SYSTEM AND METHOD FOR MOTOR FAULT DETECTION USING STATOR CURRENT NOISE CANCELLATION CROSS-REFERENCE TO RELATED APPLICATION [0001] The present application is a continuation-in-part of, and claims priority to, U.S. non-provisional application serial number 12/132,056, filed June 3, 2008, and U.S. provisional application Serial number 60/932,742, filed June 4, 2007, which are both incorporated herein by reference. . GOVERNMENT LICENSE-RIGHTS [0002] The present invention was made at least in part with Government support under Contract No. DE-FC36-04GO14000, awarded by the United States Department of Energy. The Government may have certain rights in the invention. BACKGROUND OF THE INVENTION [0003] The present invention relates generally to motors and, more particularly, to a system and method for detection of incipient conditions indicative of motor faults. [0004] Three-phase induction motors consume a large percentage of generated electricity capacity. Many applications for this "workhorse" of industry are fan and pump industrial applications. For example, in a typical integrated paper mill, low voltage and medium voltage motors may comprise nearly 70% of all driven electrical loads. Due to the prevalence of these motors in industry, it is paramount that the three- phase motor be reliable. Industry reliability surveys suggest that motor failures typically fall into one of four major categories. Specifically, motor faults typically result from bearing failure, stator turn faults, rotor bar failure, or other general faults/failures. Within these four categories: bearing, stator, and rotor failure account for approximately 85% of all motor failures. [0005] It is believed that this percentage could be significantly reduced if the driven equipment were better aligned when installed, and remained aligned regardless of changes in operating conditions. However, motors are often coupled to misaligned pump loads or loads with rotational unbalance and fail prematurely due to stresses imparted upon the motor bearings. Furthermore, manually detecting such fault causing conditions is difficult at best because doing so requires the motor to be running. As such, an operator is usually required to remove the motor from operation to perform a maintenance review and diagnosis. However, removing the motor from service is undesirable in many applications because motor down-time can be extremely costly. [0006] As such, some detection devices have been designed that generate feedback regarding an operating motor. The" feedback is then reviewed by an operator to determine the operating conditions of the motor. However, most systems that monitor operating motors merely provide feedback of faults that have likely already damaged the motor. As such, though operational feedback is sent to the operator, it is usually too late for preventive action to be taken. [0007] Some systems have attempted to provide an operator with early fault warning feedback. For example, vibration monitoring has been utilized to provide some early misalignment or unbalance-based faults. However, when a mechanical resonance occurs, machine vibrations are amplified. Due to this amplification, false positives indicating severe mechanical asymmetry are possible. Furthermore, vibration-based monitoring systems typically require highly invasive and specialized monitoring systems to be deployed within the motor system. [0008] In light of the drawbacks of vibration-based monitoring, current-based monitoring techniques have been developed to provide a more inexpensive, non- intrusive technique for detecting bearing faults. There are also limitations and drawbacks to present current-based fault detection. That is, in current-based bearing fault detection, it can be challenging to extract a fault signature from the motor stator current. For different types of bearing faults, fault signatures can be in different forms. According to general fault development processes, bearing faults can be categorized as single-point defects or generalized roughness. Most current-based bearing fault detection techniques currently in use today are directed toward detecting single-point defects and rely on locating and processing the characteristic bearing fault frequencies in the stator current. Such techniques, however, may not be suitable for detecting generalized roughness faults. That is, generalized-roughness faults exhibit degraded bearing surfaces, but not necessarily distinguished defects and, therefore, characteristic fault frequencies, may not necessarily exist in the stator current. As many bearing faults initially develop as generalized-roughness bearing faults, especially at an early stage, it would be beneficial for current-based bearing fault detection techniques to be able to detect such generalized-roughness bearing faults. [0009] It would therefore, be desirable to design a current-based bearing fault detection technique that overcomes the aforementioned drawbacks. A current-based bearing fault detection technique that allows for detection of generalized-roughness bearing faults would be beneficial-, by providing early stage detection of bearing faults. BRIEF DESCRIPTION OF THE INVENTION [0010] The present invention provides a system and method for detecting impending mechanical motor faults by way of current noise cancellation. Current data is decomposed into a non-fault component (i.e., noise) and a fault component, and noise- cancellation is performed to isolate the fault component of the current and generate a fault identifier. [0011] In accordance with one aspect of the invention, a controller configured to detect indicia of incipient mechanical motor faults includes a processor programmed to receive a first set of real-time operating current data from a motor during operation, define a noise component present in the first set of real-time operating current data, and generate a fault index for the first set of real-time operating current data based on any isolated fault components. The processor is further programmed to acquire at least one ' additional set of real-time operating, current data from the motor during operation, redefine the noise component present in each of the at least one additional sets of real- time operating current data, remove the redefined noise component from each of the at least one additional sets of real-time operating current data to identify any fault components present therein, and generate a fault index for each of the at least one additional sets of real-time operating current data based on any isolated fault components. [0012] In accordance with another aspect of the invention, a non-invasive method for detecting impending faults in electric machines includes acquiring a plurality of stator current data sets from the electric machine during operation, configuring a current data filter for each of the plurality of stator current data sets, and applying each of the current data filters to its respective stator current data set in real-time to generate a noise- cancelled stator current. The method also includes determining a fault index from the noise-cancelled stator current for each of the plurality of stator current data sets, monitoring a value of the fault index for each of the plurality of stator current data sets, and generating an alert if the value of a pre-determined number of fault indices exceeds a control limit. [0013] In accordance with yet another aspect of the invention, a system for monitoring current to predict faults includes at least one non-invasive current sensor configured to acquire stator current data from an operating motor. The system also includes a processor connected to receive the stator current data from the at least one non-invasive current sensor. The processor is programmed to repeatedly receive a set of real-time operating current data from the at least one non-invasive current sensor, where the set of real-time operating data is representative of real-time motor operation. The processor is further programmed to define a non-fault component from each of the repeatedly received sets of real-time operating current data, the non-fault component being a periodic component of the real-time operating current data, and remove the non- fault component from each of the sets of real-time operating current data in real-time to isolate residual current data. The processor is also programmed to process the residual current data to identify possible bearing faults, generate a fault index for any identified bearing faults, and generate an alert if the fault index exceeds a fault index threshold. [0014] Various other features and advantages of the present invention will be made apparent from the following detailed description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS [0015] The drawings illustrate preferred embodiments presently contemplated for carrying out the invention. [0016] In the drawings: [0017] FIG. 1 is a schematic representation of a motor assembly contemplated for carrying out the invention. . [0018] FIG. 2 is a block diagram of a controller in accordance with the invention. [0019] FIG. 3 is a block diagram of a controller for configuring of a Wiener filter in accordance with an embodiment of the invention. [0020] . FIG. 4 is a block diagram of a controller for performing fault detection using current noise cancellation in accordance with an embodiment of the invention. {0021] FIG. 5 is a graphical representation of plotted fault index data relative to fault index thresholds according to a statistical process control technique in accordance with an embodiment of the invention. [0022] FIG. 6 is a block diagram of a controller in accordance with another embodiment of the invention. [0023] FIG. 7 is a flow chart illustrating a technique for fault detection using current noise cancellation in accordance with an embodiment of the invention. [0024] FIG. 8 is a flow chart illustrating a technique for fault detection using current noise cancellation in accordance with another embodiment of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT [0025] The embodiments of the invention set forth herein relate to the detection of abnormal conditions to predictively determine potential motor faults. Current signature analysis (CSA) is utilized to review raw data received from a plurality of sensors of a controller monitoring an operating motor. The system, which is preferably disposed within the controller, decomposes the sensed/monitored current into a non-fault component and a fault. component, and performs a noise-cancellation operation to isolate the fault component of the current and generate a fault identifier. An operator of the monitored motor system is then proactively alerted' of a potential fault prior to a fault occurrence. [0026] Referring now to FIG. 1, a motor assembly, such as an induction motor, is configured to drive a load. The motor assembly 10 Includes a motor 12 that receives power from a power supply 14. The motor assembly 10 also includes a controller 16 (i.e., current monitoring system) used to monitor, .as well as control, operation of the motor 10 in response to operator inputs or motor fault conditions. The motor 12 and the controller 16 typically are coupled to electronic devices such as a power controller, or starter 17, and are in series with the motor supply to control power to the motor 12. The controller 16 includes a processor 18 that, as will be described in greater detail with respect to FIG. 2, implements an algorithm to determine the presence of unwanted mechanical conditions and predictively alert an operator of a potential fault before a fault occurs. The controller 16 further includes current sensors 22. According to an exemplary embodiment of the invention, it is understood that current sensors 22 are existing sensors used to also monitor current input to the motor and generally monitor motor operation. That is, a separate set of current sensors for acquiring current data for use in the noise-cancellation system/technique of the invention (described in detail below) are not required. Thus, the acquisition of current data via current sensors 22 for use in the noise-cancellation system/technique is understood to form a "sensorless" current monitoring system/technique for predictively determining potential motor faults. As is generally known, current data may be acquired from only two of the phases of a three-phase motor as current data for the third phase may be extrapolated from the current data of the monitored two phases. While the present invention will be described with respect to a three-phase motor, the present invention is equivalently applicable to other motors. Additionally, while shown as including a pair of current sensors 22, it is also envisioned that a single current sensor could be used to acquire only one phase of current. [0027] In one embodiment of the invention, current sensors 22 acquire stator current data from an induction motor. The stator current data acquired from sensors 22 is communicated to processor 18, where the current is analyzed using CSA to detect . Incipient (i.e., pending) motor faults, such as a bearing fault. As the identification of characteristic fault frequencies is not a viable solution for detection of all types of bearing faults (e.g., generalized roughness faults), according to an embodiment of the invention, processor 18 is programmed to treat the fault detection problem as a low signal-to-hoise ratio (SNR) problem. Processor 18 is thus programmed to decompose the stator current into noise components and fault components (i.e., the bearing fault signal). The noise components are the dominant components in the stator current, and include supply fundamental frequencies and harmonics, eccentricity harmonics, slot harmonics, saturation harmonics, and other components from unknown sources, including environmental noises. Since these dominant components exist before and after the presence of a bearing fault, a large body of the information they contain is not related to the fault. In this sense, they can be treated as "noise" for the bearing fault detection problem. As the "noise" could be 104 times stronger than the bearing fault signal (i.e., tens of Amperes vs. milli-Amperes), the detection of the bearing fault signal constitutes a low SNR problem. For solving the low SNR problem, processor 18 implements a noise cancellation technique/process for detecting the bearing fault signal. The noise components in the stator current are estimated and then cancelled by their estimates in a real-time fashion, thus providing a fault indicator from the remaining components. [0028] While processor 18 is shown as being included in a stand-alone controller 16, it is also recognized that processor 18 could be included in power control/starter 17. Additionally, it is recognized that processor 18 could be included in another power control device such as a meter, relay, or drive. That is, it is understood that controller 16 could comprise an existing power control device, such as a meter, relay, starter, or motor drive, and that processor 18 could be integrated therein. [0029] Referring now to FIG. 2, a more detailed block diagram of controller 16 is shown. As stated with respect to FIG. 1, the controller 16 includes processor 18 and current sensors 22. Furthermore, the relay assembly 16 includes a notch filter 24, a low pass filter 26, and an analog to digital (A/D) converter 28. The notch filter 24, low pass filter 26, and A/D convertor 28 operate to receive raw data generated by current sensors 22 and .prepare the raw data for processing by processor 18. That is, filters 24 and 26 are used to eliminate the fundamental frequency (e.g., 60Hz in US and 50Hz in Asia) and low frequency harmonics, as these harmonic contents are not related to bearing failure. Removing such frequencies (especially the base frequency component) from the measured .current data can greatly improve the analog -to-digital conversion resolution and SNR, as the 60Hz frequency has a large magnitude in the frequency spectrum of the current signal. While controller 16 is shown as including filters 24, 26, it is also envisioned, however, that current data could be passed directly from current sensors 22 to the A/D convertor 28. [0030} As shown in FIG. 2, processor 18 functions, at least in part, as a noise cancellation system that decomposes the stator current into noise components and fault components. Processor 18 thus includes an input delay 30 and a current predictor 32, with the current predictor 32 configured to predict noise components present in the stator current. Subtracting the prediction of the noise components from repeatedly acquired real-time stator current yields fault components which are injected into the stator current by bearing failures/faults.' It is envisioned that current predictor 32 can be configured as a Wiener filter (infinite impulse response (IIR) or fixed impulse response (FIR)), a steepest descent algorithm, a least mean square (LMS) algorithm, a least recursive squares (LRS) algorithm, or other digital filter. [0031] Referring now to FIG. 3, in an exemplary embodiment of the invention, processor 34 includes therein a Wiener filter 36 that provides for noise cancellation in the stator current and isolation of a fault signal therein. To provide accurate noise cancellation in the stator current, processor 34 is programmed to configure Wiener filter 36 to accurately define (i.e., estimate) most noise components in the stator current, such that the fault signal in the stator current is not included in its output. In configuring the Wiener filter 36, processor 34 analyzes the stator current data associated with healthy bearing conditions. This stator current data associated with healthy bearing conditions can include a first set of stator current data that is acquired, for example, within a short period after the installation of a bearing or at the start of a bearing condition monitoring process, thus ensuring that no bearing fault component is included in the stator current.. The first set of stator current data thus comprises baseline current data that essentially contains pure noise data that does not include fault information. [0032] The first set of stator current data, or baseline current data, is received by processor 34 and is implemented for. configuring Wiener filter 36. More specifically, the baseline current data is used for assigning coefficients in the Wiener filter 36. Processor 34 assigns coefficients to the Wiener filter 36 such that the prediction error, e(n), of the filter is minimized in the mean-square sense. As shown in FIG. 3, the ' baseline current data is described by: where d1(n) is the noise components, d(n) is the fault signal, and v1(n) is the measurement noise. As set forth above, the baseline current data is devoid of a fault signal, and as such, Eqn. 1 reduces to x(n) = d1(n)+v1(n). [0033] In configuring the Wiener filter 36, the processor 34 assigns the coefficients of the filter by using the minimum mean-squared error (MMSE) method. In implementing/applying the MMSE method, processor 34 solves for the coefficients, w(k), k=0, 1,..., p, to minimize the mean square prediction error,ξ according to: [0036] By assuming that the signal x(n) is wide-sense stationary (WSS), then: [0037] The autocorrelation sequences in Eqn. 9 can be estimated by time averages when implementing this method. For finite data records (i.e., a finite number of stator current data points), x(n), 0 < n