Abstract:
Methods, systems, and computer program products are provided for implementing condition monitoring activities. Systems include a processor in communication with a machine being monitored. The processor receives signals output by the machine via a signal conversion element associated with the machine. Systems also include a display device in communication with the processor for providing signatures of the signals received from the signal conversion element. Systems further include a means for identifying, isolating, and capturing a signature from the signatures presented on the display device. The systems also include a means for digitizing and recording the signature as an event kernel, normalizing the event kernel by performing a mean removal, and normalizing the energy to unity on results of the mean removal. Systems further include a storage device for storing normalized event kernels.

Description:
BACKGROUND OF THE INVENTION  
       [0001]     The invention relates to condition monitoring, and more particularly, to methods, systems, and computer program products for implementing condition monitoring activities for machines having well-defined operating cycles.  
         [0002]     Monitoring the health of a system such as a mechanical equipment device is integral to the ongoing success of the operations performed thereon. Most modernized equipment devices today utilize some form of automated monitoring systems. Without these monitoring systems, operational issues may go unnoticed or undetected, resulting in system failure and delays in operational and maintenance activities, all of which can be potentially costly.  
         [0003]     Types of conditions monitored by these systems include structural defects, temperature, speed, and torque, to name a few. Sensor devices may be used to monitor and measure these conditions and transducers may be utilized for converting the measurements into a graphical form that enables an evaluator to read and analyze the measurements.  
         [0004]     The type of monitoring performed on a device is clearly dependent upon the type of equipment being monitored as well as the nature of its operations. Accordingly, the type of sensors utilized for monitoring conditions will also depend upon the nature of the equipment and the operations performed thereon. For example, critical operations (e.g., life-saving processes) may require some redundancy in the monitoring activities performed on an equipment device to ensure the accuracy and reliability of the equipment&#39;s informational output.  
         [0005]     Sensors operating on regular running machines, or those which exhibit periodic cycles of equal time characteristics (e.g., a rotating machine), generally produce signatures of similar patterns due to the cyclic nature of the operations performed on the machines. These patterns can provide some qualitative information regarding the optimal performance of the machine due to the cyclical nature of the operations. It would be desirable to provide a condition monitoring system that utilizes the signature patterns associated with regular running machines to identify and remedy issues resulting from the machine operations.  
       BRIEF DESCRIPTION OF THE INVENTION  
       [0006]     Exemplary embodiments relate to methods, systems, and computer program products for implementing condition monitoring activities. Methods include receiving signals output by a machine being monitored, isolating and capturing a signature from the signals, digitizing and recording the signature as an event kernel, and normalizing the event kernel by performing a mean removal and normalizing the energy to unity on results of the mean removal.  
         [0007]     Systems for implementing condition monitoring activities include a processor in communication with a machine being monitored. The processor receives signals output by the machine via a signal conversion element associated with the machine. Systems also include a display device in communication with the processor for providing signatures of the signals received from the signal conversion element. Systems further include a means for identifying, isolating, and capturing a signature from the signatures presented on the display device. The system also includes a means for digitizing and recording the signature as an event kernel, a means for normalizing the event kernel by performing a mean removal, and a means for normalizing the energy to unity on results of the mean removal. Systems further include a storage device for storing normalized event kernels.  
         [0008]     Computer program products for implementing condition monitoring activities include instructions for performing a method. The method includes receiving signals output by a machine being monitored, isolating and capturing a signature from the signals, digitizing and recording the signature as an event kernel, and normalizing the event kernel by performing a mean removal and normalizing the energy to unity on results of the mean removal.  
         [0009]     Other systems, methods, and/or computer program products according to exemplary embodiments will be or become apparent to one with skill in the art upon review of the following drawings and detailed description. It is intended that all such additional systems, methods, and/or computer program products be included within this description, be within the scope of the present invention, and be protected by the accompanying claims. 
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0010]     Referring now to the drawings wherein like elements are numbered alike in the several FIGURES:  
         [0011]      FIG. 1  is a graphical representation of three traces of sample signature data captured by an equipment monitor in the prior art;  
         [0012]      FIG. 2  is a block diagram of a system upon which the condition monitoring activities may be implemented in exemplary embodiments;  
         [0013]      FIG. 3  is a flow diagram describing a process for conducting condition monitoring activities in exemplary embodiments;  
         [0014]      FIG. 4  is a graphical representation of sample signature data including a signature of interest upon which the condition monitoring activities may be implemented in exemplary embodiments;  
         [0015]      FIG. 5  is a graphical representation of a sample normalized event kernel of the signature of interest identified in  FIG. 3 , and is generated via the condition monitoring activities in exemplary embodiments;  
         [0016]      FIG. 6  is a graphical representation of a sample acyclic autocorrelation of the normalized event kernel depicted in  FIG. 5  in exemplary embodiments;  
         [0017]      FIG. 7  is a graphical representation of a sample cross-correlation of the normalized event kernel depicted in  FIG. 5  against the first trace shown in  FIG. 1 , in exemplary embodiments;  
         [0018]      FIG. 8  is a graphical representation of a sample cross-correlation of the normalized event kernel depicted in  FIG. 5  against the middle trace shown in  FIG. 1 , in exemplary embodiments;  
         [0019]      FIG. 9  is a graphical representation of a time trace and its corresponding signatures reflected in a power spectral density plot in alternative exemplary embodiments; and  
         [0020]      FIG. 10  is a system for performing active acoustic sensing in alternative exemplary embodiments.  
     
    
     DETAILED DESCRIPTION OF THE INVENTION  
       [0021]     The condition monitoring system performs pattern recognition for event identification (i.e., time-of-occurrence estimation and event type classification) utilizing a signature associated with a regularly running machine. The signature may be an acoustic/seismic signature. A regularly running machine refers to one that exhibits periodic cycles of equal time characteristics. For example, the regularly running machine may be a rotating machine under a constant load. The signature is digitalized and normalized utilizing a two-step process, resulting in a normalized event kernel. Computations such as autocorrelations and cross-correlations may be performed on the normalized event kernel. The signature data produced from the rotating machine may be referenced to a 360-degree cycle as shown in the prior art diagram of  FIG. 1 . The signature data depicted in  FIG. 1  represents a three monitor traces  102 ,  104 , and  106  for crosshead accelerometer data.  
         [0022]     Turning now to  FIG. 2 , a system upon which the condition monitoring activities may be implemented in exemplary embodiments will now be described.  FIG. 2  includes equipment  202  in communication with a signature identification and capture station  204  and a correlator bank  206 .  
         [0023]     Equipment  202  refers to the machine that is being monitored. Equipment  202  may be any type of regular running machine or mechanical device as described above. For purposes of illustration, equipment  202  is a turbine engine. Equipment  202  includes a rotor  214 , which further comprises a shaft (not shown). Equipment  202  also includes a signal conversion element  210  (e.g., a transducer and/or shaft encoder) that converts acoustic/seismic data output from equipment  202  into a digitized form. The shaft encoder, for example, may output digital pulses corresponding to incremental angular motion of the equipment shaft and registers the signatures produced with the shaft&#39;s angular position.  
         [0024]     Signature identification and capture station  204  includes a display device  205  for presenting visual data (traces) received by equipment  202 . Signature identification and capture station  204  may comprise a processor device executing a module (e.g., software application) that enables an operator of the signature identification and capture station  204  to identify and select portions of the monitor trace on the display device  205  to be used in the implementation of the condition monitoring activities described herein. A selected signature  212  from the trace is shown on the display device  205  of signature identification and capture station  204 .  
         [0025]     Correlator bank  206  refers to a collection of correlators or kernels, which may represent different instances of a same event, or different types of events. The correlations may be implemented utilizing a variety of techniques (e.g., convolution in the Fourier domain). Correlator bank  206  may comprise a storage device. Correlator bank  206  is in communication with a monitor  208 . Monitor  208  displays the cross-correlations of the event kernels against operational data as described further herein. Monitor  208  may include exceedance alarms, logging, and statistical capabilities. While shown in  FIG. 2  as separate physical devices, it will be understood that one or more of monitor  208 , correlator bank  206 , and signature identification and capture station  204  may comprise a single unit (e.g., a high-speed computer processor). Alternatively, these elements may be incorporated into the equipment  202  being monitored.  
         [0026]     Turning now to  FIG. 3 , a flow diagram describing a process for implementing the condition monitoring activities in exemplary embodiments will now be described. For ease of explanation, it is assumed that the acoustic/seismic data generated by the equipment  202  has been transmitted to the signature identification and capture station  204 . The process begins at step  302 , whereby an operator of the signature identification and capture station  204  who is monitoring a trace associated with equipment  202  identifies an event (i.e., signature) of interest  212  at step  304 . The operator locates events of interest in terms of angular intervals over the 360-degree machine cycle. For example, an isolated event (or signature) that occurs at approximately 60-70 degrees is shown in  FIG. 4 .  
         [0027]     At step  306 , the isolated signature (i.e., signature of interest)  212  is digitized and recorded in correlator bank  206  as an event kernel via the transducer  210  and the signature identification and capture station  204 . High-pass filtering techniques of the signal may be employed to eliminate any low frequency components contained in the original signal. Since the relevant information related to the signature of interest  212  is expected to exist in the high frequency components in the vicinity of the event, removal of low frequency components may potentially improve detection reliability. The kernel, S, is represented as the n-samples of the signature within the angular limits and may be expressed as S=(s 1 , s 2 , . . . , s n ) (1).  
         [0028]     The event kernel is normalized via the signature identification and capture station  204  utilizing a two-step process as provided below.  
             S   ←     S   -       s   _     ⁡     (     mean   ⁢           ⁢   removal     )                     S   ←     S         ∑     i   =   1     n     ⁢     s   i   2           ⁢     (     energy   ⁢           ⁢   normalized   ⁢           ⁢   to   ⁢           ⁢   unity     )               
 
         [0029]     A sample normalized event kernel  500  for the isolated signature is shown in  FIG. 5  and may be displayed on monitor  208  at step  310 . Energy normalization ensures that the normalized set of samples result in a signal with energy equal to unity. This further ensures that the correlation computations performed result in true correlation coefficients, which is typically desired in assigning semantics to the acyclic correlation plots.  
         [0030]     Optionally, computation of the acyclic autocorrelation of the normalized event kernel  500  may be performed at step  312  in order to determine whether it will have good localization capability. A sample acyclic autocorrelation of the event kernel is displayed at step  314  on monitor  208  as shown in  FIG. 6 . It will be appreciated that the peak to maximum sidelobe ratio of the acyclic autocorrelation of the event kernel as depicted in  FIG. 6  is not insignificant. This may indicate that the data representing the normalized event kernel is not nearly independent, and localization of the event may not be as sharp as it might be with more nearly independent data. The autocorrelation data may, however, indicate that the signature may be sufficient for nominal demands of angular localization.  
         [0031]     Apart from localization of the event, the correlation data also contains information about whether or not the event is present in another trace. Hence it can also be used merely for the detection of the presence or absence of an event in a given signal trace. This is important in applications where the event signature can be expected to change under unhealthy operating conditions. In such a case, the correlation plot using the stored event kernel will not produce any strong peaks and the absence of a strong correlation can be used to infer that the event signature has changed, thereby signaling the presence of potential anomalous operation. In one embodiment, a suitable threshold can be used on the correlation plot to ascertain the presence or absence of the event by determining whether or not any portion of the correlation signal is greater than the threshold as a means to infer the presence of the event signature of interest.  
         [0032]     At step  315 , the condition monitoring system computes the sliding cross-correlation of the normalized event kernel  500  against the top trace  102  of  FIG. 1  from which the event kernel was extracted. The portion of the trace within the sliding window is normalized to zero mean and energy equal to unity before performing the cross-correlation. The cross-correlation is displayed on monitor  208  at step  316  and a sample cross-correlation of the event kernel (including monitor data) is shown in  FIG. 7 . In one embodiment of the invention, the peak value of the correlation plot is used to mark the time of event occurrence within the trace or signal being examined. In another embodiment, a threshold-specific examination of the correlation signal can be used to infer whether or not the event signature of interest is present.  
         [0033]     At step  318 , the condition monitoring system evaluates the repeatability of the event kernel over the same equipment  202  within the same machine state. This may be accomplished by performing a sliding cross-correlation computation of the normalized event kernel  500  against, e.g., the middle trace  104  of  FIG. 1 . Again, the portion of the trace within the sliding window is normalized to zero mean and energy equal to unity prior to the cross-correlation. The results of the cross-correlation computation of step  318  is displayed on monitor  208  and a sample representation is shown in  FIG. 8 . Note that a useful cross-correlation peak appears but is reduced over its performance as shown in  FIG. 7 .  
         [0034]     As it is unlikely that the machine  202  providing the test data changed significantly between the two traces shown in  FIG. 1 , it is suggested that the difference in cross-correlation performance is due to noise. Thus, it may be beneficial to collect a set of the same event kernels from correlator bank  206  and create an averaged event kernel from the set at step  322 . Alternatively, or in addition, the variance of the cross-correlation may be estimated from a collected set of event kernels at step  324 . At step  326 , it is determined whether the operator of the signature identification and capture station  204  has completed the condition monitoring activities. If so, the process ends at step  328 . Otherwise, the process returns to step  304 , whereby the operator identifies another event of interest.  
         [0035]     In another embodiment of the invention, the described process is implemented using data sampled and retained at ultrasonic range. This is motivated by the fact that machine noise in the ultrasonic range is expected to be quite low. This is expected to improve the sensitivity of event detection using cross-correlation as described here.  FIG. 9  indicates a time trace  900  as well as signatures visible in a corresponding power spectral density plot  902 , which shows the amount of noise present in the signal. It is clear that the surrounding machine noise that is present in the region  904 , or lower frequency region, is significantly reduced in ultrasonic region  906 . Creating kernels using data present in region  906  is expected to improve the performance on event localization. The process may involve using a band-pass filter or high-pass to retain signal information pertaining to region  906  only and then using it for the extraction of kernels.  
         [0036]     In another embodiment, an improvement to passive ultrasonic sensing is applied by replacing it with active acoustic sensing, whereby a set of one or more transducers launch acoustic waves into the machinery under diagnosis and monitor and analyze the returned acoustic waveforms using the process described herein.  FIG. 10  illustrates an active acoustic machinery diagnostic analyzer  1000 . The analyzer  1000  comprises a display/interface  1005 , as well as a controller/processor unit  1010  that controls the actions of the analyzer. The analyzer further comprises a transmitter module  1020  that generates acoustic waveforms that are applied to cabled active acoustic transducers  10301 - 1030 M where M is at least 1. The active acoustic transducers are attached to the housing of the machinery  1040  under diagnosis  1040 . The active acoustic transducers  10301 - 1030 M radiate specially crafted excitation signals  1045  into the machinery  1040  under diagnosis. The signals  1045  may comprise audio and ultrasonic components. The signals  1045  interact with the moving parts  1060  of the machinery  1040  under diagnosis. The interactions modify the reflections of the signals  1045  to produce signals  1050 . The signals  1050  may reveal the position and condition of various moving parts by a changing attenuation profile or the movement of a part may result in a change in Doppler. For example, the signals  1050  may be a frequency translation of the signals  1045  by an interaction with a moving rod. In this case, the instant of rod reversal would be indicated by a zero frequency translation. The signals  1050  are conducted through the housing of the machinery  1040  under diagnosis and sampled by cabled passive acoustic transducers  10251 - 1025 N where N is at least 1. The sampled signals can be directly used to extract kernels and apply them for event detection in other traces.  
         [0037]     As indicated above, the condition monitoring system performs pattern recognition for event identification (i.e., time-of-occurrence estimation and event type classification) utilizing a signature associated with a regularly running machine (e.g., one that exhibits periodic cycles of equal time characteristics). The signature may be an acoustic/seismic signature. The signature is digitalized and normalized utilizing a two-step process, resulting in a normalized event kernel. Computations such as autocorrelations and cross-correlations may be performed on the normalized event kernel.  
         [0038]     As described above, the embodiments of the invention may be embodied in the form of computer implemented processes and apparatuses for practicing those processes. Embodiments of the invention may also be embodied in the form of computer program code containing instructions embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other computer readable storage medium, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. An embodiment of the present invention can also be embodied in the form of computer program code, for example, whether stored in a storage medium, loaded into and/or executed by a computer, or transmitted over some transmission medium, such as over electrical wiring or cabling, through fiber optics, or via electromagnetic radiation, wherein, when the computer program code is loaded into and executed by a computer, the computer becomes an apparatus for practicing the invention. When implemented on a general-purpose microprocessor, the computer program code segments configure the microprocessor to create specific logic circuits. The technical effect of the executable code is to perform pattern recognition for event identification such as time-of-occurrence estimation and event type classification.  
         [0039]     While the invention has been described with reference to exemplary embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed as the best mode contemplated for carrying out this invention, but that the invention will include all embodiments falling within the scope of the appended claims. Moreover, the use of the terms first, second, etc. do not denote any order or importance, but rather the terms first, second, etc. are used to distinguish one element from another.