Multisensor Wireless System for Eccentricity and ... - IEEE Xplore

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IEEE/ASME TRANSACTIONS ON MECHATRONICS, VOL. 19, NO. 3, JUNE 2014

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors Ehsan Tarkesh Esfahani, Member, IEEE, Shaocheng Wang, and V. Sundararajan

Abstract—This paper presents a stand-alone multisensor wireless system for continuous condition monitoring of induction motors. The proposed wireless system provides a low-cost alternative to expensive condition monitoring technology available through dedicated current signature analysis or vibration monitoring equipment. The system employs multiple sensors (acoustic, vibration, and current) mounted on a common wireless platform. The faults of interest are static and dynamic air-gap eccentricity, bearing damage, and their combinations. The Hilbert–Huang transform of vibration data and power spectral density of current and acoustic signals are used as the features in a hierarchical classifier. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% of correct detection and less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with an average accuracy of 95%. Index Terms—Condition monitoring, fault diagnosis, Hilbert– Huang transform (HHT), wireless sensor networks (WSNs).

fs fr fv i , fv o fv b fL E , fH E fV E D d β n fcu r fc

NOMENCLATURE Supplied frequency. Rotational frequency. Inner/outer race defected bearing harmonics in vibration. Ball defected bearing harmonics in vibration. Low/high frequency eccentricity component in current. Eccentricity related frequencies in vibration signal. Pitch diameter of bearing. Ball diameter of bearing. Bearing contact angle. Number of bearings’ ball. Frequency component of bearing damage in current. Characteristic vibration frequency (fv i , fv o , and fv b ).

Manuscript received February 4, 2012; revised October 15, 2012 and February 13, 2013; accepted April 23, 2013. Date of publication May 17, 2013; date of current version April 11, 2014. Recommended by Technical Editor M. Iwasaki. This work was supported by the California Energy Commission under Grant 56544 A/09-20 and in part by the DEED scholarship of the American Power Public Association. E. T. Esfahani was with the Department of Mechanical Engineering, University of California Riverside, Riverside, CA 92521 USA. He is now with the Department of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY 14269 USA (e-mail: [email protected]). S. Wang and V. Sundararajan are with the Department of Mechanical Engineering, University of California Riverside, Riverside, CA 92521 USA (e-mail: [email protected]; [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMECH.2013.2260865

R P nd ν s α(t), ω(t) H{.} ˜ X, X

Number of rotor bars. Number of pole pairs. Eccentricity order. Stator time harmonic (1, 3, 5). Slip. Instantaneous amplitude/frequency of IMFs. Hilbert transform. Original feature set, normalized feature set.

I. INTRODUCTION NDUCTION motors are widely used in a variety of industrial applications and consume a significant portion of energy [1]. Environmental stress and load conditions applied to these motors can cause a malfunction or reduce the efficiency of the motors leading to repair expenses and financial loss due to unexpected downtime. Therefore, to increase the productivity of the plant and to reduce maintenance costs of these systems, reliable condition monitoring and diagnosis is often desired. The primary problems in induction motors are: 1) air-gap eccentricity; 2) rotor bar damage; 3) bearing damage; and 4) stator winding imbalance [2]. These problems are often neither sudden nor independent. Therefore, by using the progressive nature of these problems, a continuous monitoring system can track the faults as they develop and help identify the root cause of faults and prevent the consequent failures. There has been substantial amount of research into detection of these faults. Most work focuses on motor current signature analysis because the method is noninvasive, convenient, and can yield information on a variety of faults [3]. In addition, vibrations [4] and acoustic emissions [5], [6] have also been used for condition monitoring. Classical spectral analysis (such as fast Fourier transform) of stator current is commonly used for detection of single or combination of multiple faults in steady-state conditions [7]. Additionally, to consider the nonstationary behavior and transient effects in induction motors, a variety of time–frequency analysis, such as wavelet transforms [8]–[12], Hilbert–Huang transform [12]–[14] in combination with different blind source separation methods such as empirical mode decomposition (EMD) [14], [15] and independent component analysis [16], [17], are used. A complete review of invasive and noninvasive detection methods of faults in stator, rotor, bearings, and those related to eccentricity can be found in [18] and [19]. In addition, various machine learning and statistical methods such as support vector machine (SVM) [17], [20], neural networks [21], genetic algorithms [22], and fuzzy logic are developed to increase the accuracy of fault detection.

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ESFAHANI et al.: MULTISENSOR WIRELESS SYSTEM FOR ECCENTRICITY AND BEARING FAULT DETECTION IN INDUCTION MOTORS

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The state of the art in condition monitoring of induction motor uses wired sensors, usually of a single modality, to track faults, which is mostly done offline. The installation and maintenance of these sensors usually increase the motor downtime and cost more than the sensors themselves. Wireless sensor networks (WSN) are becoming a more feasible monitoring option because they are small and lightweight, and hence, can be placed in limited spaces. They can be mounted on moving parts, thus, eliminating the need for flexible connectors, slip rings, etc. Recently, WSN have been developed for motor condition monitoring [4], [23]–[26]. However, most of these works aimed to detect motor failure rather than the root of fault. For instance, Lima-Filho et al. [25] developed an embedded WSN, which determine the motor efficiency through local computation on WSN and transmit the conditioned signal to the base node. In general, the main challenge in developing WSN for condition monitoring is data loss, which is intrinsic to wireless communication systems. Despite, the new advances in wireless communication and data imputation, data delivery rate is still imperfect in harsh industrial environment. Therefore, it is essential to have a robust detection algorithm which can rely on a fewer number of information packet and is also computationally inexpensive [26]. In the latter case, the classified data are transmitted instead of whole stream of measured data. In this paper, a multisensor fusion framework is implemented on a wireless sensor node to detect multiple motor malfunction. This paper emphasizes detailed diagnosis of the causes of the fault condition due to various bearing failure and air-gap eccentricity. Detection of progress of fault is also studied by considering the simultaneous faults. The main focus of the paper is to present a computationally inexpensive method which can detect the presence and type of malfunction in induction motors. The rest of the paper is organized as follows: Section II provides background information on bearing and air-gap fault detections. Materials, methods, and experimental studies are described in Section III. Data analysis, including feature generation/selection, is described in Section IV. Section V described the classification method used in this study. Finally Section VI present and discuss the experimental results.

fv o =

(1)

fv i

(2)

II. BACKGROUND ON FAULT DETECTION This paper considers motor malfunctions due to various bearing damages and air-gap eccentricity. This section reviews the detection methods of each of these two faults. A. Bearing Fault Detection Bearing failures are the most common failures in induction motors [2]. The major causes of failures are: damages on inner or outer races of the bearing due to thermal or dynamic mechanical stresses. Misalignment and poor bearing fitting can also damage the bearing cage. The other source of bearing fault is lack of lubricant due to thermal or electrical stresses. Various types of sensors and condition-monitoring methods have been developed for monitoring the bearing conditions. The

most common method is to use vibration sensors to monitor the vibration of the bearings [20], [27]–[29]. Widodo et al. used a low-speed bearing test rig to stimulate different bearing faults [20]. They used acoustic emission and vibration signals to train a SVM. Frosini and Bassi extended the bearing fault conditions to corrosion in bearings. They used stator current and efficiency of the induction motor for fault detection purposes [27]. Zhang et al. extracted entropy related features from vibration data [28]. They used a multiscale entropy method, which in comparison to regular entropy methods, provides additional information on nonlinear dynamics of the rotating components and coupling effects between these components. Onel and Benbouzid used Park and Concordia transform to detect bearing related failures [29]. Most of these bearing fault analyses are based on the single point defects on the bearing, which occur at a relatively severe stage of bearing failure. Based on its location, a single point defect can lead to a specific vibration frequency as follows [30]:

fv b

nfr (D − d cos β) 2D nfr (D + d cos β) = 2D 2 d D cos β = fr . 1− d D

(3)

Another method of detecting bearing faults can be obtained by noting that bearing vibration typically leads to variations in the motor torque which is related to the current drawn by the motor. Thus, the current harmonics at the specific frequencies will be affected by bearing vibration. The current spectrum can also be used to detect bearing failures as follows: fcurrent = fs ± kfc .

(4)

The current spectrum includes information from other motor malfunctions such as load oscillation, broken rotor bar, and rotor eccentricity. These extraneous sources can be removed by different methods such as Wiener filter-based noise cancellation [31] and statistical approaches [32]. B. Air-gap Eccentricity Air-gap eccentricity is a condition in which there is an uneven air gap between the stator and the rotor. Air-gap eccentricity unbalances the magnetic pull and causes vibration, acoustic noise, bearing wear, and rotor deflection. Consequently, eccentricity may lead to severe damage to the stator and rotor core. Thus, it is critical to detect the air-gap eccentricity at an early stage to protect the motor system. Static eccentricity is either caused by the ovality of stator core or incorrect positioning of stator core and/ or bearing. In static eccentricity, the position of minimum air-gap is fixed [33]. High level of static eccentricity may lead to dynamic eccentricity where the center of the rotor is not at the center of rotation, and the position of the minimum air-gap rotates with the rotor. Air-gap eccentricity induces stator current harmonics at specific high and low frequencies [33].

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Fig. 2. Fig. 1.

Eccentricity (a) axis of rotation (AOR) is translated (b) tilted AOR.

(a) Damaged bearings. (b) Bearings replacements for eccentricity.

High-frequency components of interest can be described by fH E =

(1 − s) (kR ± nd ν)fs p

(5)

where k is an integer and nd = 0 for static eccentricity and nd = 1, 2, or 3 for dynamic cases [18]. Furthermore, if both static and dynamic eccentricities exist together, the case in most air-gap related failures, there will be low-frequency components near the fundamental frequency, which can be expressed by fL E = fs ± kfr .

(6)

In case of mixed eccentricity, low-frequency component can also be detected in the stator vibration signal [18] fV E = 2fs ± fr .

(7)

Different features such as instantaneous reactive power [34] have been introduced to detect the air-gap related problems in motor current signature analysis. However, load oscillation can also lead to current harmonics at frequencies described by (5) and (6), which cause a major challenge in using current signal for eccentricity detection. To eliminate the load effects from eccentricity detection, different methods such as signal injection-based method [35] and monitoring both current and voltage harmonic [36] have been proposed. Additionally, Antonino and Pons-Llinares proposed the analysis of startup current to detect eccentricity [8]. However, these methods are limited, as they cannot provide continuous protection. III. MATERIALS AND METHODS A. Experimental Conditions The faults of interest in this paper are air-gap eccentricity and bearing failure. Experiments are conducted under 18 different conditions, which are grouped as four categories: 1) Faulty bearings (with four levels: bearing with no grease, bearing with damages on inner race, outer race and cage); 2) Air-gap eccentricity condition (four levels of static air-gap eccentricity and 6 levels of dynamic air-gap eccentricity); 3) Simultaneous bearing and eccentricity faults (three levels); 4) Normal condition. Faulty bearing conditions are studied by replacing one of the motor’s bearings by a damaged one. Also, two different sets of damaged bearings [see Fig. 1(a)] are used to test the dependency of the results on the location of bearing defect.

The effects of air-gap eccentricity are studied by replacing one or both of the bearings in the motor housing by modified bushings shown in Fig. 1(b). In this figure, bearings with off-centered outer bushing cause static eccentricity. The offset causes an uneven air-gap length between the rotor and the stator as shown in Fig. 2. Different values of offsets create different air-gap lengths. Moreover, replacing the bearings can be done in two different ways: keeping the thicker part of both bushes in the same direction or in opposite directions. The rotor’s axes of rotation will be more translated and less tilted when the thicker parts of the bearings are in the same direction and vice versa for the opposite direction as shown in Fig. 2. Ten conditions of air-gap eccentricity (both static and dynamic) are chosen from the various ways to combine the offset bearings [see Fig. 1(b)] in the motor assembly. The different configurations make various air-gap eccentricities ranging from 10% to 35%. Furthermore, to detect motor malfunction’s progress, we considered the case that both faults (bearing damage and eccentricity) co-exist. In practice, severe bearing damage can cause air-gap eccentricity, therefore; detection of simultaneous faults can be a valid determination of fault progress. The experiment is block randomized with 40 trials for the normal condition, 20 trials for each faulty bearing, 20 trial for each double fault, and 10 trials of each air-gap and conditions. This means that one of the conditions is randomly selected and the experiments are conducted in ten trials. Upon completing all the ten trials, another condition is selected randomly again and the whole procedure is repeated. In each trial, the data are recorded in 2 min time spans. The first and the last 10 s of the data are deleted to eliminate transitional effects. The remaining signal is then cut to 4 s segments. This process extracts 25 segments (data points) form each trial. The recorded data can be summarized as follows: 1) normal condition (one level)—40 trials (1000 segments); 2) air-gap eccentricity (ten levels)—100 trials (2500 segments); 3) faulty bearing (four levels)—120 trials (3000 segments); 4) double faults (three levels)—60 trials (1500 segments). B. Experimental Setup The motors used in the experiment are 1.5 hp six-pole threephase induction motors rated at 230 V line voltage and 4.8 A line current. They are connected to a PWM adjustable speed drive to control the speed. The running speed of the motors with no load is 1200 r/min (20 Hz). To apply load, the induction motor drives a dc generator through a pulley mechanism. Four power


Fig. 3.

Experimental setup.

resistors are connected in series to load the dc generator. This combination applies approximately 333 W load and causes the slip of 0.0136. Finally, the experiment is conducted using two identical motors to remove effects related to the variability in the motors and also to validate the robustness of the classification algorithm. One of the motors is only used to record normal conditions while the other one is used in both normal and faulty conditions. The experimental setup is shown in Fig. 3. For data acquisition, a two-axis accelerometer, a microphone, and a Hall-effect sensor are used to record vibration in x-, zaxis (see Fig. 3), sound and current, respectively. The acoustic signal is first passed through a high-pass filter to remove dc components and is then amplified. To avoid aliasing, a low-pass filter is used before the signal goes to the ADC. All the data are recorded at a sampling rate of 256 Hz. To develop a wireless sensor network, Imote2 sensor nodes are used [37]. Imote2 is a modular platform and can be stacked with sensor boards to customize the system to a specific application. The Imote2 consists of an Intel PXA271 XScale processor combined with 32 MB of on-board Flash storage and 32 MB of SRAM. Its radio chip supports a 250 kb/s data rate with 16 channels in the 2.4 GHz band. The integrated antenna enables the sensor mote to provide a nominal range of about 30 m. Data loss is intrinsic to wireless communication systems. Therefore, a reliable protocol system is needed to deliver packets of data between sensor nodes and the base station. For this purpose, Berkeley media access control (B-MAC) [38] protocol is used. B-MAC is a reconfigurable carrier senses multiple access protocol that achieves low power processing, collision avoidance, and high channel utilization. The B-MAC contains a clear channel assessment, to determine if the channel is clear for collision avoidance, and packet back off, link layer acknowledgement, and low power listening. The sensor continuously collects and buffers data for 4 s. When the buffer is full, the stored data are transmitted from the sensor node to the receiver in the form of packets. When all the packets are sent out, the sensor node starts to collect data again.

Fig. 4.

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IMFs of vibration signal in (a) time domain and (b) frequency domain.

pecially useful for nonstationary signals [39]. Unlike FFT, it provides information in joint time and frequency domains. A. Hilbert–Huang Transform HHT adaptively tracks the evolution of the time–frequency in the original signal and provides detailed information at arbitrary time–frequency scales. HHT is computed in two steps: 1) EMD and 2) Hilbert spectral analysis. HHT uses the EMD to decompose a signal into a finite set of intrinsic mode functions (IMFs), and then uses the Hilbert transform of the IMFs to obtain instantaneous frequency and amplitude data. Using the EMD method, a time series signal x(t) is represented as a sum of n IMFs ui (t) and a residue r. Fig. 4 illustrates the IMF extracted from vibration signal and their power spectral density in descending order. IMF1 is associated with the locally highest frequency and IMF3 with the lowest frequency. Having obtained the IMFs using EMD method, Hilbert transform is applied to each IMF. Instantaneous amplitude αi (t) and frequency ωi (t) are then calculated via the following equations: (8) αi (t) = u2i (t) + H{ui (t)}2 H{ui (t)} d tan−1 ωi (t) = . (9) dt ui (t) The frequency–time distribution of the amplitude over different IMFs is designated as the Hilbert spectrum H(ω, t). Finally, the marginal spectrum is computed as follows: T H(ω, t)dt. (10) h(ω) = 0

Using the Hilbert marginal spectrum (HMS), we calculate the instantaneous power of nine different frequencies bands in the first two IMF along with the average amplitude of the first two IMF for each signal as our features. The average instantaneous amplitude can be expressed as follows:

N αi (n) /N . (11) α ¯i = n =1

IV. DATA ANALYSIS The signals from all four channels are analyzed using two methods: 1) The Hilbert–Huang ransform (HHT); and 2) fast Fourier transform (FFT). The HHT has been shown to be es-

B. Feature Extraction The relevant features that carry the fault information depend upon the combination of sensor and signal processing algorithms. To extract the fault information, the feature extraction

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TABLE I LIST OF FEATURES EXTRACTED FROM EACH SENSOR

method is based on fault characteristic frequencies of the motor (fv o , fv i , fv b ) and the low characteristic frequencies of air-gap eccentricity (fL E ). The high-frequency components related to air-gap eccentricity (∼ kHz) require sensors with high sampling rates and an undesirable increase of computational complexity. Therefore, only low-frequency components near the supplied frequency (fs = 60 Hz) are used in the feature extraction method. The bearing used in the experiment is SKF bearing of series 6206, a deep grove ball bearing containing nine balls. The contact angle is 0◦ . The ball diameter is 9.525 mm and the pitch diameter is 46 mm. Using (1)-(2), the inner/outer race fault characteristic frequencies in vibration signal are determined as 108.63 and 71.36 Hz, respectively. The ball spin frequency is 46.22 Hz in vibration (3) and 13.78 Hz in current signals (4). Also the eccentricity related frequencies of interest are determined via (6)-(7) as 20, 40, 60, 80, and 100 Hz. A frequency band centered at each of the nine frequencies of interest, with a width of 2 Hz is used to calculate the power spectral density from the FFT analysis. Power spectral density of each frequency band is then normalized with respect to the power of the whole signal to form the first set of features. HMS of the first extracted IMF is also calculated using the same frequency bands. Due to lack of high-frequency components in the second IMF, only the Hilbert marginal spectrum at 20 and 40 Hz are calculated in the second IMF. Finally the averaged instantaneous amplitudes of the first two IMFs are selected as the last set of features. Table I lists all the features that are generated from each sensor based on the fault characteristic frequencies. As Table I illustrates, there are 23 features extracted from each sensor. This forms a feature vector of size 92 for each segment of data. After extracting the features, they are normalized with ˜ and standard deviations (σX ) as respect to the grand mean (X) ˜ shown in (12), whereXand Xare original raw and normalized features ˜ = (X − X)/σ ¯ X X.

(12)

Furthermore, in order to reduce the computational complexity as well as the number of free parameters in the classifier, it is necessary to find the best subset of features that has minimum mutual information and maximum class discriminatory information. To do so, a sequential forward search (SFS) is used for feature selection. Starting from an empty feature set, SFS creates candidate feature subsets by sequentially adding each of the features not yet selected. For each candidate feature subset, tenfold cross validation is performed by repeatedly checking the performance of a Fisher’s linear classifier (LDA) in separating the data of

Fig. 5. Principal components of HHT and FFT features separating the normal condition from faulty conditions data.

normal conditions from the faulty ones. Feature selection continues until there is no significant improvement in performance of LDA. Using the SFS method, a subset of eight features (out of 92 features) is determined as the best feature set. To further reduce the dimension of the features and to minimize mutual information, principal component analysis (PCA) is used to decorrelate the extracted features. Principal component identifies the main directions in the feature space where the data are distributed. Fig. 5 illustrates the first two components of all the data with different conditions. It can be seen in Fig. 5 that just by using the first two PCAs, the normal condition can be easily separated from air-gap and faulty bearings (inner/outer scratched, cage damaged, and no lubricant conditions). Therefore, the first two PCA are used for the primary fault detection. In Fig. 5, the two datasets labeled as “Bearing 1” and “Bearing 2” are both bearing related faults that are collected with two different sets of faulty bearings. Both sets consist of bearings with inner/outer and side damage, but location of bearing defect is different. To test the robustness of features, we only used one set of faulty bearing data (Bearing 1) for feature extraction/selection purposes. After determining the PCA of the most prominent features, we map the second set (Bearing 2) into feature space. It can be seen in Fig. 5 that although the location of bearing defect is changed, new data points still fall into the same cluster. This clearly shows the robustness of feature extraction/selection for distinguishing between normal and faulty bearing. It should also be noted that in Fig. 5, the feature space is selected for separation of normal condition from faulty one. It can be seen in Fig. 5 that in case of simultaneous faults (bearing damage and eccentricity), double fault conditions overlap with bearing fault. This is mainly due to the use of low-frequency components for detecting the eccentricity. Since power of eccentricity-related low-frequency components (fL E ) is considerably smaller than amplitude of bearing characteristic frequencies, bearing faults appear as the dominant fault. Furthermore, similar feature extraction scheme is used to determine the subcategory of eccentricity and bearing fault. The feature selection scheme for faulty bearing condition is based


Fig. 6.

Separating subclasses of bearing damages.

Fig. 8.

Fig. 7.

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Classification algorithm.

LDA classifier for separating static and dynamic eccentricity.

on the LDA performance in distinguishing between lubricant problem and any other bearings faults. Using the SFS method, PSD of z-vibration data at 71.36 Hz and average power envelope of first IMF of sound and z-vibration data are the best subset for bearing’s fault (see Fig. 6). As shown in Fig. 6, instantaneous power of sound and vibration are the best features in separating the double fault conditions from single bearing faults. In eccentricity-related faults, the goal of feature selection is to maximize the distance between dynamic and static eccentricities. SFD method selects the average power envelope of first IMF of vibration and PSD of vibration at 80 Hz as the best features. Fig. 7 illustrates the distribution of the dynamic and static eccentricity in the related feature space. V. CLASSIFICATION Based on the separability properties of the data, a two-stage classification is used to detect the motor fault and its type. Fig. 8 illustrates the complete classification process. As shown in Fig. 8, the extracted features are first classified by a QDA classifier. The aim of this classifier is to project the data into a new hyper-axis such that the projected data have the maximum distance between classes and minimum distance within classes. It uses a hyper-quadratic surface to separate the normal condition class from faulty conditions. This technique has a very low computational requirement, which makes it suitable for fast fault detection and implementation on wireless nodes. Additionally, if the data are classified as bearing fault or eccentricity, a SVM with radial basis function as the kernel is used

Fig. 9. Multiclass LDA for primary fault detection (dashed line) and multiclass QDA for primary fault detection (solid contour line).

as a secondary classification to detect the fault sub-category. Since bearing faults include five categories (Inner/outer damage, side damage, lubricant and double faults) a structured SVM is used to separate the data. The structured SVM includes 5 oneversus-all classifiers and chooses the class that classifies the test datum with greatest margin. It should be noted that different feature vectors are used in primary and secondary classifiers. VI. RESULTS AND DISCUSSION To train the primary classifier, ten percent of normal conditions (recorded at two identical motors) and ten percent of single and double fault conditions are used. The remaining of the dataset is used to evaluate the performance of the classifier. A multiclass linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) are used for the primary classification. The results of these two classifiers are shown in Fig. 9. The white area in Fig. 9 is the area classified as the normal condition. Also, the black and gray areas represent the feature subspace associated with eccentricity and bearing fault respectively. The boundary of the classes is represented by white dashed lines in LDA and solid contours in QDA. It can be seen that LDA misclassified some of the outliers because there was a larger within-class variance in fault conditions than in normal condition. In fact, both bearing and eccentricity

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TABLE II CONFUSION MATRIX FOR PRIMARY CLASSIFICATION (FAULTY DETECTION)

TABLE III CONFUSION MATRIX FOR SECONDARY CLASSIFICATION (FAULT TYPE)

TABLE IV CONFUSION MATRIX FOR SECONDARY CLASSIFICATION (FAULT TYPE)

faults consist of several subclasses. Because of the differences in the nature of these subcategories, the within-class variance of fault condition is larger than normal condition. This makes the decision surface in LDA less sensitive to faulty conditions. Therefore, the rate of missing faulty conditions will increase. However; QDA solves this problem by using contour as the decision surface. The proposed approach can correctly detect almost all faulty conditions from normal conditions (>99% accuracy). It should be noted that at this stage double fault conditions (bearing and eccentricity) are classified as bearing faults. Table II summarizes the average classification rate of the primary classifier in 100 different cross evaluations. Numbers in the parenthesis are the standard deviation. Rows are the true class and columns are the classification results. It can be seen from Table II that almost all the normal conditions are correctly classified. The rate of false alarm in fault detection is less than 0.1%. By increasing the number of training data to 20%, the false alarm decays to zero. Also, it can be seen that most misclassifications are in bearing faults (around 1%), which are misclassified as eccentricity. In the primary classification, SFS identified eight features as the best subset of features for fault detections. Among them, the following components have the highest weight in the first two principal components: HMS of the vibration signal (at 46 and 71 Hz of the first IMF) and certain key frequencies of the PSD of the current and sound data (40 and 20 Hz for sound, 60 Hz for current). Upon the detection of the fault type, the second classifier determines the subcategory of the detected fault. Again 10% of the faulty conditions data are used for training and remaining are used for testing in a 10-fold cross validation. The average classification rate over 100 evaluations is presented in Table III for bearing fault and in Table IV for eccentricity. The

numbers in the parenthesis are the standard deviation. Rows are the real class and columns are the classification result. It can be seen in Table III, that almost all the lubricant problems have been classified correctly. This is not surprising as the feature set selected for this classifier is based on the separability of this kind of fault (lack of lubricant) from the other types. Fig. 6 shows that inner race and outer race scratched cases are almost in two separate clusters. This explains their high accuracy rate (95.7% and 88.4%, respectively). Furthermore, the damage on the bearing’s cage has the lowest classification rate. It is mostly confused with inner race damage. This is mostly because of the distribution of this fault (see Fig. 6), which forms two main clusters. The larger cluster is well separated from the other faults while the second one (which is smaller in size and larger in variance) is projected on the outer race condition. It can also be interpreted from Table III, that cage damage, is the only condition with which all other bearing faults are confused, probably because the procedure for changing the bearings in different experimental setups can damage the cage. Finally, the confusion matrix for the last category of motor malfunction, eccentricity, is illustrated in Table IV. Table IV shows that the LDA classifier can achieve high accuracy in static and dynamic eccentricity (∼97% and 89% respectively). Moreover, Fig. 7 illustrates the classification of feature space with only considering two vibration-based features: PSD at 80 Hz and average power of the first IMF. It can be seen that static and dynamic eccentricities have distinguishable mean values but large standard deviations, which eventually cause confusion in the classification. This is possibly because we excluded the high-frequency components from the feature space to reduce the computational demand. VII. CONCLUSION The proposed research develops an inexpensive multisensor wireless sensor system to perform real-time condition monitoring of induction motors. The use of multiple sensor modalities reduces the need for precise instrumentation and signal processing as information from several sources are extracted. The results showed that a combination of the IMFs of the HHT of the vibration signal and certain key frequencies of the FFT of the current and sound data yield the highest accuracy. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% with less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with in a high accuracy. Bearing and eccentricity fault can be detected with 99.9% accuracy. Within bearing fault, the lack of lubricant in bearing and damage on inner race and outer race of the bearing can be detected with an average accuracy of ∼95%. The accuracy of faulty bearing with side damage is relatively low (∼60%). However, as long as bearing fault is not misclassified as normal or eccentricity, the confusion between bearing with Inner/outer and side damage is not, practically speaking, an issue because, in any case of bearing damage, the bearing needs to be replaced. It should be noted the extracted features for both eccentricity and bearing failure are a function of rotational frequency. In our


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Shaocheng Wang received the M.S. degree in mechanical engineering from the University of California Riverside, Riverside, CA, USA, in 2012, where he is currently working toward the Ph.D. degree in mechanical engineering. His main research interests include fault detection and cyber security in large-scale control systems.

Ehsan T. Esfahani (M’06) received the M.S. degree in electrical engineering and the Ph.D. degree in mechanical engineering from the University of California Riverside, Riverside, CA, USA, in 2012. He is currently an Assistant Professor in the Department of Mechanical and Aerospace Engineering, University at Buffalo, The State University of New York, Buffalo, NY, USA. His main research interests include design of intelligent systems.

V. Sundararajan received the M.S. and Ph.D. degrees in mechanical engineering from the University of California Berkeley, Berkeley, CA, USA, in 1997 and 2000, respectively. He is currently an Assistant Professor in the Department of Mechanical Engineering, University of California Riverside, Riverside, CA, USA. His main research interests include intelligent design and manufacturing processes.