SWEPT PATTERN PROBABILITY CALCULATION FOR SPEED AND DEFECT IDENTIFICATION

A method and system is provided for performing speed and defect identification of a component such as, for example, a bearing. The method can be implemented by a computer, such that the computer receives from one or more sensors condition monitoring data. The computer sweeps patterns along a speed range against the condition monitoring data and multiplies each pattern component of the patterns by a matching environmental spectral component. The computer, then, adds the pattern components together to produce one or more results.

BACKGROUND

A train can have many rail cars, each of which can have multiple axles and corresponding axle boxes. Each axle box can have installed therein bearings, whether from the same or different manufacturers. Overtime, bearings develop defects due to numerous causes (e.g., contamination, surface imperfections, lubrication issues, etc.), which can be detected within vibration harmonics of the bearings. The field of collecting and monitoring these vibration harmonics, along with addressing defects detected in these vibration harmonics, is referred to as condition monitoring.

Further, whether the conventional condition monitoring applications are on-line and off-line, installing, utilizing, and maintaining a shaft speed sensor in support of collecting and monitoring bearing vibration harmonics can be problematic and/or expensive. For example, conventional condition monitoring applications require knowledge of a shaft speed to within a few % of tolerance to identify vibration spectrum frequency components/symptoms associated with bearing defects within the bearing vibration harmonics. Even with knowledge of the approximate shaft speed, conventional condition monitoring applications are redundant or unreliable, and the bearing defect goes undetected by these applications.

In conventional condition monitoring applications, managing parameters which can affect shaft speed calculations such as wheel diameters for each axle-box, for example, and updating a database in a timely fashion is costly with respect to man-hours, while also being prone to errors.

SUMMARY

According to one or more embodiments, a method is provided herein. The method can be implemented by a computer, such that the computer receives from one or more sensors condition monitoring data. The computer sweeps patterns along a speed range against the condition monitoring data and multiplies each pattern component of the patterns by a matching direct or interpolated environmental spectral component. The interpolated environmental spectral component can be determined by performing a linear or polynomial analysis that matches a pattern component to a corresponding environmental spectral component. The computer, then, adds the pattern components together by one or more methods to produce one or more results.

According to one or more embodiments, the above method can be implemented as a system, apparatus, and/or a computer program product.

DETAILED DESCRIPTION

Embodiments herein relate to a swept pattern probability calculation (SPPC) for speed and defect identification within bearings. In accordance with one or more embodiments, an SPPC auto-detection algorithm can be implemented by one or more devices to auto-detect bearing defects on bearings and associated machinery without the need to know an exact shaft speed. The bearing defects on the bearings and the associated machinery can include, but are not limited to, when spalls or flakes of the bearing break off (e.g., by spalling and spallation) of a bearing raceway (inner or outer) and/or roller and/or a cage thereof, as a result of brinelling, false brinelling, corrosion, contamination, lack of lubrication, or excessive rolling pressure.

For example, because bearings of railway axle-boxes and the associated machinery can provide a vibration spectrum (e.g., bearing vibration harmonics) during use, defect components/symptoms can become present in the vibration spectrum as the bearing defects develop. The SPPC auto-detection algorithm utilizes inner and outer raceways, roller and/or cage defect specific patterns to identify the most probable defect components/symptoms present in railway axle-box bearings and the associated machinery, and identify the exact shaft speed. The exact shaft speed can be utilized to perform further diagnostic operations and/or degradation analysis. While embodiments herein are described with respect to railway axle-boxes, the embodiments herein are not limited thereto. That is, although embodiments herein relate to deal with rail condition monitoring errors such as wheel diameter management errors required to convert global position system (GPS) linear speed to shaft rotational speed, embodiments herein are suited to many condition monitoring applications across many industries where a tachometer or speed input is not fitted or available.

FIG. 1depicts a system100in accordance with one or more embodiments. The system100includes at least one railcar101including at least one axle-box103. The axle-box103includes one or more wheels104(e.g., a rail bogie wheel) attached thereto by fastening elements. Note that, while only a single axle-box is show, most railcars have two bogies hence two axles with eight wheels and eight axle-boxes attached thereto (for example, by a rail bogie wheel axle-box bearing). In general, a bearing housing of the axle-box103includes a rail bogie wheel axle-box bearing (e.g., the bearing(s)) that supports a corresponding wheel104and a bolt configuration that attaches the bearing housing to the axle-box103. For example, trains typically include two to an excess of seventy rail cars101, which means thousands of bearings can be present within a system100including a fleet of trains.

Further, a system100is generally shown in accordance with one or more embodiments. The system100can include an electronic, computer framework comprising and/or employing any number and combination of computing device and networks utilizing various communication technologies, as described herein. The system100can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others.

The system100includes at least one sensor device110of a plurality of condition monitoring sensor devices. Each sensor device110is an electronic device that can include a housing111, a battery112, at least one sensor113(e.g., transducers for vibration, temperature, etc.), a data collector115(e.g., a processor and a memory as described herein), a GPS114, data transmission electronics117(e.g., a wireless modem and/or a near field communication (NFC) transponders), and an attachment component118that affixes the sensor device110to the wheel104(e.g., one of the plurality of securing bolts thereof). The attachment component118can be any bracket, flange, or the like that attaches the sensor device110to a mechanical system to be monitored.

For example, each sensor device110can be a compact, battery-operated device (e.g., using the battery112) that measures static and dynamic data of the bearing of the wheel104(e.g., condition monitoring data) to which it is attached (e.g., specifically, being attached to least one of the fastening elements of that wheel104). Via the data transmission electronics117, each sensor device110can wirelessly transmit (as represented by double arrows119) the condition monitoring data to devices, servers, and systems, such as a computing device120.

In accordance with one or more embodiments, the memory of the data collector115and/or the data transmission electronics117of each sensor device110can store the condition monitoring and/or be associated with a unique sensor identifier. For instance, an NFC transponder can be pre-programmed with a unique sensor identifier associated with a wireless modem internal to a sensor device110and/or can be pre-programmed with details relating to that specific sensor and mounting location (e.g., whether it is mounted on or near a rail bogie wheel axle-box bearing). Further, at various pre-defined intervals and with speed gating (e.g., such as while the railcar101is moving and not parked parking in a rail yard, the sensor device110records the condition monitoring data.

The computing device120includes one or more central processing units (CPU(s)) (collectively or generically referred to as a processor121). The processor121is coupled via a system bus to a memory122and various other components. The memory122can include a read only memory (ROM) and a random access memory (RAM). The ROM is coupled to the system bus and may include a basic input/output system (BIOS), which controls certain basic functions of the computing device120. The RAM is read-write memory coupled to the system bus for use by the processor121. The memory122stores data124and software125.

The data124includes a set of values of qualitative or quantitative variables organized in various data structures to support and be used by operations of the software125. In accordance with one or more embodiments, the memory122can accumulate from the sensor devices110and/or store the data124for use by the computing device120. In this regard, for example, the data124can include the condition monitoring data (e.g., vibrations and temperatures of the bearings; bearing vibration harmonics), along with speed ranges (e.g., a range from a highest expected speed to a lowest expected speed along which a shaft of the axel box103can spin/rotate due to the bearings), speed values, root sum squared (RSS) values, bearing designations, unique sensor identifiers, pre-defined intervals for data accumulation, and one or more patterns specific to bearing defects. In one or more examples, the speed of a shaft can be defined as revolutions per minutes, as determined by GPS calculations that utilize approximated rail wheel diameters.

Note further that each of the one or more patterns can be a set of frequencies over time with respect to a specific bearing defect (e.g., as it develops). In this regard, the set of frequencies correlate to defect components/symptoms that are outside normal bearing operations. The patterns can be weighted, such that a maximum match (e.g., between a frequency and a defect component/symptom) gives a highest value with respect to the others. Each defect component/symptom in a pattern has maximum value of 1 but generally lower. Examples of the one or more patterns can include a ball pass frequency outer (BPFO) pattern that detects an outer race defect frequency, a ball pass frequency inner (BPFI) radial and axial loadings pattern that detects an inner race defect frequency, a ball spin frequency (BSF) pattern that detects a ball bearing defect frequency, and a cage fundamental train frequency (FTF) pattern that detects a cage defect frequency. Weighting of the patterns can be applied such that the BPFO pattern has 1×BPFO for every 5 harmonics, the BPFI radial and axial loadings pattern 1×BPFI for every 3 harmonics with 1×N sidebands, the BSF pattern has 1× or 2×BSF and few harmonics with FTF sidebands, and the cage FTF pattern has 1×FTF and a few harmonics.

The software125is stored as instructions for execution on the processor121. That is, the memory122is also an example of a tangible storage medium readable by the processor121, where software is stored as instructions for execution by the processor121to cause the system100to operate, such as is described herein with reference toFIGS. 2-3. Note that the software125can reside anywhere within many types of condition monitoring systems and can provide storage, trending, and alarming operations, when a defect is present the SPPC provides the shaft speed, defect type, and frequency for the corresponding systems condition indicator (CI) calculation. For example, in accordance with one or more embodiments, the software can include an SPPC auto-detection algorithm, as described herein. In general, the SPPC auto-detection algorithm can be implemented by the computing device120to auto-detect bearing defects on the bearings of the axle box103(e.g., railway axle-box bearings) without the need to know accurate shaft speed, thus saving costs (e.g., man-hours) and reducing errors in managing constantly changing wheel diameters.

Further, when the SPPC auto-detection algorithm of the software is executed, the computing device120sweeps several specific weighted patterns through a specified speed range while calculating, for each speed step and each pattern type, RSS values of any correlations. In turn, a speed is identified as the speed that provided a largest value, and a defect type is identified by the specific pattern that gave that largest value. If no defect components/symptoms are present, then software does not take note of the speed (e.g., as it is not important). If defect components/symptoms are present, these identified defect components/symptoms are associated with the specific pattern to calculate the shaft speed, frequency of a bearing defect, and type of a bearing defect.

The computing device120includes one or more input/output (I/O) adapters128coupled to the system bus. The one or more I/O adapters128may include a small computer system interface (SCSI) adapter that communicates with the system memory122and/or any other similar component. The one or more I/O adapters128may include an NFC transponder that communicates with the NFC transponders of the sensor devices110. For example, the one or more I/O adapters128can interconnect the system bus with a network130, which may be an outside network, enabling the system100to communicate with other such systems (i.e., a server140).

The system100also includes the network130and the server140. The network130includes a set of computers connected together, sharing resources. The network1280can be any type of network, including a local area network (LAN), a wide area network (WAN), or the Internet, as described herein. The server140comprises a processor142and a memory144(as described herein) and provides various functionalities to the computing device120, such as sharing and storing the data124, providing processing resources, and/or performing computations (e.g., implementing the software125).

In accordance with one or more embodiments, for example, the server140can be a cloud hosted condition monitoring system that executes by the processor142the software (e.g., the software125including the SPPC auto-detection algorithm) stored in the memory144. Further, at various pre-defined intervals (e.g., such as while the railcar101is parking in a rail yard at the end of use), the cloud hosted condition monitoring system of the server140downloads and stores the data (e.g., the data124, including the unique sensor identifiers and/or the respective condition monitoring data) from the sensor devices110. Thus, the software of the server140can use the data therein to perform similar operations to the software125of the computing device120.

Turning now toFIG. 2, a process flow200implemented by the system100is depicted in accordance with one or more embodiments. The process flow200can be implemented by any component of the system100. In general, with respect to the process flow200, the speed is unknown while bearing details are known. That is, while an exact shaft speed (e.g., rpm) is unknown, the exact bearing details (e.g., the bearing defect frequencies) are known and operating within a certain band (e.g., a narrow band width of +/−10% and/or a wide band width of +/−40%). Note the band can be centre fixed or derived by GPS process. The process flow200can be further enhanced by “zeroing” the spectral carpet noise and peaks not identifiably higher than the carpet by various methods.

In convention condition monitoring, if the speed variance of a bearing is small, a fixed central speed is configured with a large search band (e.g., +/−5% or more); however, this can lead to false detections/alarms (e.g., false positives) as there is a larger probability of selecting spectral components from other sources than the defect. This then requires a lot of man-hours for the analyst to manually analyse the spectra and other machine information as to accept or discard the alarm. In contrast, the process flow200provides the technical effect and benefit of reducing false positives by using the certain band described herein.

The process flow200begins at block210, a computer (e.g., the computing device120and/or the server140) receives/accumulates condition monitoring data from one or more sensors (e.g., the sensors devices110). In accordance with one or more embodiments, the condition monitoring data, along with other data described herein, can be transmitted (e.g., as represented by double arrows119inFIG. 1) from the sensor devices110to the computing device120. More particularly, the condition monitoring data includes vibration harmonics of bearings. The computing device120can further forward through the network130the condition monitoring data, along with other data described herein, to the server140. Thus, both the computing device120and the server140accumulate sufficient information to support execution of the process flow200. The accumulation of the condition monitoring data can occur at pre-defined intervals and, in some cases, the accumulation is performed twice a day (e.g., before the railcar101leaves a rail yard and after it returns).

At block220, the computer (e.g., the computing device120and/or the server140) sweeps one or more patterns along a speed range against the condition monitoring data. In accordance with one or more embodiments, the computing device120and/or the server140can have stored in their respective memories122and144a speed range. This speed range can be predefined from a highest expected speed to a lowest expected speed for the condition at the time of the measurement and include a plurality of speed steps. According to a non-limiting embodiment, the defect pattern is “swept” across a frequency range derived from a speed range in many iterations, where each iteration is referred to as a “speed step. Further, the computing device120and/or the server140can execute the software (e.g., the software125) to sweep/apply these patterns at each speed step of the speed range, which calculates RSS values of speed/pattern correlations for each speed step and each pattern type (e.g., by a fraction of a bin at a time of the highest frequency component). One or more bins correspond to a spectrum, such that if you have 1000 hertz spectrum with 800 lines, each bin for each line has a value of how much vibration energy is associated with a center frequency of that bin (e.g., width of 1.25 hertz).

Turning toFIG. 3, a graph300is depicted according to one or more embodiments. The graph300illustrates an example of vibration harmonics310that are being swept320by a pattern330of an SPPC auto-detection algorithm. A vibration component frequency351is identified by a pattern component352. In one or more embodiments, each pattern component352corresponds to several components defined by the number of orders and the number of sidebands, either side of each order. During the sweep, the pattern components352become coincidental to the vibration components351. As the pattern components352become coincidental to the vibration components351, the product obtained by multiplying the RSS (root sum squared) value of the pattern component weighting values by the corresponding spectral bin values they are aligned to in that sweep step reaches a maxima for that pattern. Accordingly, out of the several types of defect patterns, the one with the largest maxima value identifies the defect type most likely to be present. As shown, the graph300also illustrates other examples of vibration harmonics360and370being respectively swept by pattern components380and380of an SPPC auto-detection algorithm. In one or more non-limiting embodiments, each implemented bearing has a known or predetermined bearing defect frequency. Accordingly, an accurate speed can be identified by based, at least in part, on the known bearing defect frequencies ratios (i.e. bearing type), thereby allowing a narrow search band to identify defect frequency components (i.e., a frequency at which a defect of component occurs). In this manner, the number of false positives can be significantly reduced thereby increasing the confidence when a true positive is flagged by the software (e.g., the software125). This increases the reliability of detection (e.g., alarm) by the software and reduces the number of man-hours required. Note also that pattern weighting is such that if more than one pattern crosses a set of spectral components only one having the best match (probability) gives the highest value.

At block240, multiplying each pattern component (e.g., of the one or more patterns) by a matching environmental spectral component by the computer. In some example embodiments, multiplying each pattern component of the one or more patterns by a matching environmental spectral component is performed using interpolated matching environmental spectral components. In other example embodiments, multiplying each pattern component of the one or more patterns by a matching environmental spectral component is performed using quadratic peak interpolated matching environmental spectral peaks. At block250, adding by the computer. The adding by the computer includes adding the pattern components together. In some example embodiments, the pattern components can be added together using a root sum squared (RSS). The adding identifies one or more results at operation260. The results include, but are not limited to, a most probable defect, bearing defect frequency ratios, and an accurate shaft speed, i.e., the exact or actual shaft speed determined by the presence of the defect in the vibration signal and the known bearing defect ratios.

At dashed-block270(e.g., optional block), the computer outputs the one or more results. In this regard, a technician can readily ascertain concerns with any bearings being monitored by the computer and take remedial action (e.g., replace or repair the bearings). Note that if no defect components are present, it is not of importance that the speed is known. If other spectral components are present within the condition monitoring data (e.g., from the machine dynamics/mechanics), the other spectral components can also have a pattern associated therewith to calculate a speed in the absence of a bearing defect.

FIG. 4depicts an example algorithm400in accordance with one or more embodiments. The example algorithm400begins at blocks401,402and404, where initial conditioning monitoring data is received. The initial conditional monitoring data includes, but is not limited to, a vibration spectrum of enveloped acceleration measurement with respect to shaft rpm (as shown in block401), predetermined bearing defect specific pattern components (as shown in block402), calculated speed range, bearing defect specific calculated fundamental and sideband frequency ranges, and sweep step sizes (as shown in block404). In one or more non-limiting embodiments, when the GPS has an error range (e.g., +/−5%) and the wheel diameter has an error value (e.g., +/−5% from the stated diameter used to calculate RPM (shaft speed)), then the speed range to sweep the patterns through includes an acceptable minimum error value based, at least in part, on the GPS error range and wheel diameter error value, which in this example would be at least about +/−10%.

Then, at block410, the example algorithm400initializes variables. For example, a correlation value, fundamental frequency, and a sideband frequency are each initialized to zero. At block415, a FOR loop is entered for each vibration harmonic of a bearing. More particularly, for a fundamental range (low to high), the example algorithm400steps through fundamental step sizes to sweep patterns across the vibration harmonics. The FOR loop includes, at decision block425(as shown by the DO arrow), determining whether a number of sidebands is greater than zero. If the number of sidebands is not greater than zero, the example algorithm400proceeds to block430(e.g., following the “No” arrow).

At block430, a correlate function is called, and at decision block440, it is determined whether any of the correlation values are greater than stored values. If the correlation values are greater than stored values, the example algorithm400proceeds to block445(e.g., following the “Yes” arrow). Then, the example algorithm400goes to the next pattern at block450by returning to the block415. Upon returning to block415, the example algorithm400returns specific defect types (e.g., for any identified correlation value, fundamental frequency, and side band frequency; as shown at block451).

At block445, the correlation value and frequencies are updated. If the correlation values of the sidebands are not greater than stored values, the example algorithm400proceeds to block450(e.g., following the “No” arrow).

Returning to decision block425, if the number of sidebands are greater than zero, the example algorithm400proceeds to block460(e.g., following the “Yes” arrow). At block460, another FOR loop is entered for each vibration harmonic of a bearing. More particularly, for a sideband range (low to high), the example algorithm400steps through sideband step sizes to sweep patterns across the vibration harmonics. At block465, a correlate function is called. At decision block470, it is determined whether any of the correlation values are greater than stored values. If the correlation values are greater than stored values, the example algorithm400proceeds to block450(e.g., following the “No” arrow). If the correlation values are greater than stored values, the example algorithm400proceeds to block480(e.g., following the “Yes” arrow). At block480, the correlation value and frequencies are updated. Then, the example algorithm400proceeds to block450.