Feature space generated at: a) level 1- separates PP (color blue) vs C1 = {NOM , BR, SCG} (color black), b) level 2- separates SCG (color pink) vs C 2 = {NOM , BR} (color black), and c) level 3- separates BR (color cyan) vs C 3 = {NOM} (color black). The third axis in all plots is the cruising speed in RPM.

subsequently the k-NN classifier is applied to obtain a decision as explained in Sections IV-D and IV-E, respectively. If the decision on the unknown class data is PP , then the decision is finalized and the algorithm stops. If it is not PP , then the algorithm moves down the tree and the wavelet transform of the testing data is filtered with the optimal cells that can separate SCG from {NOM , BR} and subsequently passed through PCA and the corresponding classifier. If the decision is SCG , the tree operation terminates. If it is not SCG , then the operation further moves down the tree and the wavelet data is filtered with the optimal cells that can separate NOM from BR and a decision is obtained using the corresponding classifier. Since this is the last level of the diagnostic tree, the operation terminates at this step. Fig. 7a)-c) show the feature space generated at each level of the diagnostic tree: a) level ℓ = 1 that separates PP from C1 = {NOM , BR, SCG}, b) level ℓ = 2 that separates SCG from C 2 = {NOM , BR}, and c) level ℓ = 3 that separates BR from C 3 = {NOM}. As seen, the three dimensions of the features spaces consist of two principal components and the cruising speed. The class colors for {PP , SCG , BR, NOM} are blue, pink, cyan and green, respectively. The data that is colored represents the class that is separated at each level while all other classes are shown by the black color. The fault diagnosis methodology presented in this paper is evaluated in comparison with several different existing methods. For this purpose, we chose different feature extractor and classifier combinations for data analysis, as shown in Table 3. For evaluation of each feature extractor and classifier combination, the K-fold crossvalidation process [42] is employed as explained earlier. Each diagnosis decision is recorded into the confusion matrix. In the ideal case the prediction should match the actual, thus a confusion matrix with large tallies in the diagonal indicate an accurate classifier. The Correct Classification Rate (CCR) is computed by taking the trace of the confusion matrix and dividing by the sum of all entries. Similarly, the False Alarm Rate (FA) is computed by taking the sum of all entries in the c0 row that are not predicted as c0 and dividing it by the total sum of all entries in the row. Also, the Missed Detection Rate (MD) is computed by taking the sum of all entries that are predicted to be c0 but that belong to classes other than c0 and dividing it by the total sum of all entries for all rows corresponding to classes other than c0 . Table 3 provides the confusion matrices, CCRs, FAs, and MDs for the different feature and classifier combinations. The computational times per testing time-series data of these different techniques are summarized in Table 4. During the testing phase, a trained classifier can be used to directly produce a diagnostics decision such that it takes the time series data of the motor current as input and provides the fault class information as its output. As seen in Table 3, the first row shows the results when the

time-series data of the stator current were analyzed using PCA to extract the principal components as features of the classes which were then sent to different (k-NN, SVM and C4.5) classifiers. The second row shows the results when the time-series data of the stator current were analyzed using the Linear Discriminant Analysis method [42] for feature extraction and then sent to different (kNN, SVM and C4.5) classifiers [42]. The above two methods were the fastest; however, they did not produce overall good results in terms of CCRs, FAs, and MDs. In the second approach, first the wavelet coefficients were computed from the testing data and applied directly to PCA and LDA respectively, without filtering to generate the features. These set of results are shown in rows 3 and 4 of Table 3. Since the wavelet transform generates the two-dimensional shift − scale information from one-dimensional time-series data, it can be seen that using wavelets improved the results resulting in higher CCRs and lower FAs and MDs for all classifiers; however, the corresponding computation time increased a little. Finally, the proposed approach of optimal cell filtering was employed to filter in the information rich cells in the wavelet domain which contain maximum information to separate different classes. This filtered data was then applied to PCA and LDA and then sent to different classifiers and the results are shown in the bottom two rows of Table 3. As seen the proposed approach significantly improved the classifier performances and resulted in improved CCRs and lower FAs and MDs. Overall, it can seen from Table 3 that the results progressively improved from the time-domain based methods (rows 1 and 2 of the table) to the wavelet-domain based methods (rows 3 and 4 of the table) to the proposed wavelet-domain based filtering method (rows 5 and 6 of the table). As discussed in the introduction, the wavelet-domain based methods provided better classification performances as compared to the time-domain based methods due to the more subtle fault information present in the two-dimensional wavelet-domain. The proposed wavelet-domain based filtering method further improved the performance of the wavelet-domain based methods by extracting the information-rich regions in the wavelet domain which can maximally separate the fault classes. Thus, it is observed that while the wavelet domain can enhance the class separability, there are certain regions in the wavelet domain that contain the most useful information and which can further enhance the classifier performance. All, the above results were generated using a personal computer running Windows 7 Enterprise SP1 64-bit, Intel(R) Core(TM) i5-2400 CPU @ 3.1 GHz, and 16 GB RAM.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Table 3 Confusion matrices and performance values for various techniques.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

9

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎

10

Table 4 Computational testing times per time series observation. Different Classifiers Feature Extraction Process Time Series Data ↪ PCA Time Series Data ↪ LDA Time Series Data ↪ Wavelet Transform ↪ PCA Time Series Data ↪ Wavelet Transform ↪ LDA Time Series Data ↪ Wavelet Transform ↪ Optimal Cell Selection ↪ PCA Time Series Data ↪ Wavelet Transform ↪ Optimal Cell Selection ↪ LDA

k-NN

SVM

C4.5

0.012s

0.011s

0.006s

0.002s

0.002s

0.001s

0.255s

0.354s

0.107 s

0.2s

0.082s

0.133 s

0.145s

0.249s

0.178s

0.154s

0.053s

0.044s

6. Conclusions & future work This paper presented a method for fault diagnosis in electric drive systems with applications to electric vehicles, in particular electric ships. The proposed diagnosis method utilizes a wavelet-based filtering approach for feature extraction where optimal cells in the wavelet domain are selected which provide maximum separability between classes. In addition, a diagnostic tree was constructed to classify the wavelet-based features. The proposed approach was validated in comparison with several different feature extraction and classifier combinations. It was shown that the proposed filtering approach significantly improved the classifier performances and resulted in improved CCRs and lower FAs and MDs. The machine learning framework was trained to be robust to uncertainties while also being adaptive to the varying cruising speeds. Furthermore, all the classifier training steps can be performed off-line, thus the application of this method in the implementation phase needs a small computational time to achieve a consistently high degree of accuracy. Future work consists of the following directions:


Online implementation of the diagnostic tool on an experimental test-bed.


Extension of the proposed method to electric vehicles with






different driving schedules, (e.g., a typical driving profile in New York city) where the driving input can be broken down into stop-and-go traffic dynamics. Inclusion of environmental factors on data for fault diagnosis. Extension of the current work to include a larger set of electric drive faults. Development of a supervisory control approach [45] for resilience to motor faults in electric vehicles.

Acknowledgements The authors would like to acknowledge the support provided by Khushboo Mittal in comparison of the proposed wavelet-based filtering method with other existing techniques in literature. References [1] Schoen R, Habetler T, et al. Motor bearing damage detection using stator current monitoring. IEEE Trans Ind Appl 1995;31(December (6)):1274–9. [2] Siddique A, Yadava G, Singh B. A review of stator fault monitoring techniques

of induction motors. IEEE Trans Energy Convers 2005;20(March (1)):106–14. [3] Tallam R, Lee S, et al. A survey of methods for detection of stator-related faults in induction machines. IEEE Trans Ind Appl 2007;43(August (4)):920–33. [4] Gandhi A, Corrigan T, Parsa L. Recent advances in modeling and online detection of stator interturn faults in electrical motors. IEEE Trans Ind Electron 2011;58(May (5)):1564–75. [5] Bonnett A, Yung C. Increased efficiency versus increased reliability. IEEE Ind Appl Mag 2008;14(February (1)):29–36. [6] Bonnett A, Soukup G. Cause and analysis of stator and rotor failures in threephase squirrel-cage induction motors. IEEE Trans Ind Appl 1992;28(August (4)):921–37. [7] De Angelo C, Bossio G, et al. Online model-based stator-fault detection and identification in induction motors. IEEE Trans Ind Electron 2009;56(November (11)):4671–80. [8] Kallesoe C, Izadi-Zamanabadi R, et al. Observer-based estimation of statorwinding faults in delta-connected induction motors: a linear matrix inequality approach. IEEE Trans Ind Appl 2007;43(July (4)):1022–31. [9] Tallam R, Habetler T, Harley R. Stator winding turn-fault detection for closedloop induction motor drives. IEEE Trans Ind Appl 2003;39(May (3)):720–4. [10] Cusido J, Romeral L, et al. Fault detection in induction machines using power spectral density in wavelet decomposition. IEEE Trans Ind Electron 2008;55 (Februery (2)):633–43. [11] Schoen R, Lin B, et al. An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans Ind Appl 1995;31 (December (6)):1280–6. [12] Seshadrinath J, Singh B, Panigrahi B. Incipient interturn fault diagnosis in induction machines using an analytic wavelet-based optimized bayesian inference. IEEE Trans Neural Netw Learn Syst 2014;25(May (5)):990–1001. [13] Bazzi A, Dominguez-Garcia A, Krein P. Markov reliability modeling for induction motor drives under field-oriented control. IEEE Trans Power Electron 2012;27(February (2)):534–46. [14] Bazzi A, Krein P, Utilization of median filters in power electronics: Traction drive applications, In: Proceedings of the 28th annual conference on applied power electronics conference and exposition (APEC), Mar. 2013, p. 3055–60. [15] Murphey Y, Masrur M, et al. Model-based fault diagnosis in electric drives using machine learning. IEEE/ASME Trans Mechatron 2006;11(June (3)):290–303. [16] Martins J, Pires V, Pires A. Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault. IEEE Trans Ind Electron 2007;54(February (1)):259–64. [17] Ghate V, Dudul S. Optimal mlp neural network classifier for fault detection of three phase induction motor. Exp Syst Appl 2010;37(April (4)):3468–81. [18] Ghate V, Dudul S. Cascade neural-network-based fault classifier for threephase induction motor. IEEE Trans Ind Electron 2011;58(May (5)):1555–63. [19] Tabbache B, Benbouzid M, et al. Dsp-based sensor fault detection and post fault-tolerant control of an induction motor-based electric vehicle. Int J Veh Technol 2012;2012(November (1)):1–7. [20] Cheng S, Zhang P, Habetler T. An impedance identification approach to sensitive detection and location of stator turn-to-turn faults in a closed-loop multiple-motor drive. IEEE Trans Ind Electron 2011;58(5):1545–54. [21] Silva A, Gupta S, Bazzi A, Fault diagnosis in electric drives using machine learning approaches, In: Proceedings of IEEE international electric machines and drives conference (IEMDC), May 2013, p. 722–726. [22] Georgoulas G, Mustafa M, et al. Principal component analysis of the start-up transient and hidden markov modeling for broken rotor bar fault diagnosis in asynchronous machines. Exp Syst Appl 2003;40(December (17)):7024–33. [23] Tran V, Yang B, et al. Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference. Exp Syst Appl 2009;36(March

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i

A.A. Silva et al. / ISA Transactions ∎ (∎∎∎∎) ∎∎∎–∎∎∎ (2)):1840–9. [24] Jin X, Gupta S, Mukherjee K, Ray A. Wavelet-based feature extraction using probabilistic finite state automata for pattern classification. Pattern Recognit 2011;44(July (11)):1343–56. [25] Mallat S. A wavelet tour of signal processing: the sparse way.Burlington, MA: Academic Press; 2008. [26] Gaeid K, Ping H, et al. Survey of wavelet fault diagnosis and tolerant of induction machines with case study. Int Rev Electr Eng 2012;27(June (3)):4437–56. [27] Mohammed O, Liu Z, et al. Internal short circuit fault diagnosis for pm machines using fe-based phase variable model and wavelets analysis. IEEE Trans Magn 2007;43(April (4)):1729–32. [28] Ordaz-Moreno A, Romero-Troncoso R, et al. Automatic online diagnosis algorithm for broken-bar detection on induction motors based on discrete wavelet transform for fpga implementation. IEEE Trans Ind Electron 2008;55 (May (5)):2193–202. [29] Li F, Meng G, et al. Wavelet transform-based higher-order statistics for fault diagnosis in rolling element bearings. J Vib Control 2008;14(November (11)):1691–709. [30] Rajagopalan S, Restrepo J, et al. Nonstationary motor fault detection using recent quadratic time-frequency representations. IEEE Trans Ind Appl 2008;44 (June (3)):735–44. [31] Rosero J, Romeral L, et al. Short-circuit detection by means of empirical mode decomposition and wigner-ville distribution for PMSM running under dynamic condition. IEEE Trans Ind Electron 2009;56(November (11)):4534–47. [32] Sadeghian A, Zhongming Y, Wu B. Online detection of broken rotor bars in induction motors by wavelet packet decomposition and artificial neural networks. IEEE Trans Instrum Meas 2009;58(February (7)):2253–63. [33] Konar P, Chattopadhyay P. Bearing fault detection of induction motor using wavelet and support vector machines (SVMS). Appl Soft Comput 2011;11 (September (6)):4203–11. [34] Seshadrinath J, Singh B, Panigrahi B. Incipient turn fault detection and condition monitoring of induction machine using analytical wavelet transform. IEEE Trans Ind Appl 2013;50(September (3)):2235–42. [35] Seshadrinath J, Singh B, Panigrahi B. Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 2013;29(April (2)):936–45. [36] Logan K. Intelligent diagnostic requirements of future all-electric ship integrated power system. IEEE Trans Ind Appl 2007;43(January (1)):139–49. [37] Jeong Y, Sul S, Schulz SE, Patel NR. Fault detection and fault-tolerant control of interior permanent-magnet motor drive system for electric vehicle. IEEE Trans Veh Technol 2005;41(1):46–51. [38] Akin B, Ozturk SB, Toliyat HA, Rayner M. Dsp-based sensorless electric motor fault-diagnosis tools for electric and hybrid electric vehicle powertrain applications. IEEE Trans Veh Technol 2009;58(6):2679–88. [39] Wang R, Wang J. Fault-tolerant control with active fault diagnosis for fourwheel independently driven electric ground vehicles. IEEE Trans Veh Technol 2011;60(9):4276–87. [40] Amrhein M, Krein PT. Dynamic simulation for analysis of hybrid electric vehicle system and subsystem interactions, including power electronics. IEEE Trans Veh Technol 2005;54(3):825–36. [41] Levin D, Peres Y, Wilmerm EL. Markov chains and mixing times.Providence, RI: American Mathematical Society; 2008. [42] Bishop C. Pattern recognition and machine learning.New York, NY: Springer; 2006. [43] Najjar N, Hare J, et al. Heat exchanger fouling diagnosis for an aircraft airconditioning system. In: Proceedings of SAE 2013 aerotech congress & exhibition, Sep. 2013, p. 3055–3060. [44] Najjar N, Gupta S, Hare J, Kandil S, Walthall R. Optimal sensor selection and fusion for heat exchanger fouling diagnosis in aerospace systems. IEEE Sens J 2016;16(12):4866–81. [45] Mittal K, Wilson JP, Bailie BP, Gupta S, Bollas GM, Luh PB. Supervisory control for resilient chiller plants under condenser fouling. IEEE Access 2017;5(August):14028–46.

11

Shalabh Gupta received the B.E. degree in mechanical engineering from IIT Roorkee, India, in 2001. He received the M.S. degree in mechanical engineering, the M.S. degree in electrical engineering, and the Ph.D. degree in mechanical engineering from the Pennsylvania State University, University Park, PA, USA, in 2004, 2005, and 2006, respectively. He is currently an Assistant Professor with the Department of Electrical and Computer Engineering, University of Connecticut. His current research interests include distributed autonomy, cyber-physical systems, robotics, network intelligence, data analytics, information fusion, and fault diagnosis in complex systems. Dr. Gupta is currently serving as the Chief Editor of Frontiers in Robotics and AI (Specialty Sensor Fusion and Machine Intelligence) and an Associate Editor of Structural Health Monitoring: An International Journal. Dr. Gupta has published around 100 peer-reviewed journal and conference papers. He is a member of the IEEE, IEEE Systems, Man and Cybernetics Society, and ASME.

Ali M. Bazzi received the B.E. and M.E. degrees in electrical engineering from the American University of Beirut, Beirut, Lebanon, in 2006 and 2007, respectively. He received the Ph.D. degree from the University of Illinois at Urbana-Champaign (UIUC), Urbana, IL, USA in 2010. He joined the Department of Electrical and Computer Engineering (ECE), University of Connecticut (UCONN), USA, in 2012 as an Assistant Professor where he established and currently directs the Advanced Power Electronics and Electric Drives Laboratory (APEDL). He was a senior power electronics electrical engineer with Delphi Electronics and Safety in 20112012, a visiting assistant professor at UIUC during spring 2011, an engineer with Bitrode Corporation in the summers of 2008 and 2009, and a research and teaching assistant at UIUC between 2007 and 2010. His research interests include power electronics design, control, optimization, fault diagnosis, and reliability modeling in motor drives, solar photovoltaics, and other applications. He is also interested in renewable energy integration in micro-grids, and real-time control and optimization of energy systems in general. Dr. Bazzi has served on the organizing and technical committees of many IEEE conferences. He received the Mavis Memorial Scholarship at UIUC in 2009, the Outstanding Teaching Award from the ECE Department at UCONN in 2014, and the Research Excellence Award at UCONN in 2015. In 2016, he became a UTC Assistant Professor for Engineering Innovation at UCONN. He has over 80 peer-reviewed and refereed technical publications. Dr. Bazzi is a member of the IEEE, IEEE Industry Applications Society, IEEE Power Electronics Society, IEEE Power and Energy Society, and IEEE Industrial Electronics Society.

Artur Ulatowski received the B.Sc. degree (with great distinction) and M.Sc. in electrical engineering from the University of Connecticut (UConn), Storrs, CT, USA, in 2014 and 2016, respectively. He is currently an electrical engineer at DRS Consolidated Controls. Before joining DRS, he was a graduate student at the Advanced Power Electronics and Electric Drives Laboratory at UConn. His research interests include robust, reliable, and efficient control over induction motor drives systems, data-driven modeling of the behavior of such systems, and real-world applications and implementations of those ideas and methodologies.

Andre A. Silva was born in Lima, Peru and raised in Hartford, Connecticut since 1997. In 2012, he received the Bachelor's of Science in Electrical Engineering from the University of Connecticut. Subsequently he joined the Laboratory of Intelligent Networks and Knowledgeperception Systems (LINKS) at the University of Connecticut for his graduate education. In 2015, he received the Master's of Science in Electrical Engineering, specializing in Machine Learning for Aerospace and Power System applications. He is currently employed with Raytheon Missile Systems in Tucson, AZ as an Electro-Optical Systems Engineer.

Please cite this article as: Silva AA, et al. Wavelet-based information filtering for fault diagnosis of electric drive systems in electric ships. ISA Transactions (2017), http://dx.doi.org/10.1016/j.isatra.2017.08.013i